From eb874226ef7004f7955b44ec08c9221cc62569bb Mon Sep 17 00:00:00 2001 From: Roger Stark Date: Sun, 28 Jul 2024 22:48:42 +0200 Subject: [PATCH] update compiled documentation --- docs/odoc.support/odoc.css | 2 +- .../Make/Forward/index.html | 2 +- .../Make/Reverse/index.html | 2 +- .../Make/argument-1-AD/A/Linalg/index.html | 2 +- .../Make/argument-1-AD/A/Mat/index.html | 2 +- .../Make/argument-1-AD/A/Scalar/index.html | 2 +- .../Make/argument-1-AD/A/index.html | 2 +- .../Make/argument-1-AD/Arr/index.html | 2 +- .../Make/argument-1-AD/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Make/argument-1-AD/Linalg/index.html | 2 +- .../Make/argument-1-AD/Mat/index.html | 2 +- .../Make/argument-1-AD/Maths/index.html | 2 +- .../Make/argument-1-AD/NN/index.html | 2 +- .../Make/argument-1-AD/index.html | 2 +- .../Owl_algodiff_check/Make/index.html | 2 +- docs/owl-base/Owl_algodiff_check/index.html | 2 +- .../Make/A/Linalg/index.html | 2 +- .../Owl_algodiff_core/Make/A/Mat/index.html | 2 +- .../Make/A/Scalar/index.html | 2 +- .../Owl_algodiff_core/Make/A/index.html | 2 +- .../Make/argument-1-A/Linalg/index.html | 2 +- .../Make/argument-1-A/Mat/index.html | 2 +- .../Make/argument-1-A/Scalar/index.html | 2 +- .../Make/argument-1-A/index.html | 2 +- .../Owl_algodiff_core/Make/index.html | 2 +- docs/owl-base/Owl_algodiff_core/index.html | 2 +- .../owl-base/Owl_algodiff_core_sig/index.html | 2 +- .../module-type-Sig/A/Linalg/index.html | 2 +- .../module-type-Sig/A/Mat/index.html | 2 +- .../module-type-Sig/A/Scalar/index.html | 2 +- .../module-type-Sig/A/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Make/A/Linalg/index.html | 2 +- .../Make/A/Mat/index.html | 2 +- .../Make/A/Scalar/index.html | 2 +- .../Owl_algodiff_generic/Make/A/index.html | 2 +- .../Owl_algodiff_generic/Make/Arr/index.html | 2 +- .../Make/Builder/index.html | 2 +- .../Make/Builder/module-type-Aiso/index.html | 2 +- .../Make/Builder/module-type-Piso/index.html | 2 +- .../Make/Builder/module-type-Siao/index.html | 2 +- .../Make/Builder/module-type-Sipo/index.html | 2 +- .../Make/Builder/module-type-Siso/index.html | 2 +- .../Make/Builder/module-type-Sito/index.html | 2 +- .../Make/Linalg/index.html | 2 +- .../Owl_algodiff_generic/Make/Mat/index.html | 2 +- .../Make/Maths/index.html | 2 +- .../Owl_algodiff_generic/Make/NN/index.html | 2 +- .../Make/argument-1-A/Linalg/index.html | 2 +- .../Make/argument-1-A/Mat/index.html | 2 +- .../Make/argument-1-A/Scalar/index.html | 2 +- .../Make/argument-1-A/index.html | 2 +- .../Owl_algodiff_generic/Make/index.html | 2 +- docs/owl-base/Owl_algodiff_generic/index.html | 2 +- .../Owl_algodiff_generic_sig/index.html | 2 +- .../module-type-Sig/A/Linalg/index.html | 2 +- .../module-type-Sig/A/Mat/index.html | 2 +- .../module-type-Sig/A/Scalar/index.html | 2 +- .../module-type-Sig/A/index.html | 2 +- .../module-type-Sig/Arr/index.html | 2 +- .../module-type-Sig/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../module-type-Sig/Linalg/index.html | 2 +- .../module-type-Sig/Mat/index.html | 2 +- .../module-type-Sig/Maths/index.html | 2 +- .../module-type-Sig/NN/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Make/argument-1-Core/A/Linalg/index.html | 2 +- .../Make/argument-1-Core/A/Mat/index.html | 2 +- .../Make/argument-1-Core/A/Scalar/index.html | 2 +- .../Make/argument-1-Core/A/index.html | 2 +- .../Make/argument-1-Core/index.html | 2 +- .../Make/index.html | 2 +- .../Owl_algodiff_graph_convert/index.html | 2 +- .../Owl_algodiff_graph_convert_sig/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Owl_algodiff_ops/Make/Arr/index.html | 2 +- .../Owl_algodiff_ops/Make/Builder/index.html | 2 +- .../Make/Builder/module-type-Aiso/index.html | 2 +- .../Make/Builder/module-type-Piso/index.html | 2 +- .../Make/Builder/module-type-Siao/index.html | 2 +- .../Make/Builder/module-type-Sipo/index.html | 2 +- .../Make/Builder/module-type-Siso/index.html | 2 +- .../Make/Builder/module-type-Sito/index.html | 2 +- .../Owl_algodiff_ops/Make/Linalg/index.html | 2 +- .../Owl_algodiff_ops/Make/Mat/index.html | 2 +- .../Owl_algodiff_ops/Make/Maths/index.html | 2 +- .../Owl_algodiff_ops/Make/NN/index.html | 2 +- .../Make/argument-1-Core/A/Linalg/index.html | 2 +- .../Make/argument-1-Core/A/Mat/index.html | 2 +- .../Make/argument-1-Core/A/Scalar/index.html | 2 +- .../Make/argument-1-Core/A/index.html | 2 +- .../Make/argument-1-Core/index.html | 2 +- .../owl-base/Owl_algodiff_ops/Make/index.html | 2 +- docs/owl-base/Owl_algodiff_ops/index.html | 2 +- .../Make/argument-1-Core/A/Linalg/index.html | 2 +- .../Make/argument-1-Core/A/Mat/index.html | 2 +- .../Make/argument-1-Core/A/Scalar/index.html | 2 +- .../Make/argument-1-Core/A/index.html | 2 +- .../Make/argument-1-Core/index.html | 2 +- .../Owl_algodiff_ops_builder/Make/index.html | 2 +- .../Make/module-type-Aiso/index.html | 2 +- .../Make/module-type-Piso/index.html | 2 +- .../Make/module-type-Siao/index.html | 2 +- .../Make/module-type-Sipo/index.html | 2 +- .../Make/module-type-Siso/index.html | 2 +- .../Make/module-type-Sito/index.html | 2 +- .../Owl_algodiff_ops_builder/index.html | 2 +- .../Owl_algodiff_ops_builder_sig/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../module-type-Aiso/index.html | 2 +- .../module-type-Piso/index.html | 2 +- .../module-type-Siao/index.html | 2 +- .../module-type-Sipo/index.html | 2 +- .../module-type-Siso/index.html | 2 +- .../module-type-Sito/index.html | 2 +- docs/owl-base/Owl_algodiff_ops_sig/index.html | 2 +- .../module-type-Sig/Arr/index.html | 2 +- .../module-type-Sig/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../module-type-Sig/Linalg/index.html | 2 +- .../module-type-Sig/Mat/index.html | 2 +- .../module-type-Sig/Maths/index.html | 2 +- .../module-type-Sig/NN/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Make/argument-1-C/A/Linalg/index.html | 2 +- .../Make/argument-1-C/A/Mat/index.html | 2 +- .../Make/argument-1-C/A/Scalar/index.html | 2 +- .../Make/argument-1-C/A/index.html | 2 +- .../Make/argument-1-C/index.html | 2 +- .../Owl_algodiff_reverse/Make/index.html | 2 +- docs/owl-base/Owl_algodiff_reverse/index.html | 2 +- .../Make/argument-1-A/Linalg/index.html | 2 +- .../Make/argument-1-A/Mat/index.html | 2 +- .../Make/argument-1-A/Scalar/index.html | 2 +- .../Make/argument-1-A/index.html | 2 +- .../Owl_algodiff_types/Make/index.html | 2 +- docs/owl-base/Owl_algodiff_types/index.html | 2 +- .../Owl_algodiff_types_sig/index.html | 2 +- .../module-type-Sig/index.html | 2 +- docs/owl-base/Owl_base/index.html | 2 +- .../D/Linalg/index.html | 2 +- .../D/Mat/index.html | 2 +- .../Owl_base_algodiff_primal_ops/D/index.html | 2 +- .../S/Linalg/index.html | 2 +- .../S/Mat/index.html | 2 +- .../Owl_base_algodiff_primal_ops/S/index.html | 2 +- .../Owl_base_algodiff_primal_ops/index.html | 2 +- docs/owl-base/Owl_base_complex/index.html | 2 +- .../owl-base/Owl_base_dense_common/index.html | 2 +- .../Owl_base_dense_matrix_c/index.html | 2 +- .../Owl_base_dense_matrix_d/index.html | 2 +- .../Owl_base_dense_matrix_generic/index.html | 2 +- .../Owl_base_dense_matrix_intf/index.html | 2 +- .../module-type-Common/index.html | 2 +- .../Owl_base_dense_matrix_s/index.html | 2 +- .../Owl_base_dense_matrix_z/index.html | 2 +- .../Owl_base_dense_ndarray/C/index.html | 2 +- .../Owl_base_dense_ndarray/D/index.html | 2 +- .../Owl_base_dense_ndarray/Generic/index.html | 4 +- .../Operator/index.html | 2 +- .../Owl_base_dense_ndarray/S/index.html | 2 +- .../Owl_base_dense_ndarray/Z/index.html | 2 +- .../Owl_base_dense_ndarray/index.html | 2 +- .../Owl_base_dense_ndarray_c/index.html | 2 +- .../Owl_base_dense_ndarray_d/index.html | 2 +- .../Owl_base_dense_ndarray_generic/index.html | 4 +- .../Owl_base_dense_ndarray_intf/index.html | 2 +- .../module-type-Common/index.html | 2 +- .../module-type-NN/index.html | 2 +- .../module-type-Real/index.html | 2 +- .../Owl_base_dense_ndarray_s/index.html | 2 +- .../Owl_base_dense_ndarray_z/index.html | 2 +- docs/owl-base/Owl_base_linalg_c/index.html | 2 +- docs/owl-base/Owl_base_linalg_d/index.html | 2 +- .../Owl_base_linalg_generic/index.html | 2 +- docs/owl-base/Owl_base_linalg_intf/index.html | 2 +- .../module-type-Common/index.html | 2 +- .../module-type-Real/index.html | 2 +- docs/owl-base/Owl_base_linalg_s/index.html | 2 +- docs/owl-base/Owl_base_linalg_z/index.html | 2 +- docs/owl-base/Owl_base_maths/index.html | 19 +- docs/owl-base/Owl_base_slicing/index.html | 2 +- docs/owl-base/Owl_base_stats/index.html | 2 +- .../Owl_base_stats_dist_bernoulli/index.html | 2 +- .../Owl_base_stats_dist_cauchy/index.html | 2 +- .../index.html | 2 +- .../Owl_base_stats_dist_gamma/index.html | 2 +- .../Owl_base_stats_dist_gaussian/index.html | 2 +- .../Owl_base_stats_dist_gumbel1/index.html | 2 +- .../Owl_base_stats_dist_gumbel2/index.html | 2 +- .../Owl_base_stats_dist_uniform/index.html | 2 +- docs/owl-base/Owl_base_stats_prng/index.html | 2 +- docs/owl-base/Owl_computation/index.html | 2 +- .../Make/argument-1-A/Linalg/index.html | 2 +- .../Make/argument-1-A/Mat/index.html | 2 +- .../Make/argument-1-A/Scalar/index.html | 2 +- .../Make/argument-1-A/index.html | 2 +- .../Make/index.html | 2 +- .../Owl_computation_cpu_device/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 2 +- .../Graph/Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 2 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Make/Graph/Optimiser/Operator/index.html | 2 +- .../Make/Graph/Optimiser/index.html | 2 +- .../Make/Graph/index.html | 2 +- .../Make/argument-1-A/Linalg/index.html | 2 +- .../Make/argument-1-A/Mat/index.html | 2 +- .../Make/argument-1-A/Scalar/index.html | 2 +- .../Make/argument-1-A/index.html | 2 +- .../Make/index.html | 2 +- .../Make_Nested/CG_Eval/index.html | 2 +- .../Make_Nested/CG_Init/MultiMap/index.html | 2 +- .../Make_Nested/CG_Init/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Optimiser/Operator/index.html | 200 ++++++++--------- .../argument-1-Graph/Optimiser/index.html | 2 +- .../Make_Nested/argument-1-Graph/index.html | 2 +- .../Make_Nested/index.html | 2 +- .../Owl_computation_cpu_engine/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Optimiser/Operator/index.html | 200 ++++++++--------- .../argument-1-Graph/Optimiser/index.html | 2 +- .../Make/argument-1-Graph/index.html | 2 +- .../Owl_computation_cpu_eval/Make/index.html | 2 +- .../Owl_computation_cpu_eval/index.html | 2 +- .../Make/MultiMap/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Optimiser/Operator/index.html | 200 ++++++++--------- .../argument-1-Graph/Optimiser/index.html | 2 +- .../Make/argument-1-Graph/index.html | 2 +- .../Owl_computation_cpu_init/Make/index.html | 2 +- .../Owl_computation_cpu_init/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Graph/Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Graph/Optimiser/Operator/index.html | 200 ++++++++--------- .../Graph/Optimiser/index.html | 2 +- .../argument-1-Engine/Graph/index.html | 2 +- .../Flatten/argument-1-Engine/index.html | 2 +- .../Owl_computation_engine/Flatten/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 2 +- .../Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 2 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Make_Graph/Optimiser/Operator/index.html | 2 +- .../Make_Graph/Optimiser/index.html | 2 +- .../argument-1-Device/A/Linalg/index.html | 2 +- .../argument-1-Device/A/Mat/index.html | 2 +- .../argument-1-Device/A/Scalar/index.html | 2 +- .../Make_Graph/argument-1-Device/A/index.html | 2 +- .../Make_Graph/argument-1-Device/index.html | 2 +- .../Make_Graph/index.html | 2 +- .../Owl_computation_engine/index.html | 2 +- .../Owl_computation_engine_sig/index.html | 2 +- .../A/Linalg/index.html | 2 +- .../module-type-Flatten_Sig/A/Mat/index.html | 2 +- .../A/Scalar/index.html | 2 +- .../module-type-Flatten_Sig/A/index.html | 2 +- .../Device/A/Linalg/index.html | 2 +- .../Device/A/Mat/index.html | 2 +- .../Device/A/Scalar/index.html | 2 +- .../Device/A/index.html | 2 +- .../module-type-Flatten_Sig/Device/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Graph/Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Graph/Optimiser/Operator/index.html | 200 ++++++++--------- .../Graph/Optimiser/index.html | 2 +- .../module-type-Flatten_Sig/Graph/index.html | 2 +- .../module-type-Flatten_Sig/Linalg/index.html | 16 +- .../module-type-Flatten_Sig/Mat/index.html | 2 +- .../Operator/Linalg/index.html | 16 +- .../Operator/Mat/index.html | 2 +- .../Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Operator/Symbol/index.html | 2 +- .../Operator/index.html | 200 ++++++++--------- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Optimiser/Operator/index.html | 200 ++++++++--------- .../Optimiser/index.html | 2 +- .../module-type-Flatten_Sig/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Shape/Type/Device/A/index.html | 2 +- .../Shape/Type/Device/index.html | 2 +- .../Shape/Type/index.html | 2 +- .../module-type-Flatten_Sig/Shape/index.html | 2 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Symbol/Shape/Type/index.html | 2 +- .../Symbol/Shape/index.html | 2 +- .../module-type-Flatten_Sig/Symbol/index.html | 2 +- .../Type/Device/A/Linalg/index.html | 2 +- .../Type/Device/A/Mat/index.html | 2 +- .../Type/Device/A/Scalar/index.html | 2 +- .../Type/Device/A/index.html | 2 +- .../Type/Device/index.html | 2 +- .../module-type-Flatten_Sig/Type/index.html | 2 +- .../module-type-Flatten_Sig/index.html | 202 +++++++++--------- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Optimiser/Operator/index.html | 200 ++++++++--------- .../Optimiser/index.html | 2 +- .../module-type-Make_Graph_Sig/index.html | 2 +- .../Operator/Linalg/index.html | 16 +- .../Operator/Mat/index.html | 2 +- .../Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Operator/Symbol/index.html | 2 +- .../argument-1-Optimiser/Operator/index.html | 200 ++++++++--------- .../Make/argument-1-Optimiser/index.html | 2 +- .../Owl_computation_graph/Make/index.html | 2 +- .../owl-base/Owl_computation_graph/index.html | 2 +- .../Owl_computation_graph_sig/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Optimiser/Operator/index.html | 200 ++++++++--------- .../module-type-Sig/Optimiser/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Make/Linalg/index.html | 2 +- .../Make/Mat/index.html | 2 +- .../Make/Scalar/index.html | 2 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Shape/Type/Device/A/index.html | 2 +- .../Shape/Type/Device/index.html | 2 +- .../argument-1-Symbol/Shape/Type/index.html | 2 +- .../Make/argument-1-Symbol/Shape/index.html | 2 +- .../Make/argument-1-Symbol/index.html | 2 +- .../Owl_computation_operator/Make/index.html | 2 +- .../Owl_computation_operator/index.html | 2 +- .../Owl_computation_operator_sig/index.html | 2 +- .../module-type-Sig/Linalg/index.html | 16 +- .../module-type-Sig/Mat/index.html | 2 +- .../module-type-Sig/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Symbol/Shape/Type/index.html | 2 +- .../module-type-Sig/Symbol/Shape/index.html | 2 +- .../module-type-Sig/Symbol/index.html | 2 +- .../module-type-Sig/index.html | 200 ++++++++--------- .../argument-1-Operator/Linalg/index.html | 16 +- .../Make/argument-1-Operator/Mat/index.html | 2 +- .../argument-1-Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Symbol/Shape/Type/index.html | 2 +- .../Symbol/Shape/index.html | 2 +- .../argument-1-Operator/Symbol/index.html | 2 +- .../Make/argument-1-Operator/index.html | 200 ++++++++--------- .../Owl_computation_optimiser/Make/index.html | 2 +- .../Owl_computation_optimiser/index.html | 2 +- .../Owl_computation_optimiser_sig/index.html | 2 +- .../Operator/Linalg/index.html | 16 +- .../module-type-Sig/Operator/Mat/index.html | 2 +- .../Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Operator/Symbol/index.html | 2 +- .../module-type-Sig/Operator/index.html | 200 ++++++++--------- .../module-type-Sig/index.html | 2 +- .../Device/A/Linalg/index.html | 2 +- .../argument-1-Type/Device/A/Mat/index.html | 2 +- .../Device/A/Scalar/index.html | 2 +- .../Make/argument-1-Type/Device/A/index.html | 2 +- .../Make/argument-1-Type/Device/index.html | 2 +- .../Make/argument-1-Type/index.html | 2 +- .../Owl_computation_shape/Make/index.html | 2 +- .../owl-base/Owl_computation_shape/index.html | 2 +- .../Owl_computation_shape_sig/index.html | 2 +- .../Type/Device/A/Linalg/index.html | 2 +- .../Type/Device/A/Mat/index.html | 2 +- .../Type/Device/A/Scalar/index.html | 2 +- .../module-type-Sig/Type/Device/A/index.html | 2 +- .../module-type-Sig/Type/Device/index.html | 2 +- .../module-type-Sig/Type/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Type/Device/A/Linalg/index.html | 2 +- .../Type/Device/A/Mat/index.html | 2 +- .../Type/Device/A/Scalar/index.html | 2 +- .../argument-1-Shape/Type/Device/A/index.html | 2 +- .../argument-1-Shape/Type/Device/index.html | 2 +- .../Make/argument-1-Shape/Type/index.html | 2 +- .../Make/argument-1-Shape/index.html | 2 +- .../Owl_computation_symbol/Make/index.html | 2 +- .../Owl_computation_symbol/index.html | 2 +- .../Owl_computation_symbol_sig/index.html | 2 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Shape/Type/Device/A/index.html | 2 +- .../Shape/Type/Device/index.html | 2 +- .../module-type-Sig/Shape/Type/index.html | 2 +- .../module-type-Sig/Shape/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../argument-1-Device/A/Linalg/index.html | 2 +- .../Make/argument-1-Device/A/Mat/index.html | 2 +- .../argument-1-Device/A/Scalar/index.html | 2 +- .../Make/argument-1-Device/A/index.html | 2 +- .../Make/argument-1-Device/index.html | 2 +- .../Owl_computation_type/Make/index.html | 2 +- docs/owl-base/Owl_computation_type/index.html | 2 +- .../Owl_computation_type_sig/index.html | 2 +- .../Device/A/Linalg/index.html | 2 +- .../module-type-Sig/Device/A/Mat/index.html | 2 +- .../Device/A/Scalar/index.html | 2 +- .../module-type-Sig/Device/A/index.html | 2 +- .../module-type-Sig/Device/index.html | 2 +- .../module-type-Sig/index.html | 2 +- docs/owl-base/Owl_const/CGS/index.html | 2 +- docs/owl-base/Owl_const/CGSM/index.html | 2 +- docs/owl-base/Owl_const/MKS/index.html | 2 +- docs/owl-base/Owl_const/Prefix/index.html | 2 +- docs/owl-base/Owl_const/SI/index.html | 2 +- docs/owl-base/Owl_const/index.html | 2 +- .../Make/argument-1-T/index.html | 2 +- .../Owl_countmin_sketch/Make/index.html | 2 +- .../Owl_countmin_sketch/Native/index.html | 2 +- .../Owl_countmin_sketch/Owl/index.html | 2 +- docs/owl-base/Owl_countmin_sketch/index.html | 2 +- .../Owl_countmin_sketch_sig/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Owl_countmin_table/Native/index.html | 2 +- .../Owl_countmin_table/Owl/index.html | 2 +- docs/owl-base/Owl_countmin_table/index.html | 2 +- .../module-type-Sig/index.html | 2 +- docs/owl-base/Owl_dataframe/index.html | 2 +- docs/owl-base/Owl_exception/index.html | 2 +- docs/owl-base/Owl_graph/index.html | 2 +- .../Make/argument-1-CM/index.html | 2 +- .../Owl_heavyhitters_sketch/Make/index.html | 2 +- .../Owl_heavyhitters_sketch/Native/index.html | 2 +- .../Owl_heavyhitters_sketch/Owl/index.html | 2 +- .../Owl_heavyhitters_sketch/index.html | 2 +- .../Owl_heavyhitters_sketch_sig/index.html | 2 +- .../module-type-Sig/index.html | 2 +- docs/owl-base/Owl_io/index.html | 12 +- .../Make/argument-1-A/Linalg/index.html | 2 +- .../Owl_lazy/Make/argument-1-A/Mat/index.html | 2 +- .../Make/argument-1-A/Scalar/index.html | 2 +- .../Owl_lazy/Make/argument-1-A/index.html | 2 +- docs/owl-base/Owl_lazy/Make/index.html | 2 +- docs/owl-base/Owl_lazy/index.html | 2 +- docs/owl-base/Owl_log/index.html | 2 +- .../owl-base/Owl_maths_interpolate/index.html | 2 +- docs/owl-base/Owl_maths_quadrature/index.html | 23 +- docs/owl-base/Owl_maths_root/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 2 +- .../Graph/Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 2 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Graph/Optimiser/Operator/index.html | 2 +- .../Make/Engine/Graph/Optimiser/index.html | 2 +- .../Make/Engine/Graph/index.html | 2 +- .../Make/Engine/index.html | 2 +- .../Neural/Graph/Neuron/Activation/index.html | 2 +- .../Make/Neural/Graph/Neuron/Add/index.html | 2 +- .../Graph/Neuron/AlphaDropout/index.html | 2 +- .../Neural/Graph/Neuron/Average/index.html | 2 +- .../Neural/Graph/Neuron/AvgPool1D/index.html | 2 +- .../Neural/Graph/Neuron/AvgPool2D/index.html | 2 +- .../Graph/Neuron/Concatenate/index.html | 2 +- .../Neural/Graph/Neuron/Conv1D/index.html | 2 +- .../Neural/Graph/Neuron/Conv2D/index.html | 2 +- .../Neural/Graph/Neuron/Conv3D/index.html | 2 +- .../Graph/Neuron/DilatedConv1D/index.html | 2 +- .../Graph/Neuron/DilatedConv2D/index.html | 2 +- .../Graph/Neuron/DilatedConv3D/index.html | 2 +- .../Make/Neural/Graph/Neuron/Dot/index.html | 2 +- .../Neural/Graph/Neuron/Dropout/index.html | 2 +- .../Neural/Graph/Neuron/Embedding/index.html | 2 +- .../Neural/Graph/Neuron/Flatten/index.html | 2 +- .../Graph/Neuron/FullyConnected/index.html | 2 +- .../Make/Neural/Graph/Neuron/GRU/index.html | 2 +- .../Graph/Neuron/GaussianDropout/index.html | 2 +- .../Graph/Neuron/GaussianNoise/index.html | 2 +- .../Graph/Neuron/GlobalAvgPool1D/index.html | 2 +- .../Graph/Neuron/GlobalAvgPool2D/index.html | 2 +- .../Graph/Neuron/GlobalMaxPool1D/index.html | 2 +- .../Graph/Neuron/GlobalMaxPool2D/index.html | 2 +- .../Make/Neural/Graph/Neuron/Init/index.html | 2 +- .../Make/Neural/Graph/Neuron/Input/index.html | 2 +- .../Make/Neural/Graph/Neuron/LSTM/index.html | 2 +- .../Neural/Graph/Neuron/Lambda/index.html | 2 +- .../Graph/Neuron/LambdaArray/index.html | 2 +- .../Neural/Graph/Neuron/Linear/index.html | 2 +- .../Graph/Neuron/LinearNoBias/index.html | 2 +- .../Neural/Graph/Neuron/Masking/index.html | 2 +- .../Make/Neural/Graph/Neuron/Max/index.html | 2 +- .../Neural/Graph/Neuron/MaxPool1D/index.html | 2 +- .../Neural/Graph/Neuron/MaxPool2D/index.html | 2 +- .../Make/Neural/Graph/Neuron/Mul/index.html | 2 +- .../Graph/Neuron/Normalisation/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/index.html | 2 +- .../Neuron/Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/Mat/index.html | 2 +- .../Neuron/Optimise/Algodiff/Maths/index.html | 2 +- .../Neuron/Optimise/Algodiff/NN/index.html | 2 +- .../Graph/Neuron/Optimise/Algodiff/index.html | 2 +- .../Graph/Neuron/Optimise/Batch/index.html | 2 +- .../Neuron/Optimise/Checkpoint/index.html | 2 +- .../Graph/Neuron/Optimise/Clipping/index.html | 2 +- .../Graph/Neuron/Optimise/Gradient/index.html | 2 +- .../Neuron/Optimise/Learning_Rate/index.html | 2 +- .../Graph/Neuron/Optimise/Loss/index.html | 2 +- .../Graph/Neuron/Optimise/Momentum/index.html | 2 +- .../Graph/Neuron/Optimise/Params/index.html | 2 +- .../Neuron/Optimise/Regularisation/index.html | 2 +- .../Graph/Neuron/Optimise/Stopping/index.html | 2 +- .../Graph/Neuron/Optimise/Utils/index.html | 2 +- .../Neural/Graph/Neuron/Optimise/index.html | 2 +- .../Neural/Graph/Neuron/Padding1D/index.html | 2 +- .../Neural/Graph/Neuron/Padding2D/index.html | 2 +- .../Neural/Graph/Neuron/Padding3D/index.html | 2 +- .../Neural/Graph/Neuron/Recurrent/index.html | 2 +- .../Neural/Graph/Neuron/Reshape/index.html | 2 +- .../Make/Neural/Graph/Neuron/Slice/index.html | 2 +- .../Graph/Neuron/TransposeConv1D/index.html | 2 +- .../Graph/Neuron/TransposeConv2D/index.html | 2 +- .../Graph/Neuron/TransposeConv3D/index.html | 2 +- .../Graph/Neuron/UpSampling1D/index.html | 2 +- .../Graph/Neuron/UpSampling2D/index.html | 2 +- .../Graph/Neuron/UpSampling3D/index.html | 2 +- .../Make/Neural/Graph/Neuron/index.html | 2 +- .../Make/Neural/Graph/index.html | 2 +- .../Make/Neural/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Graph/Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Graph/Optimiser/Operator/index.html | 200 ++++++++--------- .../argument-1-E/Graph/Optimiser/index.html | 2 +- .../Make/argument-1-E/Graph/index.html | 2 +- .../Make/argument-1-E/index.html | 2 +- .../Owl_neural_compiler/Make/index.html | 2 +- docs/owl-base/Owl_neural_compiler/index.html | 2 +- .../Neuron/Activation/index.html | 2 +- .../argument-1-Graph/Neuron/Add/index.html | 2 +- .../Neuron/AlphaDropout/index.html | 2 +- .../Neuron/Average/index.html | 2 +- .../Neuron/AvgPool1D/index.html | 2 +- .../Neuron/AvgPool2D/index.html | 2 +- .../Neuron/Concatenate/index.html | 2 +- .../argument-1-Graph/Neuron/Conv1D/index.html | 2 +- .../argument-1-Graph/Neuron/Conv2D/index.html | 2 +- .../argument-1-Graph/Neuron/Conv3D/index.html | 2 +- .../Neuron/DilatedConv1D/index.html | 2 +- .../Neuron/DilatedConv2D/index.html | 2 +- .../Neuron/DilatedConv3D/index.html | 2 +- .../argument-1-Graph/Neuron/Dot/index.html | 2 +- .../Neuron/Dropout/index.html | 2 +- .../Neuron/Embedding/index.html | 2 +- .../Neuron/Flatten/index.html | 2 +- .../Neuron/FullyConnected/index.html | 2 +- .../argument-1-Graph/Neuron/GRU/index.html | 2 +- .../Neuron/GaussianDropout/index.html | 2 +- .../Neuron/GaussianNoise/index.html | 2 +- .../Neuron/GlobalAvgPool1D/index.html | 2 +- .../Neuron/GlobalAvgPool2D/index.html | 2 +- .../Neuron/GlobalMaxPool1D/index.html | 2 +- .../Neuron/GlobalMaxPool2D/index.html | 2 +- .../argument-1-Graph/Neuron/Init/index.html | 2 +- .../argument-1-Graph/Neuron/Input/index.html | 2 +- .../argument-1-Graph/Neuron/LSTM/index.html | 2 +- .../argument-1-Graph/Neuron/Lambda/index.html | 2 +- .../Neuron/LambdaArray/index.html | 2 +- .../argument-1-Graph/Neuron/Linear/index.html | 2 +- .../Neuron/LinearNoBias/index.html | 2 +- .../Neuron/Masking/index.html | 2 +- .../argument-1-Graph/Neuron/Max/index.html | 2 +- .../Neuron/MaxPool1D/index.html | 2 +- .../Neuron/MaxPool2D/index.html | 2 +- .../argument-1-Graph/Neuron/Mul/index.html | 2 +- .../Neuron/Normalisation/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/index.html | 2 +- .../Neuron/Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/Mat/index.html | 2 +- .../Neuron/Optimise/Algodiff/Maths/index.html | 2 +- .../Neuron/Optimise/Algodiff/NN/index.html | 2 +- .../Neuron/Optimise/Algodiff/index.html | 2 +- .../Neuron/Optimise/Batch/index.html | 2 +- .../Neuron/Optimise/Checkpoint/index.html | 2 +- .../Neuron/Optimise/Clipping/index.html | 2 +- .../Neuron/Optimise/Gradient/index.html | 2 +- .../Neuron/Optimise/Learning_Rate/index.html | 2 +- .../Neuron/Optimise/Loss/index.html | 2 +- .../Neuron/Optimise/Momentum/index.html | 2 +- .../Neuron/Optimise/Params/index.html | 2 +- .../Neuron/Optimise/Regularisation/index.html | 2 +- .../Neuron/Optimise/Stopping/index.html | 2 +- .../Neuron/Optimise/Utils/index.html | 2 +- .../Neuron/Optimise/index.html | 4 +- .../Neuron/Padding1D/index.html | 2 +- .../Neuron/Padding2D/index.html | 2 +- .../Neuron/Padding3D/index.html | 2 +- .../Neuron/Recurrent/index.html | 2 +- .../Neuron/Reshape/index.html | 2 +- .../argument-1-Graph/Neuron/Slice/index.html | 2 +- .../Neuron/TransposeConv1D/index.html | 2 +- .../Neuron/TransposeConv2D/index.html | 2 +- .../Neuron/TransposeConv3D/index.html | 2 +- .../Neuron/UpSampling1D/index.html | 2 +- .../Neuron/UpSampling2D/index.html | 2 +- .../Neuron/UpSampling3D/index.html | 2 +- .../argument-1-Graph/Neuron/index.html | 2 +- .../Flatten/argument-1-Graph/index.html | 2 +- .../Owl_neural_generic/Flatten/index.html | 2 +- .../Make/Graph/Neuron/Activation/index.html | 2 +- .../Make/Graph/Neuron/Add/index.html | 2 +- .../Make/Graph/Neuron/AlphaDropout/index.html | 2 +- .../Make/Graph/Neuron/Average/index.html | 2 +- .../Make/Graph/Neuron/AvgPool1D/index.html | 2 +- .../Make/Graph/Neuron/AvgPool2D/index.html | 2 +- .../Make/Graph/Neuron/Concatenate/index.html | 2 +- .../Make/Graph/Neuron/Conv1D/index.html | 2 +- .../Make/Graph/Neuron/Conv2D/index.html | 2 +- .../Make/Graph/Neuron/Conv3D/index.html | 2 +- .../Graph/Neuron/DilatedConv1D/index.html | 2 +- .../Graph/Neuron/DilatedConv2D/index.html | 2 +- .../Graph/Neuron/DilatedConv3D/index.html | 2 +- .../Make/Graph/Neuron/Dot/index.html | 2 +- .../Make/Graph/Neuron/Dropout/index.html | 2 +- .../Make/Graph/Neuron/Embedding/index.html | 2 +- .../Make/Graph/Neuron/Flatten/index.html | 2 +- .../Graph/Neuron/FullyConnected/index.html | 2 +- .../Make/Graph/Neuron/GRU/index.html | 2 +- .../Graph/Neuron/GaussianDropout/index.html | 2 +- .../Graph/Neuron/GaussianNoise/index.html | 2 +- .../Graph/Neuron/GlobalAvgPool1D/index.html | 2 +- .../Graph/Neuron/GlobalAvgPool2D/index.html | 2 +- .../Graph/Neuron/GlobalMaxPool1D/index.html | 2 +- .../Graph/Neuron/GlobalMaxPool2D/index.html | 2 +- .../Make/Graph/Neuron/Init/index.html | 2 +- .../Make/Graph/Neuron/Input/index.html | 2 +- .../Make/Graph/Neuron/LSTM/index.html | 2 +- .../Make/Graph/Neuron/Lambda/index.html | 2 +- .../Make/Graph/Neuron/LambdaArray/index.html | 2 +- .../Make/Graph/Neuron/Linear/index.html | 2 +- .../Make/Graph/Neuron/LinearNoBias/index.html | 2 +- .../Make/Graph/Neuron/Masking/index.html | 2 +- .../Make/Graph/Neuron/Max/index.html | 2 +- .../Make/Graph/Neuron/MaxPool1D/index.html | 2 +- .../Make/Graph/Neuron/MaxPool2D/index.html | 2 +- .../Make/Graph/Neuron/Mul/index.html | 2 +- .../Graph/Neuron/Normalisation/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/index.html | 2 +- .../Neuron/Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/Mat/index.html | 2 +- .../Neuron/Optimise/Algodiff/Maths/index.html | 2 +- .../Neuron/Optimise/Algodiff/NN/index.html | 2 +- .../Graph/Neuron/Optimise/Algodiff/index.html | 2 +- .../Graph/Neuron/Optimise/Batch/index.html | 2 +- .../Neuron/Optimise/Checkpoint/index.html | 2 +- .../Graph/Neuron/Optimise/Clipping/index.html | 2 +- .../Graph/Neuron/Optimise/Gradient/index.html | 2 +- .../Neuron/Optimise/Learning_Rate/index.html | 2 +- .../Graph/Neuron/Optimise/Loss/index.html | 2 +- .../Graph/Neuron/Optimise/Momentum/index.html | 2 +- .../Graph/Neuron/Optimise/Params/index.html | 2 +- .../Neuron/Optimise/Regularisation/index.html | 2 +- .../Graph/Neuron/Optimise/Stopping/index.html | 2 +- .../Graph/Neuron/Optimise/Utils/index.html | 2 +- .../Make/Graph/Neuron/Optimise/index.html | 2 +- .../Make/Graph/Neuron/Padding1D/index.html | 2 +- .../Make/Graph/Neuron/Padding2D/index.html | 2 +- .../Make/Graph/Neuron/Padding3D/index.html | 2 +- .../Make/Graph/Neuron/Recurrent/index.html | 2 +- .../Make/Graph/Neuron/Reshape/index.html | 2 +- .../Make/Graph/Neuron/Slice/index.html | 2 +- .../Graph/Neuron/TransposeConv1D/index.html | 2 +- .../Graph/Neuron/TransposeConv2D/index.html | 2 +- .../Graph/Neuron/TransposeConv3D/index.html | 2 +- .../Make/Graph/Neuron/UpSampling1D/index.html | 2 +- .../Make/Graph/Neuron/UpSampling2D/index.html | 2 +- .../Make/Graph/Neuron/UpSampling3D/index.html | 2 +- .../Make/Graph/Neuron/index.html | 2 +- .../Owl_neural_generic/Make/Graph/index.html | 2 +- .../Make/argument-1-A/Linalg/index.html | 2 +- .../Make/argument-1-A/Mat/index.html | 2 +- .../Make/argument-1-A/Scalar/index.html | 2 +- .../Make/argument-1-A/index.html | 2 +- .../Owl_neural_generic/Make/index.html | 2 +- .../Neuron/Activation/index.html | 2 +- .../Make_Embedded/Neuron/Add/index.html | 2 +- .../Neuron/AlphaDropout/index.html | 2 +- .../Make_Embedded/Neuron/Average/index.html | 2 +- .../Make_Embedded/Neuron/AvgPool1D/index.html | 2 +- .../Make_Embedded/Neuron/AvgPool2D/index.html | 2 +- .../Neuron/Concatenate/index.html | 2 +- .../Make_Embedded/Neuron/Conv1D/index.html | 2 +- .../Make_Embedded/Neuron/Conv2D/index.html | 2 +- .../Make_Embedded/Neuron/Conv3D/index.html | 2 +- .../Neuron/DilatedConv1D/index.html | 2 +- .../Neuron/DilatedConv2D/index.html | 2 +- .../Neuron/DilatedConv3D/index.html | 2 +- .../Make_Embedded/Neuron/Dot/index.html | 2 +- .../Make_Embedded/Neuron/Dropout/index.html | 2 +- .../Make_Embedded/Neuron/Embedding/index.html | 2 +- .../Make_Embedded/Neuron/Flatten/index.html | 2 +- .../Neuron/FullyConnected/index.html | 2 +- .../Make_Embedded/Neuron/GRU/index.html | 2 +- .../Neuron/GaussianDropout/index.html | 2 +- .../Neuron/GaussianNoise/index.html | 2 +- .../Neuron/GlobalAvgPool1D/index.html | 2 +- .../Neuron/GlobalAvgPool2D/index.html | 2 +- .../Neuron/GlobalMaxPool1D/index.html | 2 +- .../Neuron/GlobalMaxPool2D/index.html | 2 +- .../Make_Embedded/Neuron/Init/index.html | 2 +- .../Make_Embedded/Neuron/Input/index.html | 2 +- .../Make_Embedded/Neuron/LSTM/index.html | 2 +- .../Make_Embedded/Neuron/Lambda/index.html | 2 +- .../Neuron/LambdaArray/index.html | 2 +- .../Make_Embedded/Neuron/Linear/index.html | 2 +- .../Neuron/LinearNoBias/index.html | 2 +- .../Make_Embedded/Neuron/Masking/index.html | 2 +- .../Make_Embedded/Neuron/Max/index.html | 2 +- .../Make_Embedded/Neuron/MaxPool1D/index.html | 2 +- .../Make_Embedded/Neuron/MaxPool2D/index.html | 2 +- .../Make_Embedded/Neuron/Mul/index.html | 2 +- .../Neuron/Normalisation/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/index.html | 2 +- .../Neuron/Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/Mat/index.html | 2 +- .../Neuron/Optimise/Algodiff/Maths/index.html | 2 +- .../Neuron/Optimise/Algodiff/NN/index.html | 2 +- .../Neuron/Optimise/Algodiff/index.html | 2 +- .../Neuron/Optimise/Batch/index.html | 2 +- .../Neuron/Optimise/Checkpoint/index.html | 2 +- .../Neuron/Optimise/Clipping/index.html | 2 +- .../Neuron/Optimise/Gradient/index.html | 2 +- .../Neuron/Optimise/Learning_Rate/index.html | 2 +- .../Neuron/Optimise/Loss/index.html | 2 +- .../Neuron/Optimise/Momentum/index.html | 2 +- .../Neuron/Optimise/Params/index.html | 2 +- .../Neuron/Optimise/Regularisation/index.html | 2 +- .../Neuron/Optimise/Stopping/index.html | 2 +- .../Neuron/Optimise/Utils/index.html | 2 +- .../Make_Embedded/Neuron/Optimise/index.html | 2 +- .../Make_Embedded/Neuron/Padding1D/index.html | 2 +- .../Make_Embedded/Neuron/Padding2D/index.html | 2 +- .../Make_Embedded/Neuron/Padding3D/index.html | 2 +- .../Make_Embedded/Neuron/Recurrent/index.html | 2 +- .../Make_Embedded/Neuron/Reshape/index.html | 2 +- .../Make_Embedded/Neuron/Slice/index.html | 2 +- .../Neuron/TransposeConv1D/index.html | 2 +- .../Neuron/TransposeConv2D/index.html | 2 +- .../Neuron/TransposeConv3D/index.html | 2 +- .../Neuron/UpSampling1D/index.html | 2 +- .../Neuron/UpSampling2D/index.html | 2 +- .../Neuron/UpSampling3D/index.html | 2 +- .../Make_Embedded/Neuron/index.html | 2 +- .../argument-1-A/Linalg/index.html | 2 +- .../Make_Embedded/argument-1-A/Mat/index.html | 2 +- .../argument-1-A/Scalar/index.html | 2 +- .../Make_Embedded/argument-1-A/index.html | 2 +- .../Make_Embedded/index.html | 2 +- docs/owl-base/Owl_neural_generic/index.html | 2 +- .../argument-1-Neuron/Activation/index.html | 2 +- .../Make/argument-1-Neuron/Add/index.html | 2 +- .../argument-1-Neuron/AlphaDropout/index.html | 2 +- .../Make/argument-1-Neuron/Average/index.html | 2 +- .../argument-1-Neuron/AvgPool1D/index.html | 2 +- .../argument-1-Neuron/AvgPool2D/index.html | 2 +- .../argument-1-Neuron/Concatenate/index.html | 2 +- .../Make/argument-1-Neuron/Conv1D/index.html | 2 +- .../Make/argument-1-Neuron/Conv2D/index.html | 2 +- .../Make/argument-1-Neuron/Conv3D/index.html | 2 +- .../DilatedConv1D/index.html | 2 +- .../DilatedConv2D/index.html | 2 +- .../DilatedConv3D/index.html | 2 +- .../Make/argument-1-Neuron/Dot/index.html | 2 +- .../Make/argument-1-Neuron/Dropout/index.html | 2 +- .../argument-1-Neuron/Embedding/index.html | 2 +- .../Make/argument-1-Neuron/Flatten/index.html | 2 +- .../FullyConnected/index.html | 2 +- .../Make/argument-1-Neuron/GRU/index.html | 2 +- .../GaussianDropout/index.html | 2 +- .../GaussianNoise/index.html | 2 +- .../GlobalAvgPool1D/index.html | 2 +- .../GlobalAvgPool2D/index.html | 2 +- .../GlobalMaxPool1D/index.html | 2 +- .../GlobalMaxPool2D/index.html | 2 +- .../Make/argument-1-Neuron/Init/index.html | 2 +- .../Make/argument-1-Neuron/Input/index.html | 2 +- .../Make/argument-1-Neuron/LSTM/index.html | 2 +- .../Make/argument-1-Neuron/Lambda/index.html | 2 +- .../argument-1-Neuron/LambdaArray/index.html | 2 +- .../Make/argument-1-Neuron/Linear/index.html | 2 +- .../argument-1-Neuron/LinearNoBias/index.html | 2 +- .../Make/argument-1-Neuron/Masking/index.html | 2 +- .../Make/argument-1-Neuron/Max/index.html | 2 +- .../argument-1-Neuron/MaxPool1D/index.html | 2 +- .../argument-1-Neuron/MaxPool2D/index.html | 2 +- .../Make/argument-1-Neuron/Mul/index.html | 2 +- .../Normalisation/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Optimise/Algodiff/A/index.html | 2 +- .../Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Optimise/Algodiff/Mat/index.html | 2 +- .../Optimise/Algodiff/Maths/index.html | 2 +- .../Optimise/Algodiff/NN/index.html | 2 +- .../Optimise/Algodiff/index.html | 2 +- .../Optimise/Batch/index.html | 2 +- .../Optimise/Checkpoint/index.html | 2 +- .../Optimise/Clipping/index.html | 2 +- .../Optimise/Gradient/index.html | 2 +- .../Optimise/Learning_Rate/index.html | 2 +- .../Optimise/Loss/index.html | 2 +- .../Optimise/Momentum/index.html | 2 +- .../Optimise/Params/index.html | 2 +- .../Optimise/Regularisation/index.html | 2 +- .../Optimise/Stopping/index.html | 2 +- .../Optimise/Utils/index.html | 2 +- .../argument-1-Neuron/Optimise/index.html | 4 +- .../argument-1-Neuron/Padding1D/index.html | 2 +- .../argument-1-Neuron/Padding2D/index.html | 2 +- .../argument-1-Neuron/Padding3D/index.html | 2 +- .../argument-1-Neuron/Recurrent/index.html | 2 +- .../Make/argument-1-Neuron/Reshape/index.html | 2 +- .../Make/argument-1-Neuron/Slice/index.html | 2 +- .../TransposeConv1D/index.html | 2 +- .../TransposeConv2D/index.html | 2 +- .../TransposeConv3D/index.html | 2 +- .../argument-1-Neuron/UpSampling1D/index.html | 2 +- .../argument-1-Neuron/UpSampling2D/index.html | 2 +- .../argument-1-Neuron/UpSampling3D/index.html | 2 +- .../Make/argument-1-Neuron/index.html | 2 +- .../owl-base/Owl_neural_graph/Make/index.html | 2 +- docs/owl-base/Owl_neural_graph/index.html | 2 +- docs/owl-base/Owl_neural_graph_sig/index.html | 2 +- .../Neuron/Activation/index.html | 2 +- .../module-type-Sig/Neuron/Add/index.html | 2 +- .../Neuron/AlphaDropout/index.html | 2 +- .../module-type-Sig/Neuron/Average/index.html | 2 +- .../Neuron/AvgPool1D/index.html | 2 +- .../Neuron/AvgPool2D/index.html | 2 +- .../Neuron/Concatenate/index.html | 2 +- .../module-type-Sig/Neuron/Conv1D/index.html | 2 +- .../module-type-Sig/Neuron/Conv2D/index.html | 2 +- .../module-type-Sig/Neuron/Conv3D/index.html | 2 +- .../Neuron/DilatedConv1D/index.html | 2 +- .../Neuron/DilatedConv2D/index.html | 2 +- .../Neuron/DilatedConv3D/index.html | 2 +- .../module-type-Sig/Neuron/Dot/index.html | 2 +- .../module-type-Sig/Neuron/Dropout/index.html | 2 +- .../Neuron/Embedding/index.html | 2 +- .../module-type-Sig/Neuron/Flatten/index.html | 2 +- .../Neuron/FullyConnected/index.html | 2 +- .../module-type-Sig/Neuron/GRU/index.html | 2 +- .../Neuron/GaussianDropout/index.html | 2 +- .../Neuron/GaussianNoise/index.html | 2 +- .../Neuron/GlobalAvgPool1D/index.html | 2 +- .../Neuron/GlobalAvgPool2D/index.html | 2 +- .../Neuron/GlobalMaxPool1D/index.html | 2 +- .../Neuron/GlobalMaxPool2D/index.html | 2 +- .../module-type-Sig/Neuron/Init/index.html | 2 +- .../module-type-Sig/Neuron/Input/index.html | 2 +- .../module-type-Sig/Neuron/LSTM/index.html | 2 +- .../module-type-Sig/Neuron/Lambda/index.html | 2 +- .../Neuron/LambdaArray/index.html | 2 +- .../module-type-Sig/Neuron/Linear/index.html | 2 +- .../Neuron/LinearNoBias/index.html | 2 +- .../module-type-Sig/Neuron/Masking/index.html | 2 +- .../module-type-Sig/Neuron/Max/index.html | 2 +- .../Neuron/MaxPool1D/index.html | 2 +- .../Neuron/MaxPool2D/index.html | 2 +- .../module-type-Sig/Neuron/Mul/index.html | 2 +- .../Neuron/Normalisation/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/index.html | 2 +- .../Neuron/Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/Mat/index.html | 2 +- .../Neuron/Optimise/Algodiff/Maths/index.html | 2 +- .../Neuron/Optimise/Algodiff/NN/index.html | 2 +- .../Neuron/Optimise/Algodiff/index.html | 2 +- .../Neuron/Optimise/Batch/index.html | 2 +- .../Neuron/Optimise/Checkpoint/index.html | 2 +- .../Neuron/Optimise/Clipping/index.html | 2 +- .../Neuron/Optimise/Gradient/index.html | 2 +- .../Neuron/Optimise/Learning_Rate/index.html | 2 +- .../Neuron/Optimise/Loss/index.html | 2 +- .../Neuron/Optimise/Momentum/index.html | 2 +- .../Neuron/Optimise/Params/index.html | 2 +- .../Neuron/Optimise/Regularisation/index.html | 2 +- .../Neuron/Optimise/Stopping/index.html | 2 +- .../Neuron/Optimise/Utils/index.html | 2 +- .../Neuron/Optimise/index.html | 4 +- .../Neuron/Padding1D/index.html | 2 +- .../Neuron/Padding2D/index.html | 2 +- .../Neuron/Padding3D/index.html | 2 +- .../Neuron/Recurrent/index.html | 2 +- .../module-type-Sig/Neuron/Reshape/index.html | 2 +- .../module-type-Sig/Neuron/Slice/index.html | 2 +- .../Neuron/TransposeConv1D/index.html | 2 +- .../Neuron/TransposeConv2D/index.html | 2 +- .../Neuron/TransposeConv3D/index.html | 2 +- .../Neuron/UpSampling1D/index.html | 2 +- .../Neuron/UpSampling2D/index.html | 2 +- .../Neuron/UpSampling3D/index.html | 2 +- .../module-type-Sig/Neuron/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Make/Activation/index.html | 2 +- .../Owl_neural_neuron/Make/Add/index.html | 2 +- .../Make/AlphaDropout/index.html | 2 +- .../Owl_neural_neuron/Make/Average/index.html | 2 +- .../Make/AvgPool1D/index.html | 2 +- .../Make/AvgPool2D/index.html | 2 +- .../Make/Concatenate/index.html | 2 +- .../Owl_neural_neuron/Make/Conv1D/index.html | 2 +- .../Owl_neural_neuron/Make/Conv2D/index.html | 2 +- .../Owl_neural_neuron/Make/Conv3D/index.html | 2 +- .../Make/DilatedConv1D/index.html | 2 +- .../Make/DilatedConv2D/index.html | 2 +- .../Make/DilatedConv3D/index.html | 2 +- .../Owl_neural_neuron/Make/Dot/index.html | 2 +- .../Owl_neural_neuron/Make/Dropout/index.html | 2 +- .../Make/Embedding/index.html | 2 +- .../Owl_neural_neuron/Make/Flatten/index.html | 2 +- .../Make/FullyConnected/index.html | 2 +- .../Owl_neural_neuron/Make/GRU/index.html | 2 +- .../Make/GaussianDropout/index.html | 2 +- .../Make/GaussianNoise/index.html | 2 +- .../Make/GlobalAvgPool1D/index.html | 2 +- .../Make/GlobalAvgPool2D/index.html | 2 +- .../Make/GlobalMaxPool1D/index.html | 2 +- .../Make/GlobalMaxPool2D/index.html | 2 +- .../Owl_neural_neuron/Make/Init/index.html | 2 +- .../Owl_neural_neuron/Make/Input/index.html | 2 +- .../Owl_neural_neuron/Make/LSTM/index.html | 2 +- .../Owl_neural_neuron/Make/Lambda/index.html | 2 +- .../Make/LambdaArray/index.html | 2 +- .../Owl_neural_neuron/Make/Linear/index.html | 2 +- .../Make/LinearNoBias/index.html | 2 +- .../Owl_neural_neuron/Make/Masking/index.html | 2 +- .../Owl_neural_neuron/Make/Max/index.html | 2 +- .../Make/MaxPool1D/index.html | 2 +- .../Make/MaxPool2D/index.html | 2 +- .../Owl_neural_neuron/Make/Mul/index.html | 2 +- .../Make/Normalisation/index.html | 2 +- .../Make/Padding1D/index.html | 2 +- .../Make/Padding2D/index.html | 2 +- .../Make/Padding3D/index.html | 2 +- .../Make/Recurrent/index.html | 2 +- .../Owl_neural_neuron/Make/Reshape/index.html | 2 +- .../Owl_neural_neuron/Make/Slice/index.html | 2 +- .../Make/TransposeConv1D/index.html | 2 +- .../Make/TransposeConv2D/index.html | 2 +- .../Make/TransposeConv3D/index.html | 2 +- .../Make/UpSampling1D/index.html | 2 +- .../Make/UpSampling2D/index.html | 2 +- .../Make/UpSampling3D/index.html | 2 +- .../Algodiff/A/Linalg/index.html | 2 +- .../Algodiff/A/Mat/index.html | 2 +- .../Algodiff/A/Scalar/index.html | 2 +- .../argument-1-Optimise/Algodiff/A/index.html | 2 +- .../Algodiff/Arr/index.html | 2 +- .../Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Algodiff/Linalg/index.html | 2 +- .../Algodiff/Mat/index.html | 2 +- .../Algodiff/Maths/index.html | 2 +- .../Algodiff/NN/index.html | 2 +- .../argument-1-Optimise/Algodiff/index.html | 2 +- .../Make/argument-1-Optimise/Batch/index.html | 2 +- .../argument-1-Optimise/Checkpoint/index.html | 2 +- .../argument-1-Optimise/Clipping/index.html | 2 +- .../argument-1-Optimise/Gradient/index.html | 2 +- .../Learning_Rate/index.html | 2 +- .../Make/argument-1-Optimise/Loss/index.html | 2 +- .../argument-1-Optimise/Momentum/index.html | 2 +- .../argument-1-Optimise/Params/index.html | 2 +- .../Regularisation/index.html | 2 +- .../argument-1-Optimise/Stopping/index.html | 2 +- .../Make/argument-1-Optimise/Utils/index.html | 2 +- .../Make/argument-1-Optimise/index.html | 4 +- .../Owl_neural_neuron/Make/index.html | 2 +- docs/owl-base/Owl_neural_neuron/index.html | 2 +- .../owl-base/Owl_neural_neuron_sig/index.html | 2 +- .../module-type-Sig/Activation/index.html | 2 +- .../module-type-Sig/Add/index.html | 2 +- .../module-type-Sig/AlphaDropout/index.html | 2 +- .../module-type-Sig/Average/index.html | 2 +- .../module-type-Sig/AvgPool1D/index.html | 2 +- .../module-type-Sig/AvgPool2D/index.html | 2 +- .../module-type-Sig/Concatenate/index.html | 2 +- .../module-type-Sig/Conv1D/index.html | 2 +- .../module-type-Sig/Conv2D/index.html | 2 +- .../module-type-Sig/Conv3D/index.html | 2 +- .../module-type-Sig/DilatedConv1D/index.html | 2 +- .../module-type-Sig/DilatedConv2D/index.html | 2 +- .../module-type-Sig/DilatedConv3D/index.html | 2 +- .../module-type-Sig/Dot/index.html | 2 +- .../module-type-Sig/Dropout/index.html | 2 +- .../module-type-Sig/Embedding/index.html | 2 +- .../module-type-Sig/Flatten/index.html | 2 +- .../module-type-Sig/FullyConnected/index.html | 2 +- .../module-type-Sig/GRU/index.html | 2 +- .../GaussianDropout/index.html | 2 +- .../module-type-Sig/GaussianNoise/index.html | 2 +- .../GlobalAvgPool1D/index.html | 2 +- .../GlobalAvgPool2D/index.html | 2 +- .../GlobalMaxPool1D/index.html | 2 +- .../GlobalMaxPool2D/index.html | 2 +- .../module-type-Sig/Init/index.html | 2 +- .../module-type-Sig/Input/index.html | 2 +- .../module-type-Sig/LSTM/index.html | 2 +- .../module-type-Sig/Lambda/index.html | 2 +- .../module-type-Sig/LambdaArray/index.html | 2 +- .../module-type-Sig/Linear/index.html | 2 +- .../module-type-Sig/LinearNoBias/index.html | 2 +- .../module-type-Sig/Masking/index.html | 2 +- .../module-type-Sig/Max/index.html | 2 +- .../module-type-Sig/MaxPool1D/index.html | 2 +- .../module-type-Sig/MaxPool2D/index.html | 2 +- .../module-type-Sig/Mul/index.html | 2 +- .../module-type-Sig/Normalisation/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Optimise/Algodiff/A/index.html | 2 +- .../Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Optimise/Algodiff/Mat/index.html | 2 +- .../Optimise/Algodiff/Maths/index.html | 2 +- .../Optimise/Algodiff/NN/index.html | 2 +- .../Optimise/Algodiff/index.html | 2 +- .../module-type-Sig/Optimise/Batch/index.html | 2 +- .../Optimise/Checkpoint/index.html | 2 +- .../Optimise/Clipping/index.html | 2 +- .../Optimise/Gradient/index.html | 2 +- .../Optimise/Learning_Rate/index.html | 2 +- .../module-type-Sig/Optimise/Loss/index.html | 2 +- .../Optimise/Momentum/index.html | 2 +- .../Optimise/Params/index.html | 2 +- .../Optimise/Regularisation/index.html | 2 +- .../Optimise/Stopping/index.html | 2 +- .../module-type-Sig/Optimise/Utils/index.html | 2 +- .../module-type-Sig/Optimise/index.html | 4 +- .../module-type-Sig/Padding1D/index.html | 2 +- .../module-type-Sig/Padding2D/index.html | 2 +- .../module-type-Sig/Padding3D/index.html | 2 +- .../module-type-Sig/Recurrent/index.html | 2 +- .../module-type-Sig/Reshape/index.html | 2 +- .../module-type-Sig/Slice/index.html | 2 +- .../TransposeConv1D/index.html | 2 +- .../TransposeConv2D/index.html | 2 +- .../TransposeConv3D/index.html | 2 +- .../module-type-Sig/UpSampling1D/index.html | 2 +- .../module-type-Sig/UpSampling2D/index.html | 2 +- .../module-type-Sig/UpSampling3D/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Make/argument-1-A/index.html | 2 +- .../Owl_numdiff_generic/Make/index.html | 2 +- docs/owl-base/Owl_numdiff_generic/index.html | 2 +- .../Impl/argument-1-A/index.html | 2 +- .../Owl_numdiff_generic_sig/Impl/index.html | 2 +- .../Owl_numdiff_generic_sig/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Make_Basic/argument-1-M/index.html | 2 +- .../Owl_operator/Make_Basic/index.html | 2 +- .../Make_Extend/argument-1-M/index.html | 2 +- .../Owl_operator/Make_Extend/index.html | 2 +- .../Make_Linalg/argument-1-M/index.html | 2 +- .../Owl_operator/Make_Linalg/index.html | 2 +- .../Make_Matrix/argument-1-M/index.html | 2 +- .../Owl_operator/Make_Matrix/index.html | 2 +- .../Make_Ndarray/argument-1-M/index.html | 2 +- .../Owl_operator/Make_Ndarray/index.html | 2 +- docs/owl-base/Owl_operator/index.html | 2 +- .../Make/Batch/index.html | 2 +- .../Make/Checkpoint/index.html | 2 +- .../Make/Clipping/index.html | 2 +- .../Make/Gradient/index.html | 2 +- .../Make/Learning_Rate/index.html | 2 +- .../Owl_optimise_generic/Make/Loss/index.html | 2 +- .../Make/Momentum/index.html | 2 +- .../Make/Params/index.html | 2 +- .../Make/Regularisation/index.html | 2 +- .../Make/Stopping/index.html | 2 +- .../Make/Utils/index.html | 2 +- .../argument-1-Algodiff/A/Linalg/index.html | 2 +- .../Make/argument-1-Algodiff/A/Mat/index.html | 2 +- .../argument-1-Algodiff/A/Scalar/index.html | 2 +- .../Make/argument-1-Algodiff/A/index.html | 2 +- .../Make/argument-1-Algodiff/Arr/index.html | 2 +- .../argument-1-Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../argument-1-Algodiff/Linalg/index.html | 2 +- .../Make/argument-1-Algodiff/Mat/index.html | 2 +- .../Make/argument-1-Algodiff/Maths/index.html | 2 +- .../Make/argument-1-Algodiff/NN/index.html | 2 +- .../Make/argument-1-Algodiff/index.html | 2 +- .../Owl_optimise_generic/Make/index.html | 2 +- docs/owl-base/Owl_optimise_generic/index.html | 2 +- .../Owl_optimise_generic_sig/index.html | 2 +- .../Algodiff/A/Linalg/index.html | 2 +- .../module-type-Sig/Algodiff/A/Mat/index.html | 2 +- .../Algodiff/A/Scalar/index.html | 2 +- .../module-type-Sig/Algodiff/A/index.html | 2 +- .../module-type-Sig/Algodiff/Arr/index.html | 2 +- .../Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Algodiff/Linalg/index.html | 2 +- .../module-type-Sig/Algodiff/Mat/index.html | 2 +- .../module-type-Sig/Algodiff/Maths/index.html | 2 +- .../module-type-Sig/Algodiff/NN/index.html | 2 +- .../module-type-Sig/Algodiff/index.html | 2 +- .../module-type-Sig/Batch/index.html | 2 +- .../module-type-Sig/Checkpoint/index.html | 2 +- .../module-type-Sig/Clipping/index.html | 2 +- .../module-type-Sig/Gradient/index.html | 2 +- .../module-type-Sig/Learning_Rate/index.html | 2 +- .../module-type-Sig/Loss/index.html | 2 +- .../module-type-Sig/Momentum/index.html | 2 +- .../module-type-Sig/Params/index.html | 2 +- .../module-type-Sig/Regularisation/index.html | 2 +- .../module-type-Sig/Stopping/index.html | 2 +- .../module-type-Sig/Utils/index.html | 2 +- .../module-type-Sig/index.html | 4 +- docs/owl-base/Owl_pretty/index.html | 2 +- docs/owl-base/Owl_types/index.html | 2 +- .../A/Linalg/index.html | 2 +- .../A/Mat/index.html | 2 +- .../A/Scalar/index.html | 2 +- .../A/index.html | 2 +- .../module-type-Computation_Device/index.html | 2 +- .../Linalg/index.html | 2 +- .../Mat/index.html | 2 +- .../Scalar/index.html | 2 +- .../module-type-Ndarray_Algodiff/index.html | 2 +- .../module-type-Ndarray_Basic/index.html | 2 +- .../module-type-Ndarray_Compare/index.html | 2 +- .../Linalg/index.html | 2 +- .../Mat/index.html | 2 +- .../Scalar/index.html | 2 +- .../module-type-Ndarray_Mutable/index.html | 2 +- .../module-type-Ndarray_Numdiff/index.html | 2 +- .../module-type-Stats_Dist/Linalg/index.html | 2 +- .../module-type-Stats_Dist/Mat/index.html | 2 +- .../module-type-Stats_Dist/Scalar/index.html | 2 +- .../module-type-Stats_Dist/index.html | 2 +- docs/owl-base/Owl_types_common/index.html | 2 +- .../Owl_types_computation_device/index.html | 2 +- .../module-type-Sig/A/Linalg/index.html | 2 +- .../module-type-Sig/A/Mat/index.html | 2 +- .../module-type-Sig/A/Scalar/index.html | 2 +- .../module-type-Sig/A/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Owl_types_computation_engine/index.html | 2 +- .../Optimiser/Operator/Linalg/index.html | 16 +- .../Graph/Optimiser/Operator/Mat/index.html | 2 +- .../Optimiser/Operator/Scalar/index.html | 14 +- .../Shape/Type/Device/A/Linalg/index.html | 2 +- .../Symbol/Shape/Type/Device/A/Mat/index.html | 2 +- .../Shape/Type/Device/A/Scalar/index.html | 2 +- .../Symbol/Shape/Type/Device/A/index.html | 2 +- .../Symbol/Shape/Type/Device/index.html | 2 +- .../Operator/Symbol/Shape/Type/index.html | 2 +- .../Operator/Symbol/Shape/index.html | 2 +- .../Optimiser/Operator/Symbol/index.html | 2 +- .../Graph/Optimiser/Operator/index.html | 200 ++++++++--------- .../Graph/Optimiser/index.html | 2 +- .../module-type-Sig/Graph/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../owl-base/Owl_types_maths_basic/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Owl_types_ndarray_algodiff/index.html | 2 +- .../module-type-Sig/Linalg/index.html | 2 +- .../module-type-Sig/Mat/index.html | 2 +- .../module-type-Sig/Scalar/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Owl_types_ndarray_basic/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Owl_types_ndarray_compare/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Owl_types_ndarray_eltcmp/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Owl_types_ndarray_mutable/index.html | 2 +- .../module-type-Sig/Linalg/index.html | 2 +- .../module-type-Sig/Mat/index.html | 2 +- .../module-type-Sig/Scalar/index.html | 2 +- .../module-type-Sig/index.html | 2 +- .../Owl_types_ndarray_numdiff/index.html | 2 +- .../module-type-Sig/index.html | 2 +- docs/owl-base/Owl_types_operator/index.html | 2 +- .../module-type-BasicSig/index.html | 2 +- .../module-type-ExtendSig/index.html | 2 +- .../module-type-LinalgSig/index.html | 2 +- .../module-type-MatrixSig/index.html | 2 +- .../module-type-NdarraySig/index.html | 2 +- .../owl-base/Owl_types_stats_basic/index.html | 2 +- docs/owl-base/Owl_types_stats_dist/index.html | 2 +- .../module-type-Sig/Linalg/index.html | 2 +- .../module-type-Sig/Mat/index.html | 2 +- .../module-type-Sig/Scalar/index.html | 2 +- .../module-type-Sig/index.html | 2 +- docs/owl-base/Owl_utils/index.html | 2 +- docs/owl-base/Owl_utils_array/index.html | 2 +- docs/owl-base/Owl_utils_heap/index.html | 2 +- .../owl-base/Owl_utils_infer_shape/index.html | 2 +- .../Owl_utils_multimap/Make/index.html | 2 +- docs/owl-base/Owl_utils_multimap/index.html | 2 +- docs/owl-base/Owl_utils_ndarray/index.html | 2 +- docs/owl-base/Owl_utils_stack/index.html | 2 +- .../Owl_view/Make/argument-1-A/index.html | 2 +- docs/owl-base/Owl_view/Make/index.html | 2 +- docs/owl-base/Owl_view/index.html | 2 +- docs/owl-base/index.html | 2 +- docs/owl-top/Owl_top/index.html | 2 +- docs/owl-top/index.html | 2 +- docs/owl/Owl/Arr/index.html | 2 +- docs/owl/Owl/Mat/index.html | 2 +- docs/owl/Owl/index.html | 2 +- docs/owl/Owl_algodiff/D/A/Linalg/index.html | 2 +- docs/owl/Owl_algodiff/D/A/Mat/index.html | 2 +- docs/owl/Owl_algodiff/D/A/Scalar/index.html | 2 +- docs/owl/Owl_algodiff/D/A/index.html | 2 +- docs/owl/Owl_algodiff/D/Arr/index.html | 2 +- docs/owl/Owl_algodiff/D/Builder/index.html | 2 +- .../D/Builder/module-type-Aiso/index.html | 2 +- .../D/Builder/module-type-Piso/index.html | 2 +- .../D/Builder/module-type-Siao/index.html | 2 +- .../D/Builder/module-type-Sipo/index.html | 2 +- .../D/Builder/module-type-Siso/index.html | 2 +- .../D/Builder/module-type-Sito/index.html | 2 +- docs/owl/Owl_algodiff/D/Linalg/index.html | 2 +- docs/owl/Owl_algodiff/D/Mat/index.html | 2 +- docs/owl/Owl_algodiff/D/Maths/index.html | 2 +- docs/owl/Owl_algodiff/D/NN/index.html | 2 +- docs/owl/Owl_algodiff/D/index.html | 2 +- docs/owl/Owl_algodiff/S/A/Linalg/index.html | 2 +- docs/owl/Owl_algodiff/S/A/Mat/index.html | 2 +- docs/owl/Owl_algodiff/S/A/Scalar/index.html | 2 +- docs/owl/Owl_algodiff/S/A/index.html | 2 +- docs/owl/Owl_algodiff/S/Arr/index.html | 2 +- docs/owl/Owl_algodiff/S/Builder/index.html | 2 +- .../S/Builder/module-type-Aiso/index.html | 2 +- .../S/Builder/module-type-Piso/index.html | 2 +- .../S/Builder/module-type-Siao/index.html | 2 +- .../S/Builder/module-type-Sipo/index.html | 2 +- .../S/Builder/module-type-Siso/index.html | 2 +- .../S/Builder/module-type-Sito/index.html | 2 +- docs/owl/Owl_algodiff/S/Linalg/index.html | 2 +- docs/owl/Owl_algodiff/S/Mat/index.html | 2 +- docs/owl/Owl_algodiff/S/Maths/index.html | 2 +- docs/owl/Owl_algodiff/S/NN/index.html | 2 +- docs/owl/Owl_algodiff/S/index.html | 2 +- docs/owl/Owl_algodiff/index.html | 2 +- .../D/Linalg/index.html | 2 +- .../Owl_algodiff_primal_ops/D/Mat/index.html | 2 +- docs/owl/Owl_algodiff_primal_ops/D/index.html | 2 +- .../S/Linalg/index.html | 2 +- .../Owl_algodiff_primal_ops/S/Mat/index.html | 2 +- docs/owl/Owl_algodiff_primal_ops/S/index.html | 2 +- docs/owl/Owl_algodiff_primal_ops/index.html | 2 +- docs/owl/Owl_cblas/index.html | 2 +- docs/owl/Owl_cblas_basic/index.html | 2 +- docs/owl/Owl_cblas_generated/index.html | 2 +- docs/owl/Owl_cluster/index.html | 2 +- docs/owl/Owl_core_types/index.html | 2 +- docs/owl/Owl_dataset/index.html | 2 +- docs/owl/Owl_dense/index.html | 2 +- docs/owl/Owl_dense_matrix/C/index.html | 2 +- docs/owl/Owl_dense_matrix/D/index.html | 2 +- docs/owl/Owl_dense_matrix/Generic/index.html | 4 +- docs/owl/Owl_dense_matrix/Operator/index.html | 2 +- docs/owl/Owl_dense_matrix/S/index.html | 2 +- docs/owl/Owl_dense_matrix/Z/index.html | 2 +- docs/owl/Owl_dense_matrix/index.html | 2 +- docs/owl/Owl_dense_matrix_c/index.html | 2 +- docs/owl/Owl_dense_matrix_d/index.html | 2 +- docs/owl/Owl_dense_matrix_generic/index.html | 4 +- docs/owl/Owl_dense_matrix_intf/index.html | 2 +- .../module-type-Common/index.html | 2 +- .../module-type-Complex/index.html | 2 +- .../module-type-Real/index.html | 2 +- docs/owl/Owl_dense_matrix_s/index.html | 2 +- docs/owl/Owl_dense_matrix_z/index.html | 2 +- docs/owl/Owl_dense_ndarray/Any/index.html | 2 +- docs/owl/Owl_dense_ndarray/C/index.html | 2 +- docs/owl/Owl_dense_ndarray/D/index.html | 2 +- docs/owl/Owl_dense_ndarray/Generic/index.html | 198 +++++++++-------- .../owl/Owl_dense_ndarray/Operator/index.html | 2 +- docs/owl/Owl_dense_ndarray/S/index.html | 2 +- docs/owl/Owl_dense_ndarray/Z/index.html | 2 +- docs/owl/Owl_dense_ndarray/index.html | 19 +- docs/owl/Owl_dense_ndarray_a/index.html | 2 +- docs/owl/Owl_dense_ndarray_c/index.html | 2 +- docs/owl/Owl_dense_ndarray_d/index.html | 2 +- docs/owl/Owl_dense_ndarray_generic/index.html | 198 +++++++++-------- docs/owl/Owl_dense_ndarray_intf/index.html | 2 +- .../module-type-Common/index.html | 2 +- .../module-type-Complex/index.html | 2 +- .../module-type-Distribution/index.html | 2 +- .../module-type-NN/index.html | 2 +- .../module-type-Real/index.html | 2 +- docs/owl/Owl_dense_ndarray_s/index.html | 2 +- docs/owl/Owl_dense_ndarray_z/index.html | 2 +- .../owl/Owl_distribution/Make/Beta/index.html | 2 +- .../Owl_distribution/Make/Cauchy/index.html | 2 +- .../owl/Owl_distribution/Make/Chi2/index.html | 2 +- .../Make/Exponential/index.html | 2 +- docs/owl/Owl_distribution/Make/F/index.html | 2 +- .../Owl_distribution/Make/Gamma/index.html | 2 +- .../Owl_distribution/Make/Gaussian/index.html | 2 +- .../Owl_distribution/Make/Gumbel1/index.html | 2 +- .../Owl_distribution/Make/Gumbel2/index.html | 2 +- .../Owl_distribution/Make/Laplace/index.html | 2 +- .../Owl_distribution/Make/Logistic/index.html | 2 +- .../Make/Lognormal/index.html | 2 +- .../Owl_distribution/Make/Lomax/index.html | 2 +- .../Owl_distribution/Make/Poisson/index.html | 2 +- .../Owl_distribution/Make/Rayleigh/index.html | 2 +- .../Owl_distribution/Make/Uniform/index.html | 2 +- .../Owl_distribution/Make/Weibull/index.html | 2 +- .../Make/argument-1-A/Linalg/index.html | 2 +- .../Make/argument-1-A/Mat/index.html | 2 +- .../Make/argument-1-A/Scalar/index.html | 2 +- .../Make/argument-1-A/index.html | 2 +- docs/owl/Owl_distribution/Make/index.html | 2 +- docs/owl/Owl_distribution/index.html | 2 +- docs/owl/Owl_distribution_common/index.html | 2 +- docs/owl/Owl_distribution_generic/index.html | 2 +- docs/owl/Owl_fft/D/index.html | 2 +- docs/owl/Owl_fft/Generic/index.html | 2 +- docs/owl/Owl_fft/S/index.html | 2 +- docs/owl/Owl_fft/index.html | 2 +- docs/owl/Owl_fft_d/index.html | 2 +- docs/owl/Owl_fft_generic/index.html | 2 +- docs/owl/Owl_fft_s/index.html | 2 +- docs/owl/Owl_fftpack/index.html | 2 +- docs/owl/Owl_lapacke/index.html | 2 +- docs/owl/Owl_lapacke_generated/index.html | 2 +- docs/owl/Owl_linalg/C/index.html | 2 +- docs/owl/Owl_linalg/D/index.html | 2 +- docs/owl/Owl_linalg/Generic/index.html | 47 ++-- docs/owl/Owl_linalg/S/index.html | 2 +- docs/owl/Owl_linalg/Z/index.html | 2 +- docs/owl/Owl_linalg/index.html | 19 +- docs/owl/Owl_linalg_c/index.html | 2 +- docs/owl/Owl_linalg_d/index.html | 2 +- docs/owl/Owl_linalg_generic/index.html | 47 ++-- docs/owl/Owl_linalg_intf/index.html | 2 +- .../module-type-Common/index.html | 2 +- .../module-type-Real/index.html | 2 +- docs/owl/Owl_linalg_s/index.html | 2 +- docs/owl/Owl_linalg_z/index.html | 2 +- docs/owl/Owl_maths/index.html | 7 +- docs/owl/Owl_maths_special/index.html | 2 +- docs/owl/Owl_matrix/index.html | 2 +- docs/owl/Owl_matrix_check/index.html | 2 +- docs/owl/Owl_matrix_swap/index.html | 2 +- docs/owl/Owl_ndarray/index.html | 2 +- docs/owl/Owl_ndarray_contract/index.html | 2 +- docs/owl/Owl_ndarray_conv/index.html | 2 +- docs/owl/Owl_ndarray_fma/index.html | 2 +- docs/owl/Owl_ndarray_maths/index.html | 2 +- docs/owl/Owl_ndarray_pool/index.html | 2 +- docs/owl/Owl_ndarray_repeat/index.html | 2 +- docs/owl/Owl_ndarray_slide/index.html | 2 +- docs/owl/Owl_ndarray_sort/index.html | 2 +- docs/owl/Owl_ndarray_transpose/index.html | 2 +- docs/owl/Owl_ndarray_upsampling/index.html | 2 +- docs/owl/Owl_ndarray_utils/index.html | 2 +- .../D/Graph/Neuron/Activation/index.html | 2 +- .../Owl_neural/D/Graph/Neuron/Add/index.html | 2 +- .../D/Graph/Neuron/AlphaDropout/index.html | 2 +- .../D/Graph/Neuron/Average/index.html | 2 +- .../D/Graph/Neuron/AvgPool1D/index.html | 2 +- .../D/Graph/Neuron/AvgPool2D/index.html | 2 +- .../D/Graph/Neuron/Concatenate/index.html | 2 +- .../D/Graph/Neuron/Conv1D/index.html | 2 +- .../D/Graph/Neuron/Conv2D/index.html | 2 +- .../D/Graph/Neuron/Conv3D/index.html | 2 +- .../D/Graph/Neuron/DilatedConv1D/index.html | 2 +- .../D/Graph/Neuron/DilatedConv2D/index.html | 2 +- .../D/Graph/Neuron/DilatedConv3D/index.html | 2 +- .../Owl_neural/D/Graph/Neuron/Dot/index.html | 2 +- .../D/Graph/Neuron/Dropout/index.html | 2 +- .../D/Graph/Neuron/Embedding/index.html | 2 +- .../D/Graph/Neuron/Flatten/index.html | 2 +- .../D/Graph/Neuron/FullyConnected/index.html | 2 +- .../Owl_neural/D/Graph/Neuron/GRU/index.html | 2 +- .../D/Graph/Neuron/GaussianDropout/index.html | 2 +- .../D/Graph/Neuron/GaussianNoise/index.html | 2 +- .../D/Graph/Neuron/GlobalAvgPool1D/index.html | 2 +- .../D/Graph/Neuron/GlobalAvgPool2D/index.html | 2 +- .../D/Graph/Neuron/GlobalMaxPool1D/index.html | 2 +- .../D/Graph/Neuron/GlobalMaxPool2D/index.html | 2 +- .../Owl_neural/D/Graph/Neuron/Init/index.html | 2 +- .../D/Graph/Neuron/Input/index.html | 2 +- .../Owl_neural/D/Graph/Neuron/LSTM/index.html | 2 +- .../D/Graph/Neuron/Lambda/index.html | 2 +- .../D/Graph/Neuron/LambdaArray/index.html | 2 +- .../D/Graph/Neuron/Linear/index.html | 2 +- .../D/Graph/Neuron/LinearNoBias/index.html | 2 +- .../D/Graph/Neuron/Masking/index.html | 2 +- .../Owl_neural/D/Graph/Neuron/Max/index.html | 2 +- .../D/Graph/Neuron/MaxPool1D/index.html | 2 +- .../D/Graph/Neuron/MaxPool2D/index.html | 2 +- .../Owl_neural/D/Graph/Neuron/Mul/index.html | 2 +- .../D/Graph/Neuron/Normalisation/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/index.html | 2 +- .../Neuron/Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/Mat/index.html | 2 +- .../Neuron/Optimise/Algodiff/Maths/index.html | 2 +- .../Neuron/Optimise/Algodiff/NN/index.html | 2 +- .../Graph/Neuron/Optimise/Algodiff/index.html | 2 +- .../D/Graph/Neuron/Optimise/Batch/index.html | 2 +- .../Neuron/Optimise/Checkpoint/index.html | 2 +- .../Graph/Neuron/Optimise/Clipping/index.html | 2 +- .../Graph/Neuron/Optimise/Gradient/index.html | 2 +- .../Neuron/Optimise/Learning_Rate/index.html | 2 +- .../D/Graph/Neuron/Optimise/Loss/index.html | 2 +- .../Graph/Neuron/Optimise/Momentum/index.html | 2 +- .../D/Graph/Neuron/Optimise/Params/index.html | 2 +- .../Neuron/Optimise/Regularisation/index.html | 2 +- .../Graph/Neuron/Optimise/Stopping/index.html | 2 +- .../D/Graph/Neuron/Optimise/Utils/index.html | 2 +- .../D/Graph/Neuron/Optimise/index.html | 2 +- .../D/Graph/Neuron/Padding1D/index.html | 2 +- .../D/Graph/Neuron/Padding2D/index.html | 2 +- .../D/Graph/Neuron/Padding3D/index.html | 2 +- .../D/Graph/Neuron/Recurrent/index.html | 2 +- .../D/Graph/Neuron/Reshape/index.html | 2 +- .../D/Graph/Neuron/Slice/index.html | 2 +- .../D/Graph/Neuron/TransposeConv1D/index.html | 2 +- .../D/Graph/Neuron/TransposeConv2D/index.html | 2 +- .../D/Graph/Neuron/TransposeConv3D/index.html | 2 +- .../D/Graph/Neuron/UpSampling1D/index.html | 2 +- .../D/Graph/Neuron/UpSampling2D/index.html | 2 +- .../D/Graph/Neuron/UpSampling3D/index.html | 2 +- docs/owl/Owl_neural/D/Graph/Neuron/index.html | 2 +- docs/owl/Owl_neural/D/Graph/index.html | 2 +- docs/owl/Owl_neural/D/index.html | 2 +- .../S/Graph/Neuron/Activation/index.html | 2 +- .../Owl_neural/S/Graph/Neuron/Add/index.html | 2 +- .../S/Graph/Neuron/AlphaDropout/index.html | 2 +- .../S/Graph/Neuron/Average/index.html | 2 +- .../S/Graph/Neuron/AvgPool1D/index.html | 2 +- .../S/Graph/Neuron/AvgPool2D/index.html | 2 +- .../S/Graph/Neuron/Concatenate/index.html | 2 +- .../S/Graph/Neuron/Conv1D/index.html | 2 +- .../S/Graph/Neuron/Conv2D/index.html | 2 +- .../S/Graph/Neuron/Conv3D/index.html | 2 +- .../S/Graph/Neuron/DilatedConv1D/index.html | 2 +- .../S/Graph/Neuron/DilatedConv2D/index.html | 2 +- .../S/Graph/Neuron/DilatedConv3D/index.html | 2 +- .../Owl_neural/S/Graph/Neuron/Dot/index.html | 2 +- .../S/Graph/Neuron/Dropout/index.html | 2 +- .../S/Graph/Neuron/Embedding/index.html | 2 +- .../S/Graph/Neuron/Flatten/index.html | 2 +- .../S/Graph/Neuron/FullyConnected/index.html | 2 +- .../Owl_neural/S/Graph/Neuron/GRU/index.html | 2 +- .../S/Graph/Neuron/GaussianDropout/index.html | 2 +- .../S/Graph/Neuron/GaussianNoise/index.html | 2 +- .../S/Graph/Neuron/GlobalAvgPool1D/index.html | 2 +- .../S/Graph/Neuron/GlobalAvgPool2D/index.html | 2 +- .../S/Graph/Neuron/GlobalMaxPool1D/index.html | 2 +- .../S/Graph/Neuron/GlobalMaxPool2D/index.html | 2 +- .../Owl_neural/S/Graph/Neuron/Init/index.html | 2 +- .../S/Graph/Neuron/Input/index.html | 2 +- .../Owl_neural/S/Graph/Neuron/LSTM/index.html | 2 +- .../S/Graph/Neuron/Lambda/index.html | 2 +- .../S/Graph/Neuron/LambdaArray/index.html | 2 +- .../S/Graph/Neuron/Linear/index.html | 2 +- .../S/Graph/Neuron/LinearNoBias/index.html | 2 +- .../S/Graph/Neuron/Masking/index.html | 2 +- .../Owl_neural/S/Graph/Neuron/Max/index.html | 2 +- .../S/Graph/Neuron/MaxPool1D/index.html | 2 +- .../S/Graph/Neuron/MaxPool2D/index.html | 2 +- .../Owl_neural/S/Graph/Neuron/Mul/index.html | 2 +- .../S/Graph/Neuron/Normalisation/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Neuron/Optimise/Algodiff/A/index.html | 2 +- .../Neuron/Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Neuron/Optimise/Algodiff/Mat/index.html | 2 +- .../Neuron/Optimise/Algodiff/Maths/index.html | 2 +- .../Neuron/Optimise/Algodiff/NN/index.html | 2 +- .../Graph/Neuron/Optimise/Algodiff/index.html | 2 +- .../S/Graph/Neuron/Optimise/Batch/index.html | 2 +- .../Neuron/Optimise/Checkpoint/index.html | 2 +- .../Graph/Neuron/Optimise/Clipping/index.html | 2 +- .../Graph/Neuron/Optimise/Gradient/index.html | 2 +- .../Neuron/Optimise/Learning_Rate/index.html | 2 +- .../S/Graph/Neuron/Optimise/Loss/index.html | 2 +- .../Graph/Neuron/Optimise/Momentum/index.html | 2 +- .../S/Graph/Neuron/Optimise/Params/index.html | 2 +- .../Neuron/Optimise/Regularisation/index.html | 2 +- .../Graph/Neuron/Optimise/Stopping/index.html | 2 +- .../S/Graph/Neuron/Optimise/Utils/index.html | 2 +- .../S/Graph/Neuron/Optimise/index.html | 2 +- .../S/Graph/Neuron/Padding1D/index.html | 2 +- .../S/Graph/Neuron/Padding2D/index.html | 2 +- .../S/Graph/Neuron/Padding3D/index.html | 2 +- .../S/Graph/Neuron/Recurrent/index.html | 2 +- .../S/Graph/Neuron/Reshape/index.html | 2 +- .../S/Graph/Neuron/Slice/index.html | 2 +- .../S/Graph/Neuron/TransposeConv1D/index.html | 2 +- .../S/Graph/Neuron/TransposeConv2D/index.html | 2 +- .../S/Graph/Neuron/TransposeConv3D/index.html | 2 +- .../S/Graph/Neuron/UpSampling1D/index.html | 2 +- .../S/Graph/Neuron/UpSampling2D/index.html | 2 +- .../S/Graph/Neuron/UpSampling3D/index.html | 2 +- docs/owl/Owl_neural/S/Graph/Neuron/index.html | 2 +- docs/owl/Owl_neural/S/Graph/index.html | 2 +- docs/owl/Owl_neural/S/index.html | 2 +- docs/owl/Owl_neural/index.html | 2 +- .../Make/argument-1-M/index.html | 2 +- .../Make/argument-2-E/index.html | 2 +- docs/owl/Owl_neural_parallel/Make/index.html | 2 +- docs/owl/Owl_neural_parallel/index.html | 2 +- .../module-type-EngineSig/index.html | 2 +- .../module-type-ModelSig/index.html | 2 +- docs/owl/Owl_nlp/index.html | 2 +- docs/owl/Owl_nlp_corpus/index.html | 2 +- docs/owl/Owl_nlp_lda/index.html | 2 +- docs/owl/Owl_nlp_similarity/index.html | 2 +- docs/owl/Owl_nlp_tfidf/index.html | 2 +- docs/owl/Owl_nlp_utils/index.html | 2 +- docs/owl/Owl_nlp_vocabulary/index.html | 2 +- .../D/Algodiff/A/Linalg/index.html | 2 +- .../Owl_optimise/D/Algodiff/A/Mat/index.html | 2 +- .../D/Algodiff/A/Scalar/index.html | 2 +- docs/owl/Owl_optimise/D/Algodiff/A/index.html | 2 +- .../Owl_optimise/D/Algodiff/Arr/index.html | 2 +- .../D/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Owl_optimise/D/Algodiff/Linalg/index.html | 2 +- .../Owl_optimise/D/Algodiff/Mat/index.html | 2 +- .../Owl_optimise/D/Algodiff/Maths/index.html | 2 +- .../owl/Owl_optimise/D/Algodiff/NN/index.html | 2 +- docs/owl/Owl_optimise/D/Algodiff/index.html | 2 +- docs/owl/Owl_optimise/D/Batch/index.html | 2 +- docs/owl/Owl_optimise/D/Checkpoint/index.html | 2 +- docs/owl/Owl_optimise/D/Clipping/index.html | 2 +- docs/owl/Owl_optimise/D/Gradient/index.html | 2 +- .../Owl_optimise/D/Learning_Rate/index.html | 2 +- docs/owl/Owl_optimise/D/Loss/index.html | 2 +- docs/owl/Owl_optimise/D/Momentum/index.html | 2 +- docs/owl/Owl_optimise/D/Params/index.html | 2 +- .../Owl_optimise/D/Regularisation/index.html | 2 +- docs/owl/Owl_optimise/D/Stopping/index.html | 2 +- docs/owl/Owl_optimise/D/Utils/index.html | 2 +- docs/owl/Owl_optimise/D/index.html | 2 +- .../Algodiff/A/Linalg/index.html | 2 +- .../Make_Embedded/Algodiff/A/Mat/index.html | 2 +- .../Algodiff/A/Scalar/index.html | 2 +- .../Make_Embedded/Algodiff/A/index.html | 2 +- .../Make_Embedded/Algodiff/Arr/index.html | 2 +- .../Make_Embedded/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Make_Embedded/Algodiff/Linalg/index.html | 2 +- .../Make_Embedded/Algodiff/Mat/index.html | 2 +- .../Make_Embedded/Algodiff/Maths/index.html | 2 +- .../Make_Embedded/Algodiff/NN/index.html | 2 +- .../Make_Embedded/Algodiff/index.html | 2 +- .../Make_Embedded/Batch/index.html | 2 +- .../Make_Embedded/Checkpoint/index.html | 2 +- .../Make_Embedded/Clipping/index.html | 2 +- .../Make_Embedded/Gradient/index.html | 2 +- .../Make_Embedded/Learning_Rate/index.html | 2 +- .../Make_Embedded/Loss/index.html | 2 +- .../Make_Embedded/Momentum/index.html | 2 +- .../Make_Embedded/Params/index.html | 2 +- .../Make_Embedded/Regularisation/index.html | 2 +- .../Make_Embedded/Stopping/index.html | 2 +- .../Make_Embedded/Utils/index.html | 2 +- .../argument-1-A/Linalg/index.html | 2 +- .../Make_Embedded/argument-1-A/Mat/index.html | 2 +- .../argument-1-A/Scalar/index.html | 2 +- .../Make_Embedded/argument-1-A/index.html | 2 +- .../owl/Owl_optimise/Make_Embedded/index.html | 2 +- .../S/Algodiff/A/Linalg/index.html | 2 +- .../Owl_optimise/S/Algodiff/A/Mat/index.html | 2 +- .../S/Algodiff/A/Scalar/index.html | 2 +- docs/owl/Owl_optimise/S/Algodiff/A/index.html | 2 +- .../Owl_optimise/S/Algodiff/Arr/index.html | 2 +- .../S/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Owl_optimise/S/Algodiff/Linalg/index.html | 2 +- .../Owl_optimise/S/Algodiff/Mat/index.html | 2 +- .../Owl_optimise/S/Algodiff/Maths/index.html | 2 +- .../owl/Owl_optimise/S/Algodiff/NN/index.html | 2 +- docs/owl/Owl_optimise/S/Algodiff/index.html | 2 +- docs/owl/Owl_optimise/S/Batch/index.html | 2 +- docs/owl/Owl_optimise/S/Checkpoint/index.html | 2 +- docs/owl/Owl_optimise/S/Clipping/index.html | 2 +- docs/owl/Owl_optimise/S/Gradient/index.html | 2 +- .../Owl_optimise/S/Learning_Rate/index.html | 2 +- docs/owl/Owl_optimise/S/Loss/index.html | 2 +- docs/owl/Owl_optimise/S/Momentum/index.html | 2 +- docs/owl/Owl_optimise/S/Params/index.html | 2 +- .../Owl_optimise/S/Regularisation/index.html | 2 +- docs/owl/Owl_optimise/S/Stopping/index.html | 2 +- docs/owl/Owl_optimise/S/Utils/index.html | 2 +- docs/owl/Owl_optimise/S/index.html | 2 +- docs/owl/Owl_optimise/index.html | 2 +- .../D/Optimise/Algodiff/A/Linalg/index.html | 2 +- .../D/Optimise/Algodiff/A/Mat/index.html | 2 +- .../D/Optimise/Algodiff/A/Scalar/index.html | 2 +- .../D/Optimise/Algodiff/A/index.html | 2 +- .../D/Optimise/Algodiff/Arr/index.html | 2 +- .../D/Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../D/Optimise/Algodiff/Linalg/index.html | 2 +- .../D/Optimise/Algodiff/Mat/index.html | 2 +- .../D/Optimise/Algodiff/Maths/index.html | 2 +- .../D/Optimise/Algodiff/NN/index.html | 2 +- .../D/Optimise/Algodiff/index.html | 2 +- .../D/Optimise/Batch/index.html | 2 +- .../D/Optimise/Checkpoint/index.html | 2 +- .../D/Optimise/Clipping/index.html | 2 +- .../D/Optimise/Gradient/index.html | 2 +- .../D/Optimise/Learning_Rate/index.html | 2 +- .../Owl_regression/D/Optimise/Loss/index.html | 2 +- .../D/Optimise/Momentum/index.html | 2 +- .../D/Optimise/Params/index.html | 2 +- .../D/Optimise/Regularisation/index.html | 2 +- .../D/Optimise/Stopping/index.html | 2 +- .../D/Optimise/Utils/index.html | 2 +- docs/owl/Owl_regression/D/Optimise/index.html | 2 +- docs/owl/Owl_regression/D/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Optimise/Algodiff/A/index.html | 2 +- .../Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Optimise/Algodiff/Mat/index.html | 2 +- .../Optimise/Algodiff/Maths/index.html | 2 +- .../Optimise/Algodiff/NN/index.html | 2 +- .../Optimise/Algodiff/index.html | 2 +- .../Make_Embedded/Optimise/Batch/index.html | 2 +- .../Optimise/Checkpoint/index.html | 2 +- .../Optimise/Clipping/index.html | 2 +- .../Optimise/Gradient/index.html | 2 +- .../Optimise/Learning_Rate/index.html | 2 +- .../Make_Embedded/Optimise/Loss/index.html | 2 +- .../Optimise/Momentum/index.html | 2 +- .../Make_Embedded/Optimise/Params/index.html | 2 +- .../Optimise/Regularisation/index.html | 2 +- .../Optimise/Stopping/index.html | 2 +- .../Make_Embedded/Optimise/Utils/index.html | 2 +- .../Make_Embedded/Optimise/index.html | 2 +- .../argument-1-A/Linalg/index.html | 2 +- .../Make_Embedded/argument-1-A/Mat/index.html | 2 +- .../argument-1-A/Scalar/index.html | 2 +- .../Make_Embedded/argument-1-A/index.html | 2 +- .../Owl_regression/Make_Embedded/index.html | 2 +- .../S/Optimise/Algodiff/A/Linalg/index.html | 2 +- .../S/Optimise/Algodiff/A/Mat/index.html | 2 +- .../S/Optimise/Algodiff/A/Scalar/index.html | 2 +- .../S/Optimise/Algodiff/A/index.html | 2 +- .../S/Optimise/Algodiff/Arr/index.html | 2 +- .../S/Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../S/Optimise/Algodiff/Linalg/index.html | 2 +- .../S/Optimise/Algodiff/Mat/index.html | 2 +- .../S/Optimise/Algodiff/Maths/index.html | 2 +- .../S/Optimise/Algodiff/NN/index.html | 2 +- .../S/Optimise/Algodiff/index.html | 2 +- .../S/Optimise/Batch/index.html | 2 +- .../S/Optimise/Checkpoint/index.html | 2 +- .../S/Optimise/Clipping/index.html | 2 +- .../S/Optimise/Gradient/index.html | 2 +- .../S/Optimise/Learning_Rate/index.html | 2 +- .../Owl_regression/S/Optimise/Loss/index.html | 2 +- .../S/Optimise/Momentum/index.html | 2 +- .../S/Optimise/Params/index.html | 2 +- .../S/Optimise/Regularisation/index.html | 2 +- .../S/Optimise/Stopping/index.html | 2 +- .../S/Optimise/Utils/index.html | 2 +- docs/owl/Owl_regression/S/Optimise/index.html | 2 +- docs/owl/Owl_regression/S/index.html | 2 +- docs/owl/Owl_regression/index.html | 2 +- .../Algodiff/A/Linalg/index.html | 2 +- .../Algodiff/A/Mat/index.html | 2 +- .../Algodiff/A/Scalar/index.html | 2 +- .../argument-1-Optimise/Algodiff/A/index.html | 2 +- .../Algodiff/Arr/index.html | 2 +- .../Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Algodiff/Linalg/index.html | 2 +- .../Algodiff/Mat/index.html | 2 +- .../Algodiff/Maths/index.html | 2 +- .../Algodiff/NN/index.html | 2 +- .../argument-1-Optimise/Algodiff/index.html | 2 +- .../Make/argument-1-Optimise/Batch/index.html | 2 +- .../argument-1-Optimise/Checkpoint/index.html | 2 +- .../argument-1-Optimise/Clipping/index.html | 2 +- .../argument-1-Optimise/Gradient/index.html | 2 +- .../Learning_Rate/index.html | 2 +- .../Make/argument-1-Optimise/Loss/index.html | 2 +- .../argument-1-Optimise/Momentum/index.html | 2 +- .../argument-1-Optimise/Params/index.html | 2 +- .../Regularisation/index.html | 2 +- .../argument-1-Optimise/Stopping/index.html | 2 +- .../Make/argument-1-Optimise/Utils/index.html | 2 +- .../Make/argument-1-Optimise/index.html | 4 +- .../Owl_regression_generic/Make/index.html | 2 +- docs/owl/Owl_regression_generic/index.html | 2 +- .../owl/Owl_regression_generic_sig/index.html | 2 +- .../Optimise/Algodiff/A/Linalg/index.html | 2 +- .../Optimise/Algodiff/A/Mat/index.html | 2 +- .../Optimise/Algodiff/A/Scalar/index.html | 2 +- .../Optimise/Algodiff/A/index.html | 2 +- .../Optimise/Algodiff/Arr/index.html | 2 +- .../Optimise/Algodiff/Builder/index.html | 2 +- .../Builder/module-type-Aiso/index.html | 2 +- .../Builder/module-type-Piso/index.html | 2 +- .../Builder/module-type-Siao/index.html | 2 +- .../Builder/module-type-Sipo/index.html | 2 +- .../Builder/module-type-Siso/index.html | 2 +- .../Builder/module-type-Sito/index.html | 2 +- .../Optimise/Algodiff/Linalg/index.html | 2 +- .../Optimise/Algodiff/Mat/index.html | 2 +- .../Optimise/Algodiff/Maths/index.html | 2 +- .../Optimise/Algodiff/NN/index.html | 2 +- .../Optimise/Algodiff/index.html | 2 +- .../module-type-Sig/Optimise/Batch/index.html | 2 +- .../Optimise/Checkpoint/index.html | 2 +- .../Optimise/Clipping/index.html | 2 +- .../Optimise/Gradient/index.html | 2 +- .../Optimise/Learning_Rate/index.html | 2 +- .../module-type-Sig/Optimise/Loss/index.html | 2 +- .../Optimise/Momentum/index.html | 2 +- .../Optimise/Params/index.html | 2 +- .../Optimise/Regularisation/index.html | 2 +- .../Optimise/Stopping/index.html | 2 +- .../module-type-Sig/Optimise/Utils/index.html | 2 +- .../module-type-Sig/Optimise/index.html | 4 +- .../module-type-Sig/index.html | 4 +- docs/owl/Owl_signal/index.html | 2 +- docs/owl/Owl_slicing/index.html | 2 +- docs/owl/Owl_slicing_basic/index.html | 2 +- docs/owl/Owl_slicing_fancy/index.html | 2 +- docs/owl/Owl_stats/index.html | 8 +- docs/owl/Owl_stats_dist/index.html | 2 +- docs/owl/Owl_stats_extend/index.html | 2 +- docs/owl/Owl_stats_prng/index.html | 2 +- docs/owl/Owl_stats_sampler/index.html | 2 +- docs/owl/index.html | 2 +- src/owl/dense/owl_dense_ndarray_generic.mli | 4 +- src/owl/maths/owl_maths.mli | 2 +- 2037 files changed, 4211 insertions(+), 4052 deletions(-) diff --git a/docs/odoc.support/odoc.css b/docs/odoc.support/odoc.css index c23517bac..71148de3d 100644 --- a/docs/odoc.support/odoc.css +++ b/docs/odoc.support/odoc.css @@ -1,7 +1,7 @@ @charset "UTF-8"; /* Copyright (c) 2016 The odoc contributors. All rights reserved. Distributed under the ISC license, see terms at the end of the file. - odoc 2.4.1 */ + odoc 2.4.2 */ /* Fonts */ /* noticia-text-regular - latin */ diff --git a/docs/owl-base/Owl_algodiff_check/Make/Forward/index.html b/docs/owl-base/Owl_algodiff_check/Make/Forward/index.html index 91b783f71..717aba499 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/Forward/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/Forward/index.html @@ -1,5 +1,5 @@ -Forward (owl-base.Owl_algodiff_check.Make.Forward)

Module Make.Forward

val check : +Forward (owl-base.Owl_algodiff_check.Make.Forward)

Module Make.Forward

val check : threshold:float -> f:(AD.t -> AD.t) -> directions:AD.t array -> diff --git a/docs/owl-base/Owl_algodiff_check/Make/Reverse/index.html b/docs/owl-base/Owl_algodiff_check/Make/Reverse/index.html index a6b73deac..f9e51f195 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/Reverse/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/Reverse/index.html @@ -1,5 +1,5 @@ -Reverse (owl-base.Owl_algodiff_check.Make.Reverse)

Module Make.Reverse

val check : +Reverse (owl-base.Owl_algodiff_check.Make.Reverse)

Module Make.Reverse

val check : threshold:float -> order:[ `second | `fourth | `eighth ] -> ?verbose:bool -> diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Linalg/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Linalg/index.html index f52d00826..16e88dcff 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_check.Make.AD.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_check.Make.AD.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Mat/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Mat/index.html index b29d5422e..b6ca942d9 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_check.Make.AD.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_check.Make.AD.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Scalar/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Scalar/index.html index dacc7beb8..0f4ff3910 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_check.Make.AD.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_check.Make.AD.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/index.html index 1b3c04ae4..19c05b3b9 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_check.Make.AD.A)

Module AD.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_check.Make.AD.A)

Module AD.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Arr/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Arr/index.html index 417d8b5ab..9618632b0 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Arr/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_algodiff_check.Make.AD.Arr)

Module AD.Arr

val empty : int array -> t
val zeros : int array -> t
val ones : int array -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
val shape : t -> int array
val numel : t -> int
val reset : t -> unit
val reshape : t -> int array -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
+Arr (owl-base.Owl_algodiff_check.Make.AD.Arr)

Module AD.Arr

val empty : int array -> t
val zeros : int array -> t
val ones : int array -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
val shape : t -> int array
val numel : t -> int
val reset : t -> unit
val reshape : t -> int array -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/index.html index f62314df9..5dde12c28 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_algodiff_check.Make.AD.Builder)

Module AD.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

+Builder (owl-base.Owl_algodiff_check.Make.AD.Builder)

Module AD.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Aiso/index.html index a35afb77e..6b2f47174 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_algodiff_check.Make.AD.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
+Aiso (owl-base.Owl_algodiff_check.Make.AD.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Piso/index.html index 884b39cf4..2c94a3edd 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_algodiff_check.Make.AD.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : A.elt -> A.elt -> t
val ff_ab : A.elt -> A.arr -> t
val ff_ba : A.arr -> A.elt -> t
val ff_bb : A.arr -> A.arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
+Piso (owl-base.Owl_algodiff_check.Make.AD.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : A.elt -> A.elt -> t
val ff_ab : A.elt -> A.arr -> t
val ff_ba : A.arr -> A.elt -> t
val ff_bb : A.arr -> A.arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Siao/index.html index f1914e6f0..2a76d7fa7 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_algodiff_check.Make.AD.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : A.elt -> t array
val ff_arr : A.arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
+Siao (owl-base.Owl_algodiff_check.Make.AD.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : A.elt -> t array
val ff_arr : A.arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Sipo/index.html index 868bbad1e..5483c4909 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_algodiff_check.Make.AD.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : A.elt -> t * t
val ff_arr : A.arr -> t * t
val df : t -> t -> t -> t
val dr : +Sipo (owl-base.Owl_algodiff_check.Make.AD.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : A.elt -> t * t
val ff_arr : A.arr -> t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Siso/index.html index 4ea89d6f0..17da09c81 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_algodiff_check.Make.AD.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : A.elt -> t
val ff_arr : A.arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
+Siso (owl-base.Owl_algodiff_check.Make.AD.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : A.elt -> t
val ff_arr : A.arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Sito/index.html index 883851ff7..91d49d26f 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_algodiff_check.Make.AD.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : A.elt -> t * t * t
val ff_arr : A.arr -> t * t * t
val df : t -> t -> t -> t
val dr : +Sito (owl-base.Owl_algodiff_check.Make.AD.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : A.elt -> t * t * t
val ff_arr : A.arr -> t * t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Linalg/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Linalg/index.html index 8011fbee8..8eb785d8e 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_check.Make.AD.Linalg)

Module AD.Linalg

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> t -> t * t * t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : t -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_check.Make.AD.Linalg)

Module AD.Linalg

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> t -> t * t * t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : t -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Mat/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Mat/index.html index 16643be86..4099ccd0c 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_check.Make.AD.Mat)

Module AD.Mat

val empty : int -> int -> t
val zeros : int -> int -> t
val eye : int -> t
val ones : int -> int -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
val shape : t -> int * int
val numel : t -> int
val row_num : t -> int
val col_num : t -> int
val reset : t -> unit
val reshape : int -> int -> t -> t
val get : t -> int -> int -> t
val set : t -> int -> int -> t -> t
val row : t -> int -> t
val mean : t -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
val map_by_row : (t -> t) -> t -> t
val of_arrays : A.elt array array -> t
val init_2d : int -> int -> (int -> int -> t) -> t
val print : t -> unit
+Mat (owl-base.Owl_algodiff_check.Make.AD.Mat)

Module AD.Mat

val empty : int -> int -> t
val zeros : int -> int -> t
val eye : int -> t
val ones : int -> int -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
val shape : t -> int * int
val numel : t -> int
val row_num : t -> int
val col_num : t -> int
val reset : t -> unit
val reshape : int -> int -> t -> t
val get : t -> int -> int -> t
val set : t -> int -> int -> t -> t
val row : t -> int -> t
val mean : t -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
val map_by_row : (t -> t) -> t -> t
val of_arrays : A.elt array array -> t
val init_2d : int -> int -> (int -> int -> t) -> t
val print : t -> unit
diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Maths/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Maths/index.html index 097f54a4f..c7926b6d2 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Maths/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_algodiff_check.Make.AD.Maths)

Module AD.Maths

val (+) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val add : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val div : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : t -> t -> t

Refer to :doc:`owl_dense_matrix_generic`

val dot : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val round : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : t -> int -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : t -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> t -> t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : t array array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : t -> t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

+Maths (owl-base.Owl_algodiff_check.Make.AD.Maths)

Module AD.Maths

val (+) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val add : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val div : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : t -> t -> t

Refer to :doc:`owl_dense_matrix_generic`

val dot : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val round : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : t -> int -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : t -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> t -> t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : t array array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : t -> t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/NN/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/NN/index.html index e40d1b1d1..64f09effc 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/NN/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_algodiff_check.Make.AD.NN)

Module AD.NN

val dropout : ?rate:float -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dilated_conv1d : +NN (owl-base.Owl_algodiff_check.Make.AD.NN)

Module AD.NN

val dropout : ?rate:float -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/index.html b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/index.html index e94f9bbcd..ef19fbcc3 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/argument-1-AD/index.html @@ -1,5 +1,5 @@ -AD (owl-base.Owl_algodiff_check.Make.AD)

Parameter Make.AD

include Owl_algodiff_core_sig.Sig
Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

val make_forward : t -> t -> int -> t

TODO

val make_reverse : t -> int -> t

TODO

val reverse_prop : t -> t -> unit

TODO

val diff : (t -> t) -> t -> t

diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

val diff' : (t -> t) -> t -> t * t

similar to diff, but return (f x, diff f x).

val grad : (t -> t) -> t -> t

gradient of f : (vector -> scalar) at x, reverse ad.

val grad' : (t -> t) -> t -> t * t

similar to grad, but return (f x, grad f x).

val jacobian : (t -> t) -> t -> t

jacobian of f : (vector -> vector) at x, both x and y are row vectors.

val jacobian' : (t -> t) -> t -> t * t

similar to jacobian, but return (f x, jacobian f x)

val jacobianv : (t -> t) -> t -> t -> t

jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

val jacobianv' : (t -> t) -> t -> t -> t * t

similar to jacobianv', but return (f x, jacobianv f x v)

val jacobianTv : (t -> t) -> t -> t -> t

transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

val jacobianTv' : (t -> t) -> t -> t -> t * t

similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

val hessian : (t -> t) -> t -> t

hessian of f : (scalar -> scalar) at x.

val hessian' : (t -> t) -> t -> t * t

simiarl to hessian, but return (f x, hessian f x)

val hessianv : (t -> t) -> t -> t -> t

hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

val hessianv' : (t -> t) -> t -> t -> t * t

similar to hessianv, but return (f x, hessianv f x v).

val laplacian : (t -> t) -> t -> t

laplacian of f : (scalar -> scalar) at x.

val laplacian' : (t -> t) -> t -> t * t

similar to laplacian, but return (f x, laplacian f x).

val gradhessian : (t -> t) -> t -> t * t

return (grad f x, hessian f x), f : (scalar -> scalar)

val gradhessian' : (t -> t) -> t -> t * t * t

return (f x, grad f x, hessian f x)

val gradhessianv : (t -> t) -> t -> t -> t * t

return (grad f x v, hessian f x v)

val gradhessianv' : (t -> t) -> t -> t -> t * t * t

return (f x, grad f x v, hessian f x v)

include Owl_algodiff_ops_sig.Sig +AD (owl-base.Owl_algodiff_check.Make.AD)

Parameter Make.AD

include Owl_algodiff_core_sig.Sig
Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

val make_forward : t -> t -> int -> t

TODO

val make_reverse : t -> int -> t

TODO

val reverse_prop : t -> t -> unit

TODO

val diff : (t -> t) -> t -> t

diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

val diff' : (t -> t) -> t -> t * t

similar to diff, but return (f x, diff f x).

val grad : (t -> t) -> t -> t

gradient of f : (vector -> scalar) at x, reverse ad.

val grad' : (t -> t) -> t -> t * t

similar to grad, but return (f x, grad f x).

val jacobian : (t -> t) -> t -> t

jacobian of f : (vector -> vector) at x, both x and y are row vectors.

val jacobian' : (t -> t) -> t -> t * t

similar to jacobian, but return (f x, jacobian f x)

val jacobianv : (t -> t) -> t -> t -> t

jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

val jacobianv' : (t -> t) -> t -> t -> t * t

similar to jacobianv', but return (f x, jacobianv f x v)

val jacobianTv : (t -> t) -> t -> t -> t

transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

val jacobianTv' : (t -> t) -> t -> t -> t * t

similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

val hessian : (t -> t) -> t -> t

hessian of f : (scalar -> scalar) at x.

val hessian' : (t -> t) -> t -> t * t

simiarl to hessian, but return (f x, hessian f x)

val hessianv : (t -> t) -> t -> t -> t

hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

val hessianv' : (t -> t) -> t -> t -> t * t

similar to hessianv, but return (f x, hessianv f x v).

val laplacian : (t -> t) -> t -> t

laplacian of f : (scalar -> scalar) at x.

val laplacian' : (t -> t) -> t -> t * t

similar to laplacian, but return (f x, laplacian f x).

val gradhessian : (t -> t) -> t -> t * t

return (grad f x, hessian f x), f : (scalar -> scalar)

val gradhessian' : (t -> t) -> t -> t * t * t

return (f x, grad f x, hessian f x)

val gradhessianv : (t -> t) -> t -> t -> t * t

return (grad f x v, hessian f x v)

val gradhessianv' : (t -> t) -> t -> t -> t * t * t

return (f x, grad f x v, hessian f x v)

include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl-base/Owl_algodiff_check/Make/index.html b/docs/owl-base/Owl_algodiff_check/Make/index.html index 9b46d38c9..38e55b9f4 100644 --- a/docs/owl-base/Owl_algodiff_check/Make/index.html +++ b/docs/owl-base/Owl_algodiff_check/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_algodiff_check.Make)

Module Owl_algodiff_check.Make

Parameters

Signature

val generate_test_samples : (int * int) -> int -> AD.t array * AD.t array
module Reverse : sig ... end
module Forward : sig ... end
+Make (owl-base.Owl_algodiff_check.Make)

Module Owl_algodiff_check.Make

Parameters

Signature

val generate_test_samples : (int * int) -> int -> AD.t array * AD.t array
module Reverse : sig ... end
module Forward : sig ... end
diff --git a/docs/owl-base/Owl_algodiff_check/index.html b/docs/owl-base/Owl_algodiff_check/index.html index 7e1f4aa0d..bf0e31449 100644 --- a/docs/owl-base/Owl_algodiff_check/index.html +++ b/docs/owl-base/Owl_algodiff_check/index.html @@ -1,2 +1,2 @@ -Owl_algodiff_check (owl-base.Owl_algodiff_check)

Module Owl_algodiff_check

module Make (AD : Owl_algodiff_generic_sig.Sig) : sig ... end
+Owl_algodiff_check (owl-base.Owl_algodiff_check)

Module Owl_algodiff_check

module Make (AD : Owl_algodiff_generic_sig.Sig) : sig ... end
diff --git a/docs/owl-base/Owl_algodiff_core/Make/A/Linalg/index.html b/docs/owl-base/Owl_algodiff_core/Make/A/Linalg/index.html index 955d29f42..b9d6ae5b2 100644 --- a/docs/owl-base/Owl_algodiff_core/Make/A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_core/Make/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_core.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_core.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_core/Make/A/Mat/index.html b/docs/owl-base/Owl_algodiff_core/Make/A/Mat/index.html index c3fc50016..fad7b23ee 100644 --- a/docs/owl-base/Owl_algodiff_core/Make/A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_core/Make/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_core.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_core.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_core/Make/A/Scalar/index.html b/docs/owl-base/Owl_algodiff_core/Make/A/Scalar/index.html index cc60a7ce3..874bb9aec 100644 --- a/docs/owl-base/Owl_algodiff_core/Make/A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_core/Make/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_core.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_core.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_core/Make/A/index.html b/docs/owl-base/Owl_algodiff_core/Make/A/index.html index c804808fe..664be14a4 100644 --- a/docs/owl-base/Owl_algodiff_core/Make/A/index.html +++ b/docs/owl-base/Owl_algodiff_core/Make/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_core.Make.A)

Module Make.A

include Owl_types_ndarray_eltcmp.Sig +A (owl-base.Owl_algodiff_core.Make.A)

Module Make.A

include Owl_types_ndarray_eltcmp.Sig with type arr = A.arr with type elt = A.elt
include Owl_types_ndarray_basic.Sig with type arr = A.arr with type elt = A.elt
type arr = A.arr
type elt = A.elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> diff --git a/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Linalg/index.html b/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Linalg/index.html index 955d29f42..b9d6ae5b2 100644 --- a/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_core.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_core.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Mat/index.html b/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Mat/index.html index c3fc50016..fad7b23ee 100644 --- a/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_core.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_core.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Scalar/index.html b/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Scalar/index.html index cc60a7ce3..874bb9aec 100644 --- a/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_core.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_core.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/index.html b/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/index.html index e5fd8d433..f73c6a5b1 100644 --- a/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/index.html +++ b/docs/owl-base/Owl_algodiff_core/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_core.Make.A)

Parameter Make.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_core.Make.A)

Parameter Make.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_core/Make/index.html b/docs/owl-base/Owl_algodiff_core/Make/index.html index 99ee2ed3e..7af95d99e 100644 --- a/docs/owl-base/Owl_algodiff_core/Make/index.html +++ b/docs/owl-base/Owl_algodiff_core/Make/index.html @@ -1,3 +1,3 @@ -Make (owl-base.Owl_algodiff_core.Make)

Module Owl_algodiff_core.Make

Parameters

Signature

module A : +Make (owl-base.Owl_algodiff_core.Make)

Module Owl_algodiff_core.Make

Parameters

Signature

module A : Owl_types_ndarray_algodiff.Sig with type arr = A.arr with type elt = A.elt
Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

diff --git a/docs/owl-base/Owl_algodiff_core/index.html b/docs/owl-base/Owl_algodiff_core/index.html index 30105cb01..baf522795 100644 --- a/docs/owl-base/Owl_algodiff_core/index.html +++ b/docs/owl-base/Owl_algodiff_core/index.html @@ -1,4 +1,4 @@ -Owl_algodiff_core (owl-base.Owl_algodiff_core)

Module Owl_algodiff_core

module Make +Owl_algodiff_core (owl-base.Owl_algodiff_core)

Module Owl_algodiff_core

diff --git a/docs/owl-base/Owl_algodiff_core_sig/index.html b/docs/owl-base/Owl_algodiff_core_sig/index.html index ee93d7bcc..301871cfc 100644 --- a/docs/owl-base/Owl_algodiff_core_sig/index.html +++ b/docs/owl-base/Owl_algodiff_core_sig/index.html @@ -1,2 +1,2 @@ -Owl_algodiff_core_sig (owl-base.Owl_algodiff_core_sig)

Module Owl_algodiff_core_sig

module type Sig = sig ... end
+Owl_algodiff_core_sig (owl-base.Owl_algodiff_core_sig)

Module Owl_algodiff_core_sig

module type Sig = sig ... end
diff --git a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Linalg/index.html b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Linalg/index.html index d1d667269..77c9eba46 100644 --- a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_core_sig.Sig.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_core_sig.Sig.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Mat/index.html b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Mat/index.html index 0a17ffa1c..a1fb4e2e5 100644 --- a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_core_sig.Sig.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_core_sig.Sig.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Scalar/index.html b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Scalar/index.html index 8b7c83d2e..cb8730d44 100644 --- a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_core_sig.Sig.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_core_sig.Sig.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/index.html b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/index.html index d2df34cc4..8b140e246 100644 --- a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/index.html +++ b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_core_sig.Sig.A)

Module Sig.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_core_sig.Sig.A)

Module Sig.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/index.html b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/index.html index 564076165..831966975 100644 --- a/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_algodiff_core_sig/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_algodiff_core_sig.Sig)

Module type Owl_algodiff_core_sig.Sig

Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

+Sig (owl-base.Owl_algodiff_core_sig.Sig)

Module type Owl_algodiff_core_sig.Sig

Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

diff --git a/docs/owl-base/Owl_algodiff_generic/Make/A/Linalg/index.html b/docs/owl-base/Owl_algodiff_generic/Make/A/Linalg/index.html index 913ffffeb..b0905f6fb 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_generic.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_generic.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_generic/Make/A/Mat/index.html b/docs/owl-base/Owl_algodiff_generic/Make/A/Mat/index.html index 608ea8097..12d48d64d 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_generic.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_generic.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/A/Scalar/index.html b/docs/owl-base/Owl_algodiff_generic/Make/A/Scalar/index.html index 9d5d78ecc..5bd3d19a7 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_generic.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_generic.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/A/index.html b/docs/owl-base/Owl_algodiff_generic/Make/A/index.html index bc1a7379c..2aae1cd54 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/A/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_generic.Make.A)

Module Make.A

include Owl_types_ndarray_eltcmp.Sig +A (owl-base.Owl_algodiff_generic.Make.A)

Module Make.A

include Owl_types_ndarray_eltcmp.Sig with type arr = A.arr with type elt = A.elt
include Owl_types_ndarray_basic.Sig with type arr = A.arr with type elt = A.elt
type arr = A.arr
type elt = A.elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Arr/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Arr/index.html index 2517a858f..0c57a8a53 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Arr/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_algodiff_generic.Make.Arr)

Module Make.Arr

val empty : int array -> t
val zeros : int array -> t
val ones : int array -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
val shape : t -> int array
val numel : t -> int
val reset : t -> unit
val reshape : t -> int array -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
+Arr (owl-base.Owl_algodiff_generic.Make.Arr)

Module Make.Arr

val empty : int array -> t
val zeros : int array -> t
val ones : int array -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
val shape : t -> int array
val numel : t -> int
val reset : t -> unit
val reshape : t -> int array -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Builder/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Builder/index.html index e726825a5..2526d4c76 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Builder/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_algodiff_generic.Make.Builder)

Module Make.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

+Builder (owl-base.Owl_algodiff_generic.Make.Builder)

Module Make.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Aiso/index.html index 803647de6..497f0ef4f 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_algodiff_generic.Make.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
+Aiso (owl-base.Owl_algodiff_generic.Make.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Piso/index.html index 254558b84..7a0f299c0 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_algodiff_generic.Make.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : A.elt -> A.elt -> t
val ff_ab : A.elt -> A.arr -> t
val ff_ba : A.arr -> A.elt -> t
val ff_bb : A.arr -> A.arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
+Piso (owl-base.Owl_algodiff_generic.Make.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : A.elt -> A.elt -> t
val ff_ab : A.elt -> A.arr -> t
val ff_ba : A.arr -> A.elt -> t
val ff_bb : A.arr -> A.arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Siao/index.html index d9b3233ae..a1678fa1a 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_algodiff_generic.Make.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : A.elt -> t array
val ff_arr : A.arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
+Siao (owl-base.Owl_algodiff_generic.Make.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : A.elt -> t array
val ff_arr : A.arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Sipo/index.html index d0ede28e9..d339c2e92 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_algodiff_generic.Make.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : A.elt -> t * t
val ff_arr : A.arr -> t * t
val df : t -> t -> t -> t
val dr : +Sipo (owl-base.Owl_algodiff_generic.Make.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : A.elt -> t * t
val ff_arr : A.arr -> t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Siso/index.html index 7900afd8e..a75c503a5 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_algodiff_generic.Make.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : A.elt -> t
val ff_arr : A.arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
+Siso (owl-base.Owl_algodiff_generic.Make.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : A.elt -> t
val ff_arr : A.arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Sito/index.html index d3a6e868f..587cc408d 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_algodiff_generic.Make.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : A.elt -> t * t * t
val ff_arr : A.arr -> t * t * t
val df : t -> t -> t -> t
val dr : +Sito (owl-base.Owl_algodiff_generic.Make.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : A.elt -> t * t * t
val ff_arr : A.arr -> t * t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Linalg/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Linalg/index.html index aa31d8107..fb7128dfe 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_generic.Make.Linalg)

Module Make.Linalg

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> t -> t * t * t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : t -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_generic.Make.Linalg)

Module Make.Linalg

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> t -> t * t * t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : t -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Mat/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Mat/index.html index c1ef47644..aba7bb0ff 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_generic.Make.Mat)

Module Make.Mat

val empty : int -> int -> t
val zeros : int -> int -> t
val eye : int -> t
val ones : int -> int -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
val shape : t -> int * int
val numel : t -> int
val row_num : t -> int
val col_num : t -> int
val reset : t -> unit
val reshape : int -> int -> t -> t
val get : t -> int -> int -> t
val set : t -> int -> int -> t -> t
val row : t -> int -> t
val mean : t -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
val map_by_row : (t -> t) -> t -> t
val of_arrays : A.elt array array -> t
val init_2d : int -> int -> (int -> int -> t) -> t
val print : t -> unit
+Mat (owl-base.Owl_algodiff_generic.Make.Mat)

Module Make.Mat

val empty : int -> int -> t
val zeros : int -> int -> t
val eye : int -> t
val ones : int -> int -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
val shape : t -> int * int
val numel : t -> int
val row_num : t -> int
val col_num : t -> int
val reset : t -> unit
val reshape : int -> int -> t -> t
val get : t -> int -> int -> t
val set : t -> int -> int -> t -> t
val row : t -> int -> t
val mean : t -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
val map_by_row : (t -> t) -> t -> t
val of_arrays : A.elt array array -> t
val init_2d : int -> int -> (int -> int -> t) -> t
val print : t -> unit
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/Maths/index.html b/docs/owl-base/Owl_algodiff_generic/Make/Maths/index.html index 57d77f439..305bd8c15 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/Maths/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_algodiff_generic.Make.Maths)

Module Make.Maths

val (+) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val add : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val div : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : t -> t -> t

Refer to :doc:`owl_dense_matrix_generic`

val dot : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val round : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : t -> int -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : t -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> t -> t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : t array array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : t -> t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

+Maths (owl-base.Owl_algodiff_generic.Make.Maths)

Module Make.Maths

val (+) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val add : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val div : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : t -> t -> t

Refer to :doc:`owl_dense_matrix_generic`

val dot : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val round : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : t -> int -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : t -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> t -> t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : t array array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : t -> t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

diff --git a/docs/owl-base/Owl_algodiff_generic/Make/NN/index.html b/docs/owl-base/Owl_algodiff_generic/Make/NN/index.html index 5172c366a..ad72a0a04 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/NN/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_algodiff_generic.Make.NN)

Module Make.NN

val dropout : ?rate:float -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dilated_conv1d : +NN (owl-base.Owl_algodiff_generic.Make.NN)

Module Make.NN

val dropout : ?rate:float -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Linalg/index.html b/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Linalg/index.html index 913ffffeb..b0905f6fb 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_generic.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_generic.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Mat/index.html b/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Mat/index.html index 608ea8097..12d48d64d 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_generic.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_generic.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Scalar/index.html b/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Scalar/index.html index 9d5d78ecc..5bd3d19a7 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_generic.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_generic.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/index.html b/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/index.html index d2b87bc15..20bb33b60 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_generic.Make.A)

Parameter Make.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_generic.Make.A)

Parameter Make.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_generic/Make/index.html b/docs/owl-base/Owl_algodiff_generic/Make/index.html index 2765a2e86..bfcb7c31d 100644 --- a/docs/owl-base/Owl_algodiff_generic/Make/index.html +++ b/docs/owl-base/Owl_algodiff_generic/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_algodiff_generic.Make)

Module Owl_algodiff_generic.Make

Parameters

Signature

include Owl_algodiff_core_sig.Sig +Make (owl-base.Owl_algodiff_generic.Make)

Module Owl_algodiff_generic.Make

Parameters

Signature

include Owl_algodiff_core_sig.Sig with type A.arr = A.arr with type A.elt = A.elt
module A : Owl_types_ndarray_algodiff.Sig with type arr = A.arr with type elt = A.elt
Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

val make_forward : t -> t -> int -> t

TODO

val make_reverse : t -> int -> t

TODO

val reverse_prop : t -> t -> unit

TODO

val diff : (t -> t) -> t -> t

diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

val diff' : (t -> t) -> t -> t * t

similar to diff, but return (f x, diff f x).

val grad : (t -> t) -> t -> t

gradient of f : (vector -> scalar) at x, reverse ad.

val grad' : (t -> t) -> t -> t * t

similar to grad, but return (f x, grad f x).

val jacobian : (t -> t) -> t -> t

jacobian of f : (vector -> vector) at x, both x and y are row vectors.

val jacobian' : (t -> t) -> t -> t * t

similar to jacobian, but return (f x, jacobian f x)

val jacobianv : (t -> t) -> t -> t -> t

jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

val jacobianv' : (t -> t) -> t -> t -> t * t

similar to jacobianv', but return (f x, jacobianv f x v)

val jacobianTv : (t -> t) -> t -> t -> t

transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

val jacobianTv' : (t -> t) -> t -> t -> t * t

similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

val hessian : (t -> t) -> t -> t

hessian of f : (scalar -> scalar) at x.

val hessian' : (t -> t) -> t -> t * t

simiarl to hessian, but return (f x, hessian f x)

val hessianv : (t -> t) -> t -> t -> t

hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

val hessianv' : (t -> t) -> t -> t -> t * t

similar to hessianv, but return (f x, hessianv f x v).

val laplacian : (t -> t) -> t -> t

laplacian of f : (scalar -> scalar) at x.

val laplacian' : (t -> t) -> t -> t * t

similar to laplacian, but return (f x, laplacian f x).

val gradhessian : (t -> t) -> t -> t * t

return (grad f x, hessian f x), f : (scalar -> scalar)

val gradhessian' : (t -> t) -> t -> t * t * t

return (f x, grad f x, hessian f x)

val gradhessianv : (t -> t) -> t -> t -> t * t

return (grad f x v, hessian f x v)

val gradhessianv' : (t -> t) -> t -> t -> t * t * t

return (f x, grad f x v, hessian f x v)

include Owl_algodiff_ops_sig.Sig diff --git a/docs/owl-base/Owl_algodiff_generic/index.html b/docs/owl-base/Owl_algodiff_generic/index.html index f83cf795f..7253c980c 100644 --- a/docs/owl-base/Owl_algodiff_generic/index.html +++ b/docs/owl-base/Owl_algodiff_generic/index.html @@ -1,4 +1,4 @@ -Owl_algodiff_generic (owl-base.Owl_algodiff_generic)

Module Owl_algodiff_generic

module Make +Owl_algodiff_generic (owl-base.Owl_algodiff_generic)

Module Owl_algodiff_generic

diff --git a/docs/owl-base/Owl_algodiff_generic_sig/index.html b/docs/owl-base/Owl_algodiff_generic_sig/index.html index 4ffb36326..b17d7364e 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/index.html @@ -1,2 +1,2 @@ -Owl_algodiff_generic_sig (owl-base.Owl_algodiff_generic_sig)

Module Owl_algodiff_generic_sig

module type Sig = sig ... end
+Owl_algodiff_generic_sig (owl-base.Owl_algodiff_generic_sig)

Module Owl_algodiff_generic_sig

module type Sig = sig ... end
diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Linalg/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Linalg/index.html index fb3aacc47..9e426a10d 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_generic_sig.Sig.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_generic_sig.Sig.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Mat/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Mat/index.html index e22367238..d21523cd4 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_generic_sig.Sig.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_generic_sig.Sig.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Scalar/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Scalar/index.html index 6c7d04c05..9e37be488 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_generic_sig.Sig.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_generic_sig.Sig.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/index.html index a74f2b050..9ee49fe7e 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_generic_sig.Sig.A)

Module Sig.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_generic_sig.Sig.A)

Module Sig.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Arr/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Arr/index.html index 14be57302..4827ff179 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Arr/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_algodiff_generic_sig.Sig.Arr)

Module Sig.Arr

val empty : int array -> t
val zeros : int array -> t
val ones : int array -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
val shape : t -> int array
val numel : t -> int
val reset : t -> unit
val reshape : t -> int array -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
+Arr (owl-base.Owl_algodiff_generic_sig.Sig.Arr)

Module Sig.Arr

val empty : int array -> t
val zeros : int array -> t
val ones : int array -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
val shape : t -> int array
val numel : t -> int
val reset : t -> unit
val reshape : t -> int array -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/index.html index 42a005886..bc501f2ab 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_algodiff_generic_sig.Sig.Builder)

Module Sig.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

+Builder (owl-base.Owl_algodiff_generic_sig.Sig.Builder)

Module Sig.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Aiso/index.html index 3cced3ff3..97fa62ee1 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
+Aiso (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Piso/index.html index 77274b99d..7c2fa8e07 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : A.elt -> A.elt -> t
val ff_ab : A.elt -> A.arr -> t
val ff_ba : A.arr -> A.elt -> t
val ff_bb : A.arr -> A.arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
+Piso (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : A.elt -> A.elt -> t
val ff_ab : A.elt -> A.arr -> t
val ff_ba : A.arr -> A.elt -> t
val ff_bb : A.arr -> A.arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Siao/index.html index 9170f4a22..e5af7e8a9 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : A.elt -> t array
val ff_arr : A.arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
+Siao (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : A.elt -> t array
val ff_arr : A.arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Sipo/index.html index 975ea9323..a86034e90 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : A.elt -> t * t
val ff_arr : A.arr -> t * t
val df : t -> t -> t -> t
val dr : +Sipo (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : A.elt -> t * t
val ff_arr : A.arr -> t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Siso/index.html index b4e55e53e..19442f604 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : A.elt -> t
val ff_arr : A.arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
+Siso (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : A.elt -> t
val ff_arr : A.arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Sito/index.html index ee8f074a5..634e43f91 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : A.elt -> t * t * t
val ff_arr : A.arr -> t * t * t
val df : t -> t -> t -> t
val dr : +Sito (owl-base.Owl_algodiff_generic_sig.Sig.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : A.elt -> t * t * t
val ff_arr : A.arr -> t * t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Linalg/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Linalg/index.html index b12d498d0..ac9558a10 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_generic_sig.Sig.Linalg)

Module Sig.Linalg

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> t -> t * t * t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : t -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_generic_sig.Sig.Linalg)

Module Sig.Linalg

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> t -> t * t * t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : t -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Mat/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Mat/index.html index f9f6502df..cadc018bf 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_generic_sig.Sig.Mat)

Module Sig.Mat

val empty : int -> int -> t
val zeros : int -> int -> t
val eye : int -> t
val ones : int -> int -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
val shape : t -> int * int
val numel : t -> int
val row_num : t -> int
val col_num : t -> int
val reset : t -> unit
val reshape : int -> int -> t -> t
val get : t -> int -> int -> t
val set : t -> int -> int -> t -> t
val row : t -> int -> t
val mean : t -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
val map_by_row : (t -> t) -> t -> t
val of_arrays : A.elt array array -> t
val init_2d : int -> int -> (int -> int -> t) -> t
val print : t -> unit
+Mat (owl-base.Owl_algodiff_generic_sig.Sig.Mat)

Module Sig.Mat

val empty : int -> int -> t
val zeros : int -> int -> t
val eye : int -> t
val ones : int -> int -> t
val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
val shape : t -> int * int
val numel : t -> int
val row_num : t -> int
val col_num : t -> int
val reset : t -> unit
val reshape : int -> int -> t -> t
val get : t -> int -> int -> t
val set : t -> int -> int -> t -> t
val row : t -> int -> t
val mean : t -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
val map_by_row : (t -> t) -> t -> t
val of_arrays : A.elt array array -> t
val init_2d : int -> int -> (int -> int -> t) -> t
val print : t -> unit
diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Maths/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Maths/index.html index 8a1e3821d..17ea0c8c0 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Maths/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_algodiff_generic_sig.Sig.Maths)

Module Sig.Maths

val (+) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val add : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val div : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : t -> t -> t

Refer to :doc:`owl_dense_matrix_generic`

val dot : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val round : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : t -> int -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : t -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> t -> t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : t array array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : t -> t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

+Maths (owl-base.Owl_algodiff_generic_sig.Sig.Maths)

Module Sig.Maths

val (+) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val add : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val div : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : t -> t -> t

Refer to :doc:`owl_dense_matrix_generic`

val dot : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val round : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : t -> int -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : t -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> t -> t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : t array array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : t -> t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/NN/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/NN/index.html index 87cff1d54..72e8849e3 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/NN/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_algodiff_generic_sig.Sig.NN)

Module Sig.NN

val dropout : ?rate:float -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dilated_conv1d : +NN (owl-base.Owl_algodiff_generic_sig.Sig.NN)

Module Sig.NN

val dropout : ?rate:float -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/index.html b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/index.html index 5f4531b26..e5b464def 100644 --- a/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_algodiff_generic_sig/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_algodiff_generic_sig.Sig)

Module type Owl_algodiff_generic_sig.Sig

include Owl_algodiff_core_sig.Sig
Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

val make_forward : t -> t -> int -> t

TODO

val make_reverse : t -> int -> t

TODO

val reverse_prop : t -> t -> unit

TODO

val diff : (t -> t) -> t -> t

diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

val diff' : (t -> t) -> t -> t * t

similar to diff, but return (f x, diff f x).

val grad : (t -> t) -> t -> t

gradient of f : (vector -> scalar) at x, reverse ad.

val grad' : (t -> t) -> t -> t * t

similar to grad, but return (f x, grad f x).

val jacobian : (t -> t) -> t -> t

jacobian of f : (vector -> vector) at x, both x and y are row vectors.

val jacobian' : (t -> t) -> t -> t * t

similar to jacobian, but return (f x, jacobian f x)

val jacobianv : (t -> t) -> t -> t -> t

jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

val jacobianv' : (t -> t) -> t -> t -> t * t

similar to jacobianv', but return (f x, jacobianv f x v)

val jacobianTv : (t -> t) -> t -> t -> t

transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

val jacobianTv' : (t -> t) -> t -> t -> t * t

similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

val hessian : (t -> t) -> t -> t

hessian of f : (scalar -> scalar) at x.

val hessian' : (t -> t) -> t -> t * t

simiarl to hessian, but return (f x, hessian f x)

val hessianv : (t -> t) -> t -> t -> t

hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

val hessianv' : (t -> t) -> t -> t -> t * t

similar to hessianv, but return (f x, hessianv f x v).

val laplacian : (t -> t) -> t -> t

laplacian of f : (scalar -> scalar) at x.

val laplacian' : (t -> t) -> t -> t * t

similar to laplacian, but return (f x, laplacian f x).

val gradhessian : (t -> t) -> t -> t * t

return (grad f x, hessian f x), f : (scalar -> scalar)

val gradhessian' : (t -> t) -> t -> t * t * t

return (f x, grad f x, hessian f x)

val gradhessianv : (t -> t) -> t -> t -> t * t

return (grad f x v, hessian f x v)

val gradhessianv' : (t -> t) -> t -> t -> t * t * t

return (f x, grad f x v, hessian f x v)

include Owl_algodiff_ops_sig.Sig +Sig (owl-base.Owl_algodiff_generic_sig.Sig)

Module type Owl_algodiff_generic_sig.Sig

include Owl_algodiff_core_sig.Sig
Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

val make_forward : t -> t -> int -> t

TODO

val make_reverse : t -> int -> t

TODO

val reverse_prop : t -> t -> unit

TODO

val diff : (t -> t) -> t -> t

diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

val diff' : (t -> t) -> t -> t * t

similar to diff, but return (f x, diff f x).

val grad : (t -> t) -> t -> t

gradient of f : (vector -> scalar) at x, reverse ad.

val grad' : (t -> t) -> t -> t * t

similar to grad, but return (f x, grad f x).

val jacobian : (t -> t) -> t -> t

jacobian of f : (vector -> vector) at x, both x and y are row vectors.

val jacobian' : (t -> t) -> t -> t * t

similar to jacobian, but return (f x, jacobian f x)

val jacobianv : (t -> t) -> t -> t -> t

jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

val jacobianv' : (t -> t) -> t -> t -> t * t

similar to jacobianv', but return (f x, jacobianv f x v)

val jacobianTv : (t -> t) -> t -> t -> t

transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

val jacobianTv' : (t -> t) -> t -> t -> t * t

similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

val hessian : (t -> t) -> t -> t

hessian of f : (scalar -> scalar) at x.

val hessian' : (t -> t) -> t -> t * t

simiarl to hessian, but return (f x, hessian f x)

val hessianv : (t -> t) -> t -> t -> t

hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

val hessianv' : (t -> t) -> t -> t -> t * t

similar to hessianv, but return (f x, hessianv f x v).

val laplacian : (t -> t) -> t -> t

laplacian of f : (scalar -> scalar) at x.

val laplacian' : (t -> t) -> t -> t * t

similar to laplacian, but return (f x, laplacian f x).

val gradhessian : (t -> t) -> t -> t * t

return (grad f x, hessian f x), f : (scalar -> scalar)

val gradhessian' : (t -> t) -> t -> t * t * t

return (f x, grad f x, hessian f x)

val gradhessianv : (t -> t) -> t -> t -> t * t

return (grad f x v, hessian f x v)

val gradhessianv' : (t -> t) -> t -> t -> t * t * t

return (f x, grad f x v, hessian f x v)

include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Linalg/index.html b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Linalg/index.html index 2733dbd57..e62772893 100644 --- a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_graph_convert.Make.Core.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_graph_convert.Make.Core.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Mat/index.html b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Mat/index.html index 3e429a987..f8a3106ad 100644 --- a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_graph_convert.Make.Core.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_graph_convert.Make.Core.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Scalar/index.html b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Scalar/index.html index 0217ddc5e..cf98658ee 100644 --- a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_graph_convert.Make.Core.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_graph_convert.Make.Core.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/index.html b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/index.html index bb81c57d7..feb7a2fe4 100644 --- a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/index.html +++ b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_graph_convert.Make.Core.A)

Module Core.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_graph_convert.Make.Core.A)

Module Core.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/index.html b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/index.html index 7171e5bcc..05115d4fe 100644 --- a/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/index.html +++ b/docs/owl-base/Owl_algodiff_graph_convert/Make/argument-1-Core/index.html @@ -1,2 +1,2 @@ -Core (owl-base.Owl_algodiff_graph_convert.Make.Core)

Parameter Make.Core

Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

+Core (owl-base.Owl_algodiff_graph_convert.Make.Core)

Parameter Make.Core

Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

diff --git a/docs/owl-base/Owl_algodiff_graph_convert/Make/index.html b/docs/owl-base/Owl_algodiff_graph_convert/Make/index.html index 740d983ab..2126db174 100644 --- a/docs/owl-base/Owl_algodiff_graph_convert/Make/index.html +++ b/docs/owl-base/Owl_algodiff_graph_convert/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_algodiff_graph_convert.Make)

Module Owl_algodiff_graph_convert.Make

Parameters

Signature

val to_trace : Core.t list -> string

to_trace [t0; t1; ...] outputs the trace of computation graph on the terminal in a human-readable format.

val to_dot : Core.t list -> string

to_dot [t0; t1; ...] outputs the trace of computation graph in the dot file format which you can use other tools further visualisation, such as Graphviz.

val pp_num : Stdlib.Format.formatter -> Core.t -> unit

pp_num t pretty prints the abstract number used in Algodiff.

+Make (owl-base.Owl_algodiff_graph_convert.Make)

Module Owl_algodiff_graph_convert.Make

Parameters

Signature

val to_trace : Core.t list -> string

to_trace [t0; t1; ...] outputs the trace of computation graph on the terminal in a human-readable format.

val to_dot : Core.t list -> string

to_dot [t0; t1; ...] outputs the trace of computation graph in the dot file format which you can use other tools further visualisation, such as Graphviz.

val pp_num : Stdlib.Format.formatter -> Core.t -> unit

pp_num t pretty prints the abstract number used in Algodiff.

diff --git a/docs/owl-base/Owl_algodiff_graph_convert/index.html b/docs/owl-base/Owl_algodiff_graph_convert/index.html index 1a8cab266..0faebe2f9 100644 --- a/docs/owl-base/Owl_algodiff_graph_convert/index.html +++ b/docs/owl-base/Owl_algodiff_graph_convert/index.html @@ -1,4 +1,4 @@ -Owl_algodiff_graph_convert (owl-base.Owl_algodiff_graph_convert)

Module Owl_algodiff_graph_convert

module Make +Owl_algodiff_graph_convert (owl-base.Owl_algodiff_graph_convert)

Module Owl_algodiff_graph_convert

diff --git a/docs/owl-base/Owl_algodiff_graph_convert_sig/index.html b/docs/owl-base/Owl_algodiff_graph_convert_sig/index.html index d2b134736..3cedb8ebe 100644 --- a/docs/owl-base/Owl_algodiff_graph_convert_sig/index.html +++ b/docs/owl-base/Owl_algodiff_graph_convert_sig/index.html @@ -1,2 +1,2 @@ -Owl_algodiff_graph_convert_sig (owl-base.Owl_algodiff_graph_convert_sig)

Module Owl_algodiff_graph_convert_sig

module type Sig = sig ... end
+Owl_algodiff_graph_convert_sig (owl-base.Owl_algodiff_graph_convert_sig)

Module Owl_algodiff_graph_convert_sig

module type Sig = sig ... end
diff --git a/docs/owl-base/Owl_algodiff_graph_convert_sig/module-type-Sig/index.html b/docs/owl-base/Owl_algodiff_graph_convert_sig/module-type-Sig/index.html index e42d1d861..ecc87a319 100644 --- a/docs/owl-base/Owl_algodiff_graph_convert_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_algodiff_graph_convert_sig/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_algodiff_graph_convert_sig.Sig)

Module type Owl_algodiff_graph_convert_sig.Sig

type t
val to_trace : t list -> string

to_trace [t0; t1; ...] outputs the trace of computation graph on the terminal in a human-readable format.

val to_dot : t list -> string

to_dot [t0; t1; ...] outputs the trace of computation graph in the dot file format which you can use other tools further visualisation, such as Graphviz.

val pp_num : Stdlib.Format.formatter -> t -> unit

pp_num t pretty prints the abstract number used in Algodiff.

+Sig (owl-base.Owl_algodiff_graph_convert_sig.Sig)

Module type Owl_algodiff_graph_convert_sig.Sig

type t
val to_trace : t list -> string

to_trace [t0; t1; ...] outputs the trace of computation graph on the terminal in a human-readable format.

val to_dot : t list -> string

to_dot [t0; t1; ...] outputs the trace of computation graph in the dot file format which you can use other tools further visualisation, such as Graphviz.

val pp_num : Stdlib.Format.formatter -> t -> unit

pp_num t pretty prints the abstract number used in Algodiff.

diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Arr/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Arr/index.html index 04ae090e7..cb4aed27c 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Arr/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_algodiff_ops.Make.Arr)

Module Make.Arr

val empty : int array -> Core.t
val zeros : int array -> Core.t
val ones : int array -> Core.t
val uniform : ?a:Core.A.elt -> ?b:Core.A.elt -> int array -> Core.t
val gaussian : ?mu:Core.A.elt -> ?sigma:Core.A.elt -> int array -> Core.t
val shape : Core.t -> int array
val numel : Core.t -> int
val reset : Core.t -> unit
val reshape : Core.t -> int array -> Core.t
val add : Core.t -> Core.t -> Core.t
val sub : Core.t -> Core.t -> Core.t
val mul : Core.t -> Core.t -> Core.t
val div : Core.t -> Core.t -> Core.t
val dot : Core.t -> Core.t -> Core.t
+Arr (owl-base.Owl_algodiff_ops.Make.Arr)

Module Make.Arr

val empty : int array -> Core.t
val zeros : int array -> Core.t
val ones : int array -> Core.t
val uniform : ?a:Core.A.elt -> ?b:Core.A.elt -> int array -> Core.t
val gaussian : ?mu:Core.A.elt -> ?sigma:Core.A.elt -> int array -> Core.t
val shape : Core.t -> int array
val numel : Core.t -> int
val reset : Core.t -> unit
val reshape : Core.t -> int array -> Core.t
val add : Core.t -> Core.t -> Core.t
val sub : Core.t -> Core.t -> Core.t
val mul : Core.t -> Core.t -> Core.t
val div : Core.t -> Core.t -> Core.t
val dot : Core.t -> Core.t -> Core.t
diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Builder/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Builder/index.html index 26c123724..f90440bbb 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Builder/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_algodiff_ops.Make.Builder)

Module Make.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> Core.t -> Core.t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> Core.t -> Core.t * Core.t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> Core.t -> Core.t * Core.t * Core.t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> Core.t -> Core.t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> Core.t -> Core.t -> Core.t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> Core.t array -> Core.t

build array input single output operations

+Builder (owl-base.Owl_algodiff_ops.Make.Builder)

Module Make.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> Core.t -> Core.t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> Core.t -> Core.t * Core.t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> Core.t -> Core.t * Core.t * Core.t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> Core.t -> Core.t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> Core.t -> Core.t -> Core.t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> Core.t array -> Core.t

build array input single output operations

diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Aiso/index.html index 70615bd1a..43c882d9e 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_algodiff_ops.Make.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : Core.t array -> Core.t
val df : int list -> Core.t -> Core.t array -> Core.t array -> Core.t
val dr : int list -> Core.t array -> Core.t -> Core.t Stdlib.ref -> Core.t list
+Aiso (owl-base.Owl_algodiff_ops.Make.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : Core.t array -> Core.t
val df : int list -> Core.t -> Core.t array -> Core.t array -> Core.t
val dr : int list -> Core.t array -> Core.t -> Core.t Stdlib.ref -> Core.t list
diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Piso/index.html index f90acaf80..ef7bf1544 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_algodiff_ops.Make.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : Core.A.elt -> Core.A.elt -> Core.t
val ff_ab : Core.A.elt -> Core.A.arr -> Core.t
val ff_ba : Core.A.arr -> Core.A.elt -> Core.t
val ff_bb : Core.A.arr -> Core.A.arr -> Core.t
val df_da : Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val df_db : Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val df_dab : Core.t -> Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val dr_ab : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t * Core.t
val dr_a : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
val dr_b : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
+Piso (owl-base.Owl_algodiff_ops.Make.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : Core.A.elt -> Core.A.elt -> Core.t
val ff_ab : Core.A.elt -> Core.A.arr -> Core.t
val ff_ba : Core.A.arr -> Core.A.elt -> Core.t
val ff_bb : Core.A.arr -> Core.A.arr -> Core.t
val df_da : Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val df_db : Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val df_dab : Core.t -> Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val dr_ab : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t * Core.t
val dr_a : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
val dr_b : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Siao/index.html index 68a444b30..a231fef92 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Siao/index.html @@ -1,5 +1,5 @@ -Siao (owl-base.Owl_algodiff_ops.Make.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : Core.A.elt -> Core.t array
val ff_arr : Core.A.arr -> Core.t array
val df : Core.t array -> Core.t -> Core.t -> Core.t array
val dr : +Siao (owl-base.Owl_algodiff_ops.Make.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : Core.A.elt -> Core.t array
val ff_arr : Core.A.arr -> Core.t array
val df : Core.t array -> Core.t -> Core.t -> Core.t array
val dr : Core.t -> Core.t -> Core.t Stdlib.ref array -> diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Sipo/index.html index cbd53d676..9949c6997 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_algodiff_ops.Make.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : Core.A.elt -> Core.t * Core.t
val ff_arr : Core.A.arr -> Core.t * Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : +Sipo (owl-base.Owl_algodiff_ops.Make.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : Core.A.elt -> Core.t * Core.t
val ff_arr : Core.A.arr -> Core.t * Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : Core.t -> Core.t -> (Core.t Stdlib.ref * Core.t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Siso/index.html index 1ce3bfd46..bca5e48d4 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_algodiff_ops.Make.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : Core.A.elt -> Core.t
val ff_arr : Core.A.arr -> Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
+Siso (owl-base.Owl_algodiff_ops.Make.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : Core.A.elt -> Core.t
val ff_arr : Core.A.arr -> Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Sito/index.html index f1a80566f..10af4e4df 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_algodiff_ops.Make.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : Core.A.elt -> Core.t * Core.t * Core.t
val ff_arr : Core.A.arr -> Core.t * Core.t * Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : +Sito (owl-base.Owl_algodiff_ops.Make.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : Core.A.elt -> Core.t * Core.t * Core.t
val ff_arr : Core.A.arr -> Core.t * Core.t * Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : Core.t -> Core.t -> (Core.t Stdlib.ref * Core.t Stdlib.ref * Core.t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Linalg/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Linalg/index.html index 9d4652b08..3587b3693 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_ops.Make.Linalg)

Module Make.Linalg

val inv : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : Core.t -> Core.t * Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : Core.t -> Core.t * Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> Core.t -> Core.t * Core.t * Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : Core.t -> Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_ops.Make.Linalg)

Module Make.Linalg

val inv : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : Core.t -> Core.t * Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : Core.t -> Core.t * Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> Core.t -> Core.t * Core.t * Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : Core.t -> Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Core.t -> Core.t -> diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Mat/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Mat/index.html index c975360e9..3019b6b3c 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_ops.Make.Mat)

Module Make.Mat

val empty : int -> int -> Core.t
val zeros : int -> int -> Core.t
val eye : int -> Core.t
val ones : int -> int -> Core.t
val uniform : ?a:Core.A.elt -> ?b:Core.A.elt -> int -> int -> Core.t
val gaussian : ?mu:Core.A.elt -> ?sigma:Core.A.elt -> int -> int -> Core.t
val shape : Core.t -> int * int
val numel : Core.t -> int
val row_num : Core.t -> int
val col_num : Core.t -> int
val reset : Core.t -> unit
val reshape : int -> int -> Core.t -> Core.t
val get : Core.t -> int -> int -> Core.t
val set : Core.t -> int -> int -> Core.t -> Core.t
val row : Core.t -> int -> Core.t
val mean : Core.t -> Core.t
val add : Core.t -> Core.t -> Core.t
val sub : Core.t -> Core.t -> Core.t
val mul : Core.t -> Core.t -> Core.t
val div : Core.t -> Core.t -> Core.t
val dot : Core.t -> Core.t -> Core.t
val map_by_row : (Core.t -> Core.t) -> Core.t -> Core.t
val of_arrays : Core.A.elt array array -> Core.t
val init_2d : int -> int -> (int -> int -> Core.t) -> Core.t
val print : Core.t -> unit
+Mat (owl-base.Owl_algodiff_ops.Make.Mat)

Module Make.Mat

val empty : int -> int -> Core.t
val zeros : int -> int -> Core.t
val eye : int -> Core.t
val ones : int -> int -> Core.t
val uniform : ?a:Core.A.elt -> ?b:Core.A.elt -> int -> int -> Core.t
val gaussian : ?mu:Core.A.elt -> ?sigma:Core.A.elt -> int -> int -> Core.t
val shape : Core.t -> int * int
val numel : Core.t -> int
val row_num : Core.t -> int
val col_num : Core.t -> int
val reset : Core.t -> unit
val reshape : int -> int -> Core.t -> Core.t
val get : Core.t -> int -> int -> Core.t
val set : Core.t -> int -> int -> Core.t -> Core.t
val row : Core.t -> int -> Core.t
val mean : Core.t -> Core.t
val add : Core.t -> Core.t -> Core.t
val sub : Core.t -> Core.t -> Core.t
val mul : Core.t -> Core.t -> Core.t
val div : Core.t -> Core.t -> Core.t
val dot : Core.t -> Core.t -> Core.t
val map_by_row : (Core.t -> Core.t) -> Core.t -> Core.t
val of_arrays : Core.A.elt array array -> Core.t
val init_2d : int -> int -> (int -> int -> Core.t) -> Core.t
val print : Core.t -> unit
diff --git a/docs/owl-base/Owl_algodiff_ops/Make/Maths/index.html b/docs/owl-base/Owl_algodiff_ops/Make/Maths/index.html index f46255df2..473a9bad4 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/Maths/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_algodiff_ops.Make.Maths)

Module Make.Maths

val (+) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val add : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val div : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_matrix_generic`

val dot : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val round : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : Core.t -> int array -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : Core.t -> int -> int -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : Core.t -> int -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> Core.t -> Core.t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : Core.t array array -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : Core.t -> Core.t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> Core.t array -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> Core.t array -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

+Maths (owl-base.Owl_algodiff_ops.Make.Maths)

Module Make.Maths

val (+) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val add : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val div : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_matrix_generic`

val dot : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val round : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : Core.t -> int array -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : Core.t -> int -> int -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : Core.t -> int -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> Core.t -> Core.t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : Core.t array array -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : Core.t -> Core.t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> Core.t array -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> Core.t array -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> Core.t -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

diff --git a/docs/owl-base/Owl_algodiff_ops/Make/NN/index.html b/docs/owl-base/Owl_algodiff_ops/Make/NN/index.html index 17df6b796..bbd81a296 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/NN/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_algodiff_ops.Make.NN)

Module Make.NN

val dropout : ?rate:float -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : +NN (owl-base.Owl_algodiff_ops.Make.NN)

Module Make.NN

val dropout : ?rate:float -> Core.t -> Core.t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : ?padding:Owl_types.padding -> Core.t -> Core.t -> diff --git a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Linalg/index.html b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Linalg/index.html index 9609baa5a..210a5e6d1 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_ops.Make.Core.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_ops.Make.Core.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Mat/index.html b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Mat/index.html index 67c6f2803..95f83a2c5 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_ops.Make.Core.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_ops.Make.Core.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Scalar/index.html b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Scalar/index.html index b1edc9e34..b496997b6 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_ops.Make.Core.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_ops.Make.Core.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/index.html b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/index.html index f8b38342f..4d6309578 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_ops.Make.Core.A)

Module Core.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_ops.Make.Core.A)

Module Core.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/index.html b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/index.html index c6b4b023f..eee55e17e 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/argument-1-Core/index.html @@ -1,2 +1,2 @@ -Core (owl-base.Owl_algodiff_ops.Make.Core)

Parameter Make.Core

Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

+Core (owl-base.Owl_algodiff_ops.Make.Core)

Parameter Make.Core

Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

diff --git a/docs/owl-base/Owl_algodiff_ops/Make/index.html b/docs/owl-base/Owl_algodiff_ops/Make/index.html index 1202757ee..ac6ec3829 100644 --- a/docs/owl-base/Owl_algodiff_ops/Make/index.html +++ b/docs/owl-base/Owl_algodiff_ops/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_algodiff_ops.Make)

Module Owl_algodiff_ops.Make

Parameters

Signature

module Builder : +Make (owl-base.Owl_algodiff_ops.Make)

Module Owl_algodiff_ops.Make

Parameters

Signature

module Builder : Owl_algodiff_ops_builder_sig.Sig with type t := Core.t and type elt := Core.A.elt diff --git a/docs/owl-base/Owl_algodiff_ops/index.html b/docs/owl-base/Owl_algodiff_ops/index.html index 35e0bc415..c612e1912 100644 --- a/docs/owl-base/Owl_algodiff_ops/index.html +++ b/docs/owl-base/Owl_algodiff_ops/index.html @@ -1,5 +1,5 @@ -Owl_algodiff_ops (owl-base.Owl_algodiff_ops)

Module Owl_algodiff_ops

module Make +Owl_algodiff_ops (owl-base.Owl_algodiff_ops)

Module Owl_algodiff_ops

module Make (Core : Owl_algodiff_core_sig.Sig) : Owl_algodiff_ops_sig.Sig with type t := Core.t diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Linalg/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Linalg/index.html index 7f85713d9..8a43b50fd 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_ops_builder.Make.Core.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_ops_builder.Make.Core.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Mat/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Mat/index.html index da8efb3bf..ff6ea7a68 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_ops_builder.Make.Core.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_ops_builder.Make.Core.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Scalar/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Scalar/index.html index d38caaa25..af5b2ecfb 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_ops_builder.Make.Core.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_ops_builder.Make.Core.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/index.html index 6d81b5168..dc90b1e60 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_ops_builder.Make.Core.A)

Module Core.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_ops_builder.Make.Core.A)

Module Core.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/index.html index 8d580c814..431c1f0b6 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/argument-1-Core/index.html @@ -1,2 +1,2 @@ -Core (owl-base.Owl_algodiff_ops_builder.Make.Core)

Parameter Make.Core

Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

+Core (owl-base.Owl_algodiff_ops_builder.Make.Core)

Parameter Make.Core

Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/index.html index a12ebdac9..3d26c10fd 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_algodiff_ops_builder.Make)

Module Owl_algodiff_ops_builder.Make

Parameters

Signature

module type Siso = sig ... end
val build_siso : (module Siso) -> Core.t -> Core.t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> Core.t -> Core.t * Core.t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> Core.t -> Core.t * Core.t * Core.t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> Core.t -> Core.t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> Core.t -> Core.t -> Core.t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> Core.t array -> Core.t

build array input single output operations

+Make (owl-base.Owl_algodiff_ops_builder.Make)

Module Owl_algodiff_ops_builder.Make

Parameters

Signature

module type Siso = sig ... end
val build_siso : (module Siso) -> Core.t -> Core.t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> Core.t -> Core.t * Core.t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> Core.t -> Core.t * Core.t * Core.t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> Core.t -> Core.t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> Core.t -> Core.t -> Core.t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> Core.t array -> Core.t

build array input single output operations

diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Aiso/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Aiso/index.html index b10428a49..26898eec5 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_algodiff_ops_builder.Make.Aiso)

Module type Make.Aiso

val label : string
val ff : Core.t array -> Core.t
val df : int list -> Core.t -> Core.t array -> Core.t array -> Core.t
val dr : int list -> Core.t array -> Core.t -> Core.t Stdlib.ref -> Core.t list
+Aiso (owl-base.Owl_algodiff_ops_builder.Make.Aiso)

Module type Make.Aiso

val label : string
val ff : Core.t array -> Core.t
val df : int list -> Core.t -> Core.t array -> Core.t array -> Core.t
val dr : int list -> Core.t array -> Core.t -> Core.t Stdlib.ref -> Core.t list
diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Piso/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Piso/index.html index fd5f14742..b811fb35f 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Piso/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_algodiff_ops_builder.Make.Piso)

Module type Make.Piso

val label : string
val ff_aa : Core.A.elt -> Core.A.elt -> Core.t
val ff_ab : Core.A.elt -> Core.A.arr -> Core.t
val ff_ba : Core.A.arr -> Core.A.elt -> Core.t
val ff_bb : Core.A.arr -> Core.A.arr -> Core.t
val df_da : Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val df_db : Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val df_dab : Core.t -> Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val dr_ab : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t * Core.t
val dr_a : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
val dr_b : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
+Piso (owl-base.Owl_algodiff_ops_builder.Make.Piso)

Module type Make.Piso

val label : string
val ff_aa : Core.A.elt -> Core.A.elt -> Core.t
val ff_ab : Core.A.elt -> Core.A.arr -> Core.t
val ff_ba : Core.A.arr -> Core.A.elt -> Core.t
val ff_bb : Core.A.arr -> Core.A.arr -> Core.t
val df_da : Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val df_db : Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val df_dab : Core.t -> Core.t -> Core.t -> Core.t -> Core.t -> Core.t
val dr_ab : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t * Core.t
val dr_a : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
val dr_b : Core.t -> Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Siao/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Siao/index.html index 7af270f8f..3840b3a46 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Siao/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Siao/index.html @@ -1,5 +1,5 @@ -Siao (owl-base.Owl_algodiff_ops_builder.Make.Siao)

Module type Make.Siao

val label : string
val ff_f : Core.A.elt -> Core.t array
val ff_arr : Core.A.arr -> Core.t array
val df : Core.t array -> Core.t -> Core.t -> Core.t array
val dr : +Siao (owl-base.Owl_algodiff_ops_builder.Make.Siao)

Module type Make.Siao

val label : string
val ff_f : Core.A.elt -> Core.t array
val ff_arr : Core.A.arr -> Core.t array
val df : Core.t array -> Core.t -> Core.t -> Core.t array
val dr : Core.t -> Core.t -> Core.t Stdlib.ref array -> diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Sipo/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Sipo/index.html index 948412464..de6c7172e 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_algodiff_ops_builder.Make.Sipo)

Module type Make.Sipo

val label : string
val ff_f : Core.A.elt -> Core.t * Core.t
val ff_arr : Core.A.arr -> Core.t * Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : +Sipo (owl-base.Owl_algodiff_ops_builder.Make.Sipo)

Module type Make.Sipo

val label : string
val ff_f : Core.A.elt -> Core.t * Core.t
val ff_arr : Core.A.arr -> Core.t * Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : Core.t -> Core.t -> (Core.t Stdlib.ref * Core.t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Siso/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Siso/index.html index 8e0839bec..079d88e6d 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Siso/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_algodiff_ops_builder.Make.Siso)

Module type Make.Siso

val label : string
val ff_f : Core.A.elt -> Core.t
val ff_arr : Core.A.arr -> Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
+Siso (owl-base.Owl_algodiff_ops_builder.Make.Siso)

Module type Make.Siso

val label : string
val ff_f : Core.A.elt -> Core.t
val ff_arr : Core.A.arr -> Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : Core.t -> Core.t -> Core.t Stdlib.ref -> Core.t
diff --git a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Sito/index.html b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Sito/index.html index d0d46661a..e88313026 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Sito/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/Make/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_algodiff_ops_builder.Make.Sito)

Module type Make.Sito

val label : string
val ff_f : Core.A.elt -> Core.t * Core.t * Core.t
val ff_arr : Core.A.arr -> Core.t * Core.t * Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : +Sito (owl-base.Owl_algodiff_ops_builder.Make.Sito)

Module type Make.Sito

val label : string
val ff_f : Core.A.elt -> Core.t * Core.t * Core.t
val ff_arr : Core.A.arr -> Core.t * Core.t * Core.t
val df : Core.t -> Core.t -> Core.t -> Core.t
val dr : Core.t -> Core.t -> (Core.t Stdlib.ref * Core.t Stdlib.ref * Core.t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_ops_builder/index.html b/docs/owl-base/Owl_algodiff_ops_builder/index.html index 527892f6c..173ecc752 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder/index.html @@ -1,5 +1,5 @@ -Owl_algodiff_ops_builder (owl-base.Owl_algodiff_ops_builder)

Module Owl_algodiff_ops_builder

module Make +Owl_algodiff_ops_builder (owl-base.Owl_algodiff_ops_builder)

Module Owl_algodiff_ops_builder

module Make (Core : Owl_algodiff_core_sig.Sig) : Owl_algodiff_ops_builder_sig.Sig with type t := Core.t diff --git a/docs/owl-base/Owl_algodiff_ops_builder_sig/index.html b/docs/owl-base/Owl_algodiff_ops_builder_sig/index.html index dc79e64f5..a55e1dba3 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder_sig/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder_sig/index.html @@ -1,2 +1,2 @@ -Owl_algodiff_ops_builder_sig (owl-base.Owl_algodiff_ops_builder_sig)

Module Owl_algodiff_ops_builder_sig

module type Sig = sig ... end
+Owl_algodiff_ops_builder_sig (owl-base.Owl_algodiff_ops_builder_sig)

Module Owl_algodiff_ops_builder_sig

module type Sig = sig ... end
diff --git a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/index.html b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/index.html index ec719ce9f..6be345cfe 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_algodiff_ops_builder_sig.Sig)

Module type Owl_algodiff_ops_builder_sig.Sig

type elt
type arr
type t
type op
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

+Sig (owl-base.Owl_algodiff_ops_builder_sig.Sig)

Module type Owl_algodiff_ops_builder_sig.Sig

type elt
type arr
type t
type op
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

diff --git a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Aiso/index.html b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Aiso/index.html index 2c80805fe..73065a479 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_algodiff_ops_builder_sig.Sig.Aiso)

Module type Sig.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
+Aiso (owl-base.Owl_algodiff_ops_builder_sig.Sig.Aiso)

Module type Sig.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
diff --git a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Piso/index.html b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Piso/index.html index c1653acca..1a21d160d 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Piso/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_algodiff_ops_builder_sig.Sig.Piso)

Module type Sig.Piso

val label : string
val ff_aa : elt -> elt -> t
val ff_ab : elt -> arr -> t
val ff_ba : arr -> elt -> t
val ff_bb : arr -> arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
+Piso (owl-base.Owl_algodiff_ops_builder_sig.Sig.Piso)

Module type Sig.Piso

val label : string
val ff_aa : elt -> elt -> t
val ff_ab : elt -> arr -> t
val ff_ba : arr -> elt -> t
val ff_bb : arr -> arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Siao/index.html b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Siao/index.html index a16887c66..da40eff2e 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Siao/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_algodiff_ops_builder_sig.Sig.Siao)

Module type Sig.Siao

val label : string
val ff_f : elt -> t array
val ff_arr : arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
+Siao (owl-base.Owl_algodiff_ops_builder_sig.Sig.Siao)

Module type Sig.Siao

val label : string
val ff_f : elt -> t array
val ff_arr : arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
diff --git a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Sipo/index.html b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Sipo/index.html index 2383db1b2..d35f2ff13 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_algodiff_ops_builder_sig.Sig.Sipo)

Module type Sig.Sipo

val label : string
val ff_f : elt -> t * t
val ff_arr : arr -> t * t
val df : t -> t -> t -> t
val dr : +Sipo (owl-base.Owl_algodiff_ops_builder_sig.Sig.Sipo)

Module type Sig.Sipo

val label : string
val ff_f : elt -> t * t
val ff_arr : arr -> t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Siso/index.html b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Siso/index.html index 234966ffe..a37d64acf 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Siso/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_algodiff_ops_builder_sig.Sig.Siso)

Module type Sig.Siso

val label : string
val ff_f : elt -> t
val ff_arr : arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
+Siso (owl-base.Owl_algodiff_ops_builder_sig.Sig.Siso)

Module type Sig.Siso

val label : string
val ff_f : elt -> t
val ff_arr : arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Sito/index.html b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Sito/index.html index 6edba1ba8..8f89575df 100644 --- a/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Sito/index.html +++ b/docs/owl-base/Owl_algodiff_ops_builder_sig/module-type-Sig/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_algodiff_ops_builder_sig.Sig.Sito)

Module type Sig.Sito

val label : string
val ff_f : elt -> t * t * t
val ff_arr : arr -> t * t * t
val df : t -> t -> t -> t
val dr : +Sito (owl-base.Owl_algodiff_ops_builder_sig.Sig.Sito)

Module type Sig.Sito

val label : string
val ff_f : elt -> t * t * t
val ff_arr : arr -> t * t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_ops_sig/index.html b/docs/owl-base/Owl_algodiff_ops_sig/index.html index 572a60399..f8df539d8 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/index.html @@ -1,2 +1,2 @@ -Owl_algodiff_ops_sig (owl-base.Owl_algodiff_ops_sig)

Module Owl_algodiff_ops_sig

module type Sig = sig ... end
+Owl_algodiff_ops_sig (owl-base.Owl_algodiff_ops_sig)

Module Owl_algodiff_ops_sig

module type Sig = sig ... end
diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Arr/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Arr/index.html index 25da15b09..4fa3ca2ce 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Arr/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_algodiff_ops_sig.Sig.Arr)

Module Sig.Arr

val empty : int array -> t
val zeros : int array -> t
val ones : int array -> t
val uniform : ?a:elt -> ?b:elt -> int array -> t
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> t
val shape : t -> int array
val numel : t -> int
val reset : t -> unit
val reshape : t -> int array -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
+Arr (owl-base.Owl_algodiff_ops_sig.Sig.Arr)

Module Sig.Arr

val empty : int array -> t
val zeros : int array -> t
val ones : int array -> t
val uniform : ?a:elt -> ?b:elt -> int array -> t
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> t
val shape : t -> int array
val numel : t -> int
val reset : t -> unit
val reshape : t -> int array -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/index.html index bcb0f6cda..1512ca287 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_algodiff_ops_sig.Sig.Builder)

Module Sig.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

+Builder (owl-base.Owl_algodiff_ops_sig.Sig.Builder)

Module Sig.Builder

Ops Builder
module type Siso = sig ... end
val build_siso : (module Siso) -> t -> t

build single input single output operations

module type Sipo = sig ... end
val build_sipo : (module Sipo) -> t -> t * t

build single input pair outputs operations

module type Sito = sig ... end
val build_sito : (module Sito) -> t -> t * t * t

build single input triple outputs operations

module type Siao = sig ... end
val build_siao : (module Siao) -> t -> t array

build single input array output operations

module type Piso = sig ... end
val build_piso : (module Piso) -> t -> t -> t

build pair inputs single output operations

module type Aiso = sig ... end
val build_aiso : (module Aiso) -> t array -> t

build array input single output operations

diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Aiso/index.html index 9e135502e..27618bfe8 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
+Aiso (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Aiso)

Module type Builder.Aiso

val label : string
val ff : t array -> t
val df : int list -> t -> t array -> t array -> t
val dr : int list -> t array -> t -> t Stdlib.ref -> t list
diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Piso/index.html index af9e04199..008ac801a 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : elt -> elt -> t
val ff_ab : elt -> arr -> t
val ff_ba : arr -> elt -> t
val ff_bb : arr -> arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
+Piso (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Piso)

Module type Builder.Piso

val label : string
val ff_aa : elt -> elt -> t
val ff_ab : elt -> arr -> t
val ff_ba : arr -> elt -> t
val ff_bb : arr -> arr -> t
val df_da : t -> t -> t -> t -> t
val df_db : t -> t -> t -> t -> t
val df_dab : t -> t -> t -> t -> t -> t
val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
val dr_a : t -> t -> t -> t Stdlib.ref -> t
val dr_b : t -> t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Siao/index.html index 35107814f..cec54b3fc 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : elt -> t array
val ff_arr : arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
+Siao (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Siao)

Module type Builder.Siao

val label : string
val ff_f : elt -> t array
val ff_arr : arr -> t array
val df : t array -> t -> t -> t array
val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Sipo/index.html index 28c701ecb..a08f759c9 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : elt -> t * t
val ff_arr : arr -> t * t
val df : t -> t -> t -> t
val dr : +Sipo (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Sipo)

Module type Builder.Sipo

val label : string
val ff_f : elt -> t * t
val ff_arr : arr -> t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Siso/index.html index 93d322694..22889e96c 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : elt -> t
val ff_arr : arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
+Siso (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Siso)

Module type Builder.Siso

val label : string
val ff_f : elt -> t
val ff_arr : arr -> t
val df : t -> t -> t -> t
val dr : t -> t -> t Stdlib.ref -> t
diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Sito/index.html index d344f93c0..82d48286f 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : elt -> t * t * t
val ff_arr : arr -> t * t * t
val df : t -> t -> t -> t
val dr : +Sito (owl-base.Owl_algodiff_ops_sig.Sig.Builder.Sito)

Module type Builder.Sito

val label : string
val ff_f : elt -> t * t * t
val ff_arr : arr -> t * t * t
val df : t -> t -> t -> t
val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Linalg/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Linalg/index.html index 91cd8a620..02f0c26ff 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_ops_sig.Sig.Linalg)

Module Sig.Linalg

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> t -> t * t * t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : t -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_ops_sig.Sig.Linalg)

Module Sig.Linalg

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val logdet : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val chol : ?upper:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val qr : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val lq : t -> t * t

Refer to :doc:`owl_dense_ndarray_generic`

val svd : ?thin:bool -> t -> t * t * t

Refer to :doc:`owl_dense_ndarray_generic`

val sylvester : t -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val lyapunov : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Mat/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Mat/index.html index fc1c74337..a071aa210 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_ops_sig.Sig.Mat)

Module Sig.Mat

val empty : int -> int -> t
val zeros : int -> int -> t
val eye : int -> t
val ones : int -> int -> t
val uniform : ?a:elt -> ?b:elt -> int -> int -> t
val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> t
val shape : t -> int * int
val numel : t -> int
val row_num : t -> int
val col_num : t -> int
val reset : t -> unit
val reshape : int -> int -> t -> t
val get : t -> int -> int -> t
val set : t -> int -> int -> t -> t
val row : t -> int -> t
val mean : t -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
val map_by_row : (t -> t) -> t -> t
val of_arrays : elt array array -> t
val init_2d : int -> int -> (int -> int -> t) -> t
val print : t -> unit
+Mat (owl-base.Owl_algodiff_ops_sig.Sig.Mat)

Module Sig.Mat

val empty : int -> int -> t
val zeros : int -> int -> t
val eye : int -> t
val ones : int -> int -> t
val uniform : ?a:elt -> ?b:elt -> int -> int -> t
val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> t
val shape : t -> int * int
val numel : t -> int
val row_num : t -> int
val col_num : t -> int
val reset : t -> unit
val reshape : int -> int -> t -> t
val get : t -> int -> int -> t
val set : t -> int -> int -> t -> t
val row : t -> int -> t
val mean : t -> t
val add : t -> t -> t
val sub : t -> t -> t
val mul : t -> t -> t
val div : t -> t -> t
val dot : t -> t -> t
val map_by_row : (t -> t) -> t -> t
val of_arrays : elt array array -> t
val init_2d : int -> int -> (int -> int -> t) -> t
val print : t -> unit
diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Maths/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Maths/index.html index 23265172a..afcb29ab4 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Maths/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_algodiff_ops_sig.Sig.Maths)

Module Sig.Maths

val (+) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val add : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val div : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : t -> t -> t

Refer to :doc:`owl_dense_matrix_generic`

val dot : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val round : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : t -> int -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : t -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> t -> t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : t array array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : t -> t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

+Maths (owl-base.Owl_algodiff_ops_sig.Sig.Maths)

Module Sig.Maths

val (+) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (-) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (/) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (*@) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val (**) : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val add : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sub : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mul : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val div : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val kron : t -> t -> t

Refer to :doc:`owl_dense_matrix_generic`

val dot : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val pow : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val min2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val max2 : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cross_entropy : t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val inv : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val neg : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val abs : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val signum : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val floor : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val ceil : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val round : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqr : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sqrt : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log2 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log10 : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val exp : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val cosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asin : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acos : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atan : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val asinh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val acosh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val atanh : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum : ?axis:int -> ?keep_dims:bool -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sum_reduce : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val mean : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val swap : int -> int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l1norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val l2norm_sqr' : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val sigmoid : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val relu : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dawsn : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softplus : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softsign : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val softmax : ?axis:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_item : t -> int -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_row : t -> int -> t

Refer to :doc:`owl_dense_ndarray_generic`

val concat : axis:int -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val split : axis:int -> int array -> t -> t array

Refer to :doc:`owl_dense_ndarray_generic`

val of_arrays : t array array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val to_arrays : t -> t array array

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : axis:int -> t array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> t -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diag : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val diagm : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val trace : t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val triu : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val tril : ?k:int -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/NN/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/NN/index.html index 515e4ce68..f13701d3c 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/NN/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_algodiff_ops_sig.Sig.NN)

Module Sig.NN

val dropout : ?rate:float -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dilated_conv1d : +NN (owl-base.Owl_algodiff_ops_sig.Sig.NN)

Module Sig.NN

val dropout : ?rate:float -> t -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

Refer to :doc:`owl_dense_ndarray_generic`

val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/index.html b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/index.html index 4414f94d6..0386bd9b7 100644 --- a/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_algodiff_ops_sig/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_algodiff_ops_sig.Sig)

Module type Owl_algodiff_ops_sig.Sig

type t
type elt
type arr
type op
module Builder : +Sig (owl-base.Owl_algodiff_ops_sig.Sig)

Module type Owl_algodiff_ops_sig.Sig

type t
type elt
type arr
type op
module Builder : Owl_algodiff_ops_builder_sig.Sig with type t := t and type elt := elt diff --git a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Linalg/index.html b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Linalg/index.html index b690d5cbd..29fecfc65 100644 --- a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_reverse.Make.C.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_reverse.Make.C.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Mat/index.html b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Mat/index.html index 384735004..c5919a838 100644 --- a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_reverse.Make.C.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_reverse.Make.C.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Scalar/index.html b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Scalar/index.html index 68c2466db..efae86ad4 100644 --- a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_reverse.Make.C.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_reverse.Make.C.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/index.html b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/index.html index d511b637f..c4db0b546 100644 --- a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/index.html +++ b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_reverse.Make.C.A)

Module C.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_reverse.Make.C.A)

Module C.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/index.html b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/index.html index a344f38fc..a225a2dfc 100644 --- a/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/index.html +++ b/docs/owl-base/Owl_algodiff_reverse/Make/argument-1-C/index.html @@ -1,2 +1,2 @@ -C (owl-base.Owl_algodiff_reverse.Make.C)

Parameter Make.C

include Owl_algodiff_core_sig.Sig
Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

val reverse_add : t -> t -> t
+C (owl-base.Owl_algodiff_reverse.Make.C)

Parameter Make.C

include Owl_algodiff_core_sig.Sig
Type definition
include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
Core functions
val tag : unit -> int

TODO

val primal : t -> t

TODO

val primal' : t -> t

TODO

val zero : t -> t

TODO

val reset_zero : t -> t

TODO

val tangent : t -> t

TODO

val adjref : t -> t Stdlib.ref

TODO

val adjval : t -> t

TODO

val shape : t -> int array

TODO

val is_float : t -> bool

TODO

val is_arr : t -> bool

TODO

val row_num : t -> int

number of rows

val col_num : t -> int

number of columns

val numel : t -> int

number of elements

val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

other functions, without tracking gradient

val clip_by_l2norm : A.elt -> t -> t

other functions, without tracking gradient

val copy_primal' : t -> t

TODO

val tile : t -> int array -> t

TODO

val repeat : t -> int array -> t

TODO

val pack_elt : A.elt -> t

convert from elt type to t type.

val unpack_elt : t -> A.elt

convert from t type to elt type.

val pack_flt : float -> t

convert from float type to t type.

val _f : float -> t

A shortcut function for F A.(float_to_elt x).

val unpack_flt : t -> float

convert from t type to float type.

val pack_arr : A.arr -> t

convert from arr type to t type.

val unpack_arr : t -> A.arr

convert from t type to arr type.

val deep_info : t -> string

TODO

val type_info : t -> string

TODO

val error_binop : string -> t -> t -> 'a

TODO

val error_uniop : string -> t -> 'a

TODO

val reverse_add : t -> t -> t
diff --git a/docs/owl-base/Owl_algodiff_reverse/Make/index.html b/docs/owl-base/Owl_algodiff_reverse/Make/index.html index be1acacee..1a7670f7d 100644 --- a/docs/owl-base/Owl_algodiff_reverse/Make/index.html +++ b/docs/owl-base/Owl_algodiff_reverse/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_algodiff_reverse.Make)

Module Owl_algodiff_reverse.Make

Parameters

module C : sig ... end

Signature

val reverse_push : C.t -> C.t -> unit
val reverse_prop : C.t -> C.t -> unit
val reverse_reset : C.t -> unit
+Make (owl-base.Owl_algodiff_reverse.Make)

Module Owl_algodiff_reverse.Make

Parameters

module C : sig ... end

Signature

val reverse_push : C.t -> C.t -> unit
val reverse_prop : C.t -> C.t -> unit
val reverse_reset : C.t -> unit
diff --git a/docs/owl-base/Owl_algodiff_reverse/index.html b/docs/owl-base/Owl_algodiff_reverse/index.html index fdfcf134a..fc5160e00 100644 --- a/docs/owl-base/Owl_algodiff_reverse/index.html +++ b/docs/owl-base/Owl_algodiff_reverse/index.html @@ -1,2 +1,2 @@ -Owl_algodiff_reverse (owl-base.Owl_algodiff_reverse)

Module Owl_algodiff_reverse

module Make (C : sig ... end) : sig ... end
+Owl_algodiff_reverse (owl-base.Owl_algodiff_reverse)

Module Owl_algodiff_reverse

module Make (C : sig ... end) : sig ... end
diff --git a/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Linalg/index.html b/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Linalg/index.html index cfcb22d77..dc71f9606 100644 --- a/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Linalg/index.html +++ b/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_algodiff_types.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_algodiff_types.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Mat/index.html b/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Mat/index.html index 3ede69c52..8a31b9e52 100644 --- a/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Mat/index.html +++ b/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_algodiff_types.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_algodiff_types.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Scalar/index.html b/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Scalar/index.html index 5d0c36b58..d6864c4fa 100644 --- a/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Scalar/index.html +++ b/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_algodiff_types.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_algodiff_types.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/index.html b/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/index.html index 1f0d09233..a4209d4bf 100644 --- a/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/index.html +++ b/docs/owl-base/Owl_algodiff_types/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_algodiff_types.Make.A)

Parameter Make.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_algodiff_types.Make.A)

Parameter Make.A

include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_algodiff_types/Make/index.html b/docs/owl-base/Owl_algodiff_types/Make/index.html index e5bf408a1..8215edeaa 100644 --- a/docs/owl-base/Owl_algodiff_types/Make/index.html +++ b/docs/owl-base/Owl_algodiff_types/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_algodiff_types.Make)

Module Owl_algodiff_types.Make

Parameters

Signature

type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
+Make (owl-base.Owl_algodiff_types.Make)

Module Owl_algodiff_types.Make

Parameters

Signature

type t =
  1. | F of A.elt
  2. | Arr of A.arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
diff --git a/docs/owl-base/Owl_algodiff_types/index.html b/docs/owl-base/Owl_algodiff_types/index.html index bb130e769..634ec0940 100644 --- a/docs/owl-base/Owl_algodiff_types/index.html +++ b/docs/owl-base/Owl_algodiff_types/index.html @@ -1,4 +1,4 @@ -Owl_algodiff_types (owl-base.Owl_algodiff_types)

Module Owl_algodiff_types

module Make +Owl_algodiff_types (owl-base.Owl_algodiff_types)

Module Owl_algodiff_types

diff --git a/docs/owl-base/Owl_algodiff_types_sig/index.html b/docs/owl-base/Owl_algodiff_types_sig/index.html index 6e46cb609..c99f12551 100644 --- a/docs/owl-base/Owl_algodiff_types_sig/index.html +++ b/docs/owl-base/Owl_algodiff_types_sig/index.html @@ -1,2 +1,2 @@ -Owl_algodiff_types_sig (owl-base.Owl_algodiff_types_sig)

Module Owl_algodiff_types_sig

module type Sig = sig ... end
+Owl_algodiff_types_sig (owl-base.Owl_algodiff_types_sig)

Module Owl_algodiff_types_sig

module type Sig = sig ... end
diff --git a/docs/owl-base/Owl_algodiff_types_sig/module-type-Sig/index.html b/docs/owl-base/Owl_algodiff_types_sig/module-type-Sig/index.html index 657bca386..c7f8c9a77 100644 --- a/docs/owl-base/Owl_algodiff_types_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_algodiff_types_sig/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_algodiff_types_sig.Sig)

Module type Owl_algodiff_types_sig.Sig

type elt
type arr
type t =
  1. | F of elt
  2. | Arr of arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
+Sig (owl-base.Owl_algodiff_types_sig.Sig)

Module type Owl_algodiff_types_sig.Sig

type elt
type arr
type t =
  1. | F of elt
  2. | Arr of arr
  3. | DF of t * t * int
  4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
and register = t list -> t list
and label = string * t list
and op = adjoint * register * label
diff --git a/docs/owl-base/Owl_base/index.html b/docs/owl-base/Owl_base/index.html index 1d1e67943..b3041363d 100644 --- a/docs/owl-base/Owl_base/index.html +++ b/docs/owl-base/Owl_base/index.html @@ -1,2 +1,2 @@ -Owl_base (owl-base.Owl_base)

Module Owl_base

module Const = Owl_const
module Maths = Owl_base_maths
module Stats = Owl_base_stats
module Complex = Owl_base_complex
module Quadrature = Owl_maths_quadrature
module Root = Owl_maths_root
module Graph = Owl_graph
module Lazy = Owl_lazy
module View = Owl_view
module Log = Owl_log
module Utils = Owl_utils
module Computation = Owl_computation
module Countmin_sketch = Owl_countmin_sketch
module HeavyHitters_sketch = Owl_heavyhitters_sketch
+Owl_base (owl-base.Owl_base)

Module Owl_base

module Const = Owl_const
module Maths = Owl_base_maths
module Stats = Owl_base_stats
module Complex = Owl_base_complex
module Quadrature = Owl_maths_quadrature
module Root = Owl_maths_root
module Graph = Owl_graph
module Lazy = Owl_lazy
module View = Owl_view
module Log = Owl_log
module Utils = Owl_utils
module Computation = Owl_computation
module Countmin_sketch = Owl_countmin_sketch
module HeavyHitters_sketch = Owl_heavyhitters_sketch
diff --git a/docs/owl-base/Owl_base_algodiff_primal_ops/D/Linalg/index.html b/docs/owl-base/Owl_base_algodiff_primal_ops/D/Linalg/index.html index bc526e74d..e6fd7f97e 100644 --- a/docs/owl-base/Owl_base_algodiff_primal_ops/D/Linalg/index.html +++ b/docs/owl-base/Owl_base_algodiff_primal_ops/D/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_base_algodiff_primal_ops.D.Linalg)

Module D.Linalg

include module type of struct include Owl_base_linalg_d end
type elt = float
type complex_mat = Owl_base_dense_matrix_z.mat
type int32_mat = +Linalg (owl-base.Owl_base_algodiff_primal_ops.D.Linalg)

Module D.Linalg

include module type of struct include Owl_base_linalg_d end
type elt = float
type complex_mat = Owl_base_dense_matrix_z.mat
type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_base_dense_matrix_generic.t
include Owl_base_linalg_intf.Common with type elt := elt and type mat := mat diff --git a/docs/owl-base/Owl_base_algodiff_primal_ops/D/Mat/index.html b/docs/owl-base/Owl_base_algodiff_primal_ops/D/Mat/index.html index 5ce77327e..189fa37b9 100644 --- a/docs/owl-base/Owl_base_algodiff_primal_ops/D/Mat/index.html +++ b/docs/owl-base/Owl_base_algodiff_primal_ops/D/Mat/index.html @@ -1,5 +1,5 @@ -Mat (owl-base.Owl_base_algodiff_primal_ops.D.Mat)

Module D.Mat

val eye : +Mat (owl-base.Owl_base_algodiff_primal_ops.D.Mat)

Module D.Mat

val eye : int -> (float, Stdlib.Bigarray.float64_elt) Owl_base_dense_matrix_d.M.t
val tril : ?k:int -> diff --git a/docs/owl-base/Owl_base_algodiff_primal_ops/D/index.html b/docs/owl-base/Owl_base_algodiff_primal_ops/D/index.html index 5f0a6c622..d9de26800 100644 --- a/docs/owl-base/Owl_base_algodiff_primal_ops/D/index.html +++ b/docs/owl-base/Owl_base_algodiff_primal_ops/D/index.html @@ -1,5 +1,5 @@ -D (owl-base.Owl_base_algodiff_primal_ops.D)

Module Owl_base_algodiff_primal_ops.D

include module type of struct include Owl_base_dense_ndarray_d end
type elt = float
type arr = +D (owl-base.Owl_base_algodiff_primal_ops.D)

Module Owl_base_algodiff_primal_ops.D

include module type of struct include Owl_base_dense_ndarray_d end
type elt = float
type arr = (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_algodiff_primal_ops/S/Linalg/index.html b/docs/owl-base/Owl_base_algodiff_primal_ops/S/Linalg/index.html index 57c449d7e..67a6fe26e 100644 --- a/docs/owl-base/Owl_base_algodiff_primal_ops/S/Linalg/index.html +++ b/docs/owl-base/Owl_base_algodiff_primal_ops/S/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_base_algodiff_primal_ops.S.Linalg)

Module S.Linalg

include module type of struct include Owl_base_linalg_s end
type elt = float
type complex_mat = Owl_base_dense_matrix_c.mat
type int32_mat = +Linalg (owl-base.Owl_base_algodiff_primal_ops.S.Linalg)

Module S.Linalg

include module type of struct include Owl_base_linalg_s end
type elt = float
type complex_mat = Owl_base_dense_matrix_c.mat
type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_base_dense_matrix_generic.t
include Owl_base_linalg_intf.Common with type elt := elt and type mat := mat diff --git a/docs/owl-base/Owl_base_algodiff_primal_ops/S/Mat/index.html b/docs/owl-base/Owl_base_algodiff_primal_ops/S/Mat/index.html index 430411032..00dc0e6b5 100644 --- a/docs/owl-base/Owl_base_algodiff_primal_ops/S/Mat/index.html +++ b/docs/owl-base/Owl_base_algodiff_primal_ops/S/Mat/index.html @@ -1,5 +1,5 @@ -Mat (owl-base.Owl_base_algodiff_primal_ops.S.Mat)

Module S.Mat

val eye : +Mat (owl-base.Owl_base_algodiff_primal_ops.S.Mat)

Module S.Mat

val eye : int -> (float, Stdlib.Bigarray.float32_elt) Owl_base_dense_matrix_s.M.t
val tril : ?k:int -> diff --git a/docs/owl-base/Owl_base_algodiff_primal_ops/S/index.html b/docs/owl-base/Owl_base_algodiff_primal_ops/S/index.html index 15c5db41b..1f4d9077b 100644 --- a/docs/owl-base/Owl_base_algodiff_primal_ops/S/index.html +++ b/docs/owl-base/Owl_base_algodiff_primal_ops/S/index.html @@ -1,5 +1,5 @@ -S (owl-base.Owl_base_algodiff_primal_ops.S)

Module Owl_base_algodiff_primal_ops.S

include module type of struct include Owl_base_dense_ndarray.S end
include module type of struct include Owl_base_dense_ndarray_s end
type elt = float
type arr = +S (owl-base.Owl_base_algodiff_primal_ops.S)

Module Owl_base_algodiff_primal_ops.S

include module type of struct include Owl_base_dense_ndarray.S end
include module type of struct include Owl_base_dense_ndarray_s end
type elt = float
type arr = (float, Stdlib.Bigarray.float32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_algodiff_primal_ops/index.html b/docs/owl-base/Owl_base_algodiff_primal_ops/index.html index 7e3299e06..56d237cf7 100644 --- a/docs/owl-base/Owl_base_algodiff_primal_ops/index.html +++ b/docs/owl-base/Owl_base_algodiff_primal_ops/index.html @@ -1,2 +1,2 @@ -Owl_base_algodiff_primal_ops (owl-base.Owl_base_algodiff_primal_ops)

Module Owl_base_algodiff_primal_ops

module S : sig ... end
module D : sig ... end
+Owl_base_algodiff_primal_ops (owl-base.Owl_base_algodiff_primal_ops)

Module Owl_base_algodiff_primal_ops

module S : sig ... end
module D : sig ... end
diff --git a/docs/owl-base/Owl_base_complex/index.html b/docs/owl-base/Owl_base_complex/index.html index 5245f0777..f87b83785 100644 --- a/docs/owl-base/Owl_base_complex/index.html +++ b/docs/owl-base/Owl_base_complex/index.html @@ -1,2 +1,2 @@ -Owl_base_complex (owl-base.Owl_base_complex)

Module Owl_base_complex

Type definition and constants
type t = Stdlib.Complex.t

Type definition of a complex number.

val zero : t

Constant value zero.

val one : t

Constant value one.

val i : t

Constant value i.

Unary functions
val neg : t -> t

TODO

val abs : t -> float

TODO

val abs2 : t -> float

TODO

val logabs : t -> float

TODO

val conj : t -> t

TODO

val inv : t -> t

TODO

val sqrt : t -> t

TODO

val exp : t -> t

TODO

val exp2 : t -> t

TODO

val exp10 : t -> t

TODO

val expm1 : t -> t

TODO

val log : t -> t

TODO

val log2 : t -> t

TODO

val log10 : t -> t

TODO

val log1p : t -> t

TODO

val sin : t -> t

TODO

val cos : t -> t

TODO

val tan : t -> t

TODO

val cot : t -> t

TODO

val sec : t -> t

TODO

val csc : t -> t

TODO

val sinh : t -> t

TODO

val cosh : t -> t

TODO

val tanh : t -> t

TODO

val sech : t -> t

TODO

val csch : t -> t

TODO

val coth : t -> t

TODO

val asin : t -> t

TODO

val acos : t -> t

TODO

val atan : t -> t

TODO

val asec : t -> t

TODO

val acsc : t -> t

TODO

val acot : t -> t

TODO

val asinh : t -> t

TODO

val acosh : t -> t

TODO

val atanh : t -> t

TODO

val asech : t -> t

TODO

val acsch : t -> t

TODO

val acoth : t -> t

TODO

val arg : t -> float

arg x returns the angle of a complex number x.

val phase : t -> float

phase x returns the phase of a complex number x.

val floor : t -> t

floor x

val ceil : t -> t

ceil x

val round : t -> t

round x

val trunc : t -> t

trunc x

val fix : t -> t

fix x

Binary functions
val add : t -> t -> t

TODO

val sub : t -> t -> t

TODO

val mul : t -> t -> t

TODO

val div : t -> t -> t

TODO

val add_re : t -> float -> t

TODO

val add_im : t -> float -> t

TODO

val sub_re : t -> float -> t

TODO

val sub_im : t -> float -> t

TODO

val mul_re : t -> float -> t

TODO

val mul_im : t -> float -> t

TODO

val div_re : t -> float -> t

TODO

val div_im : t -> float -> t

TODO

val pow : t -> t -> t

TODO

val polar : float -> float -> t

TODO

val rect : float -> float -> t

rect r phi return a complex number with polar coordinates r and phi.

Comparison functions
val equal : t -> t -> bool

TODO

val not_equal : t -> t -> bool

TODO

val less : t -> t -> bool

TODO

val greater : t -> t -> bool

TODO

val less_equal : t -> t -> bool

TODO

val greater_equal : t -> t -> bool

TODO

Helper functions
val complex : float -> float -> t

complex re im returns a complex number {re; im}.

val of_tuple : (float * float) -> t

of_tuple (re, im) returns a complex number {re; im}.

val to_tuple : t -> float * float

to_tuple x converts a complex number to tuple (x.re; x.im).

val is_nan : t -> bool

is_nan x returns true if x.re is nan or x.im is nan.

val is_inf : t -> bool

is_inf x returns true if either x.re or x.im is infinity or neg_infinity.

val is_normal : t -> bool

is_normal x returns true if both x.re and x.im are normal.

+Owl_base_complex (owl-base.Owl_base_complex)

Module Owl_base_complex

Type definition and constants
type t = Stdlib.Complex.t

Type definition for a complex number.

val zero : t

Constant value representing the complex number zero (0 + 0i).

val one : t

Constant value representing the complex number one (1 + 0i).

val i : t

Constant value representing the imaginary unit i (0 + 1i).

Unary functions
val neg : t -> t

neg z returns the negation of the complex number z. If z = a + bi, then neg z = -a - bi.

val abs : t -> float

abs z returns the magnitude (absolute value) of the complex number z. This is computed as sqrt(Re(z)^2 + Im(z)^2).

val abs2 : t -> float

abs2 z returns the squared magnitude of the complex number z. This is computed as Re(z)^2 + Im(z)^2.

val logabs : t -> float

logabs z returns the natural logarithm of the magnitude of the complex number z.

val conj : t -> t

conj z returns the complex conjugate of the complex number z. If z = a + bi, then conj z = a - bi.

val inv : t -> t

inv z returns the multiplicative inverse of the complex number z. This is computed as 1 / z.

val sqrt : t -> t

sqrt z returns the square root of the complex number z.

val exp : t -> t

exp z returns the exponential of the complex number z, calculated as e^z.

val exp2 : t -> t

exp2 z returns 2 raised to the power of the complex number z, calculated as 2^z.

val exp10 : t -> t

exp10 z returns 10 raised to the power of the complex number z, calculated as 10^z.

val expm1 : t -> t

expm1 z returns the value of exp(z) - 1, providing a more accurate result for small values of z.

val log : t -> t

log z returns the natural logarithm of the complex number z.

val log2 : t -> t

log2 z returns the base-2 logarithm of the complex number z.

val log10 : t -> t

log10 z returns the base-10 logarithm of the complex number z.

val log1p : t -> t

log1p z returns the natural logarithm of (1 + z), providing a more accurate result for small values of z.

val sin : t -> t

sin z returns the sine of the complex number z.

val cos : t -> t

cos z returns the cosine of the complex number z.

val tan : t -> t

tan z returns the tangent of the complex number z.

val cot : t -> t

cot z returns the cotangent of the complex number z.

val sec : t -> t

sec z returns the secant of the complex number z.

val csc : t -> t

csc z returns the cosecant of the complex number z.

val sinh : t -> t

sinh z returns the hyperbolic sine of the complex number z.

val cosh : t -> t

cosh z returns the hyperbolic cosine of the complex number z.

val tanh : t -> t

tanh z returns the hyperbolic tangent of the complex number z.

val sech : t -> t

sech z returns the hyperbolic secant of the complex number z.

val csch : t -> t

csch z returns the hyperbolic cosecant of the complex number z.

val coth : t -> t

coth z returns the hyperbolic cotangent of the complex number z.

val asin : t -> t

asin z returns the arcsine of the complex number z.

val acos : t -> t

acos z returns the arccosine of the complex number z.

val atan : t -> t

atan z returns the arctangent of the complex number z.

val asec : t -> t

asec z returns the arcsecant of the complex number z.

val acsc : t -> t

acsc z returns the arccosecant of the complex number z.

val acot : t -> t

acot z returns the arccotangent of the complex number z.

val asinh : t -> t

asinh z returns the inverse hyperbolic sine of the complex number z.

val acosh : t -> t

acosh z returns the inverse hyperbolic cosine of the complex number z.

val atanh : t -> t

atanh z returns the inverse hyperbolic tangent of the complex number z.

val asech : t -> t

asech z returns the inverse hyperbolic secant of the complex number z.

val acsch : t -> t

acsch z returns the inverse hyperbolic cosecant of the complex number z.

val acoth : t -> t

acoth z returns the inverse hyperbolic cotangent of the complex number z.

val arg : t -> float

arg x returns the angle of a complex number x.

val phase : t -> float

phase x returns the phase of a complex number x.

val floor : t -> t

floor x

val ceil : t -> t

ceil x

val round : t -> t

round x

val trunc : t -> t

trunc x

val fix : t -> t

fix x

Binary functions
val add : t -> t -> t

add z1 z2 returns the sum of the complex numbers z1 and z2.

val sub : t -> t -> t

sub z1 z2 returns the difference of the complex numbers z1 and z2.

val mul : t -> t -> t

mul z1 z2 returns the product of the complex numbers z1 and z2.

val div : t -> t -> t

div z1 z2 returns the quotient of the complex numbers z1 and z2.

val add_re : t -> float -> t

add_re z r adds the real number r to the real part of the complex number z. Returns a new complex number with the real part increased by r.

val add_im : t -> float -> t

add_im z i adds the real number i to the imaginary part of the complex number z. Returns a new complex number with the imaginary part increased by i.

val sub_re : t -> float -> t

sub_re z r subtracts the real number r from the real part of the complex number z. Returns a new complex number with the real part decreased by r.

val sub_im : t -> float -> t

sub_im z i subtracts the real number i from the imaginary part of the complex number z. Returns a new complex number with the imaginary part decreased by i.

val mul_re : t -> float -> t

mul_re z r multiplies the real part of the complex number z by the real number r. Returns a new complex number with the real part scaled by r.

val mul_im : t -> float -> t

mul_im z i multiplies the imaginary part of the complex number z by the real number i. Returns a new complex number with the imaginary part scaled by i.

val div_re : t -> float -> t

div_re z r divides the real part of the complex number z by the real number r. Returns a new complex number with the real part divided by r.

val div_im : t -> float -> t

div_im z i divides the imaginary part of the complex number z by the real number i. Returns a new complex number with the imaginary part divided by i.

val pow : t -> t -> t

pow z1 z2 raises the complex number z1 to the power of z2. Returns a new complex number representing z1 raised to z2.

val polar : float -> float -> t

polar r theta creates a complex number from the polar coordinates r (magnitude) and theta (angle in radians). Returns a new complex number.

val rect : float -> float -> t

rect r phi returns a complex number with polar coordinates r and phi. Equivalent to polar r phi.

Comparison functions
val equal : t -> t -> bool

equal z1 z2 returns true if the complex numbers z1 and z2 are equal, false otherwise.

val not_equal : t -> t -> bool

not_equal z1 z2 returns true if the complex numbers z1 and z2 are not equal, false otherwise.

val less : t -> t -> bool

less z1 z2 returns true if the magnitude of the complex number z1 is less than that of z2.

val greater : t -> t -> bool

greater z1 z2 returns true if the magnitude of the complex number z1 is greater than that of z2.

val less_equal : t -> t -> bool

less_equal z1 z2 returns true if the magnitude of the complex number z1 is less than or equal to that of z2.

val greater_equal : t -> t -> bool

greater_equal z1 z2 returns true if the magnitude of the complex number z1 is greater than or equal to that of z2.

Helper functions
val complex : float -> float -> t

complex re im returns a complex number {re; im}.

val of_tuple : (float * float) -> t

of_tuple (re, im) returns a complex number {re; im}.

val to_tuple : t -> float * float

to_tuple x converts a complex number to tuple (x.re; x.im).

val is_nan : t -> bool

is_nan x returns true if x.re is nan or x.im is nan.

val is_inf : t -> bool

is_inf x returns true if either x.re or x.im is infinity or neg_infinity.

val is_normal : t -> bool

is_normal x returns true if both x.re and x.im are normal.

diff --git a/docs/owl-base/Owl_base_dense_common/index.html b/docs/owl-base/Owl_base_dense_common/index.html index c5853bfe2..3550299c5 100644 --- a/docs/owl-base/Owl_base_dense_common/index.html +++ b/docs/owl-base/Owl_base_dense_common/index.html @@ -1,5 +1,5 @@ -Owl_base_dense_common (owl-base.Owl_base_dense_common)

Module Owl_base_dense_common

val _max_val_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a
val _min_val_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a
val _max_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _min_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _add_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _sub_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _mul_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _div_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _inv_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _neg_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _abs_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _log_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _log2_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _log10_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _log1p_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _exp_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _exp2_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _exp10_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _expm1_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _re_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> float
val _im_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> float
val _sqr_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _sqrt_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _mean_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> int -> 'a
val _pow_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _sin_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _cos_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _tan_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _asin_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _acos_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _atan_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _sinh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _cosh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _tanh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _asinh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _acosh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _atanh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _scale_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a -> 'a
val _conj_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _ceil_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _floor_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _round_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _trunc_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _fix_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _is_nan_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> bool
val _is_inf_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> bool
val _is_normal_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> bool
val _float_typ_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
val _uniform_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a -> 'a
val _gaussian_elt : +Owl_base_dense_common (owl-base.Owl_base_dense_common)

Module Owl_base_dense_common

val _max_val_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a
val _min_val_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a
val _max_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _min_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _add_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _sub_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _mul_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _div_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _inv_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _neg_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _abs_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _log_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _log2_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _log10_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _log1p_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _exp_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _exp2_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _exp10_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _expm1_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _re_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> float
val _im_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> float
val _sqr_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _sqrt_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _mean_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> int -> 'a
val _pow_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a
val _sin_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _cos_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _tan_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _asin_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _acos_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _atan_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _sinh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _cosh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _tanh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _asinh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _acosh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _atanh_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _scale_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a -> 'a
val _conj_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _ceil_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _floor_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _round_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _trunc_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _fix_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a
val _is_nan_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> bool
val _is_inf_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> bool
val _is_normal_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> bool
val _float_typ_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
val _uniform_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> 'a -> 'a
val _gaussian_elt : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> 'a -> diff --git a/docs/owl-base/Owl_base_dense_matrix_c/index.html b/docs/owl-base/Owl_base_dense_matrix_c/index.html index 39cb9bf7a..3f2d13ce3 100644 --- a/docs/owl-base/Owl_base_dense_matrix_c/index.html +++ b/docs/owl-base/Owl_base_dense_matrix_c/index.html @@ -1,2 +1,2 @@ -Owl_base_dense_matrix_c (owl-base.Owl_base_dense_matrix_c)

Module Owl_base_dense_matrix_c

include module type of struct include M end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

type elt = Stdlib.Complex.t
type mat = (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) M.t
val eye : int -> (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) M.t
+Owl_base_dense_matrix_c (owl-base.Owl_base_dense_matrix_c)

Module Owl_base_dense_matrix_c

include module type of struct include M end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

type elt = Stdlib.Complex.t
type mat = (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) M.t
val eye : int -> (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) M.t
diff --git a/docs/owl-base/Owl_base_dense_matrix_d/index.html b/docs/owl-base/Owl_base_dense_matrix_d/index.html index 53dc34504..1e84e47ee 100644 --- a/docs/owl-base/Owl_base_dense_matrix_d/index.html +++ b/docs/owl-base/Owl_base_dense_matrix_d/index.html @@ -1,2 +1,2 @@ -Owl_base_dense_matrix_d (owl-base.Owl_base_dense_matrix_d)

Module Owl_base_dense_matrix_d

include module type of struct include M end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

type elt = float
type mat = (float, Stdlib.Bigarray.float64_elt) M.t
val eye : int -> (float, Stdlib.Bigarray.float64_elt) M.t
+Owl_base_dense_matrix_d (owl-base.Owl_base_dense_matrix_d)

Module Owl_base_dense_matrix_d

include module type of struct include M end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

type elt = float
type mat = (float, Stdlib.Bigarray.float64_elt) M.t
val eye : int -> (float, Stdlib.Bigarray.float64_elt) M.t
diff --git a/docs/owl-base/Owl_base_dense_matrix_generic/index.html b/docs/owl-base/Owl_base_dense_matrix_generic/index.html index f913fc687..1c0d49585 100644 --- a/docs/owl-base/Owl_base_dense_matrix_generic/index.html +++ b/docs/owl-base/Owl_base_dense_matrix_generic/index.html @@ -1,2 +1,2 @@ -Owl_base_dense_matrix_generic (owl-base.Owl_base_dense_matrix_generic)

Module Owl_base_dense_matrix_generic

Matrix module: including creation, manipulation, and various vectorised mathematical operations.

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val eye : ('a, 'b) Stdlib.Bigarray.kind -> int -> ('a, 'b) t

eye m creates an m by m identity matrix.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

+Owl_base_dense_matrix_generic (owl-base.Owl_base_dense_matrix_generic)

Module Owl_base_dense_matrix_generic

Matrix module: including creation, manipulation, and various vectorised mathematical operations.

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val eye : ('a, 'b) Stdlib.Bigarray.kind -> int -> ('a, 'b) t

eye m creates an m by m identity matrix.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

diff --git a/docs/owl-base/Owl_base_dense_matrix_intf/index.html b/docs/owl-base/Owl_base_dense_matrix_intf/index.html index e69c98cd6..5b2735832 100644 --- a/docs/owl-base/Owl_base_dense_matrix_intf/index.html +++ b/docs/owl-base/Owl_base_dense_matrix_intf/index.html @@ -1,2 +1,2 @@ -Owl_base_dense_matrix_intf (owl-base.Owl_base_dense_matrix_intf)

Module Owl_base_dense_matrix_intf

module type Common = sig ... end
+Owl_base_dense_matrix_intf (owl-base.Owl_base_dense_matrix_intf)

Module Owl_base_dense_matrix_intf

module type Common = sig ... end
diff --git a/docs/owl-base/Owl_base_dense_matrix_intf/module-type-Common/index.html b/docs/owl-base/Owl_base_dense_matrix_intf/module-type-Common/index.html index c02602723..849b2db98 100644 --- a/docs/owl-base/Owl_base_dense_matrix_intf/module-type-Common/index.html +++ b/docs/owl-base/Owl_base_dense_matrix_intf/module-type-Common/index.html @@ -1,2 +1,2 @@ -Common (owl-base.Owl_base_dense_matrix_intf.Common)

Module type Owl_base_dense_matrix_intf.Common

type elt
type arr
val diagm : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
+Common (owl-base.Owl_base_dense_matrix_intf.Common)

Module type Owl_base_dense_matrix_intf.Common

type elt
type arr
val diagm : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
diff --git a/docs/owl-base/Owl_base_dense_matrix_s/index.html b/docs/owl-base/Owl_base_dense_matrix_s/index.html index ad938dd02..48963dd5a 100644 --- a/docs/owl-base/Owl_base_dense_matrix_s/index.html +++ b/docs/owl-base/Owl_base_dense_matrix_s/index.html @@ -1,2 +1,2 @@ -Owl_base_dense_matrix_s (owl-base.Owl_base_dense_matrix_s)

Module Owl_base_dense_matrix_s

include module type of struct include M end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

type elt = float
type mat = (float, Stdlib.Bigarray.float32_elt) M.t
val eye : int -> (float, Stdlib.Bigarray.float32_elt) M.t
+Owl_base_dense_matrix_s (owl-base.Owl_base_dense_matrix_s)

Module Owl_base_dense_matrix_s

include module type of struct include M end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

type elt = float
type mat = (float, Stdlib.Bigarray.float32_elt) M.t
val eye : int -> (float, Stdlib.Bigarray.float32_elt) M.t
diff --git a/docs/owl-base/Owl_base_dense_matrix_z/index.html b/docs/owl-base/Owl_base_dense_matrix_z/index.html index 4d5d69a20..ee20be29d 100644 --- a/docs/owl-base/Owl_base_dense_matrix_z/index.html +++ b/docs/owl-base/Owl_base_dense_matrix_z/index.html @@ -1,3 +1,3 @@ -Owl_base_dense_matrix_z (owl-base.Owl_base_dense_matrix_z)

Module Owl_base_dense_matrix_z

include module type of struct include M end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

type elt = Stdlib.Complex.t
type mat = (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) M.t
type cast_mat = +Owl_base_dense_matrix_z (owl-base.Owl_base_dense_matrix_z)

Module Owl_base_dense_matrix_z

include module type of struct include M end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

N-dimensional array type, i.e. Bigarray Genarray type.

val diagm : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val tril : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val triu : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

type elt = Stdlib.Complex.t
type mat = (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) M.t
type cast_mat = (float, Stdlib.Bigarray.float64_elt) Owl_base_dense_matrix_generic.t
val eye : int -> (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) M.t
diff --git a/docs/owl-base/Owl_base_dense_ndarray/C/index.html b/docs/owl-base/Owl_base_dense_ndarray/C/index.html index 50ee6e73e..cb8ac3923 100644 --- a/docs/owl-base/Owl_base_dense_ndarray/C/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray/C/index.html @@ -1,5 +1,5 @@ -C (owl-base.Owl_base_dense_ndarray.C)

Module Owl_base_dense_ndarray.C

include module type of struct include Owl_base_dense_ndarray_c end
type elt = Stdlib.Complex.t
type arr = +C (owl-base.Owl_base_dense_ndarray.C)

Module Owl_base_dense_ndarray.C

include module type of struct include Owl_base_dense_ndarray_c end
type elt = Stdlib.Complex.t
type arr = (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_dense_ndarray/D/index.html b/docs/owl-base/Owl_base_dense_ndarray/D/index.html index 4f9168bc8..85395fda2 100644 --- a/docs/owl-base/Owl_base_dense_ndarray/D/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray/D/index.html @@ -1,5 +1,5 @@ -D (owl-base.Owl_base_dense_ndarray.D)

Module Owl_base_dense_ndarray.D

include module type of struct include Owl_base_dense_ndarray_d end
type elt = float
type arr = +D (owl-base.Owl_base_dense_ndarray.D)

Module Owl_base_dense_ndarray.D

include module type of struct include Owl_base_dense_ndarray_d end
type elt = float
type arr = (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_dense_ndarray/Generic/index.html b/docs/owl-base/Owl_base_dense_ndarray/Generic/index.html index 50cef1cbc..89683b5e4 100644 --- a/docs/owl-base/Owl_base_dense_ndarray/Generic/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray/Generic/index.html @@ -1,5 +1,5 @@ -Generic (owl-base.Owl_base_dense_ndarray.Generic)

Module Owl_base_dense_ndarray.Generic

include module type of struct include Owl_base_dense_ndarray_generic end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

Refer to :doc:`owl_dense_ndarray_generic`

type ('a, 'b) kind = ('a, 'b) Stdlib.Bigarray.kind

Refer to :doc:`owl_dense_ndarray_generic`

Create Ndarrays
val empty : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val create : ('a, 'b) kind -> int array -> 'a -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val init : ('a, 'b) kind -> int array -> (int -> 'a) -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val init_nd : ('a, 'b) kind -> int array -> (int array -> 'a) -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val zeros : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val ones : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val eye : ('a, 'b) kind -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val uniform : ('a, 'b) kind -> ?a:'a -> ?b:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val gaussian : ('a, 'b) kind -> ?mu:'a -> ?sigma:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val sequential : ('a, 'b) kind -> ?a:'a -> ?step:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val bernoulli : ('a, 'b) kind -> ?p:float -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

Obtain basic properties
val shape : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val num_dims : ('a, 'b) t -> int

Refer to :doc:`owl_dense_ndarray_generic`

val nth_dim : ('a, 'b) t -> int -> int

Refer to :doc:`owl_dense_ndarray_generic`

val numel : ('a, 'b) t -> int

Refer to :doc:`owl_dense_ndarray_generic`

val kind : ('a, 'b) t -> ('a, 'b) kind

Refer to :doc:`owl_dense_ndarray_generic`

val strides : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val slice_size : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

Manipulate Ndarrays
val get : ('a, 'b) t -> int array -> 'a

Refer to :doc:`owl_dense_ndarray_generic`

val set : ('a, 'b) t -> int array -> 'a -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> ('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val reset : ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val fill : ('a, 'b) t -> 'a -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val copy : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val reverse : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val tile : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val repeat : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val pad : ?v:'a -> int list list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val squeeze : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val expand : ?hi:bool -> ('a, 'b) t -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val split : ?axis:int -> int array -> ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_ndarray_generic`

val draw : ?axis:int -> ('a, 'b) t -> int -> ('a, 'b) t * int array

Refer to :doc:`owl_dense_ndarray_generic`

val one_hot : int -> ('a, 'b) t -> ('a, 'b) t

TODO: not implemented

Iterate array elements
val iteri : (int -> 'a -> unit) -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val iter : ('a -> unit) -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val mapi : (int -> 'a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val map : ('a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val filteri : (int -> 'a -> bool) -> ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val filter : ('a -> bool) -> ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val foldi : +Generic (owl-base.Owl_base_dense_ndarray.Generic)

Module Owl_base_dense_ndarray.Generic

include module type of struct include Owl_base_dense_ndarray_generic end

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

Refer to :doc:`owl_dense_ndarray_generic`

type ('a, 'b) kind = ('a, 'b) Stdlib.Bigarray.kind

Refer to :doc:`owl_dense_ndarray_generic`

Create Ndarrays
val empty : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val create : ('a, 'b) kind -> int array -> 'a -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val init : ('a, 'b) kind -> int array -> (int -> 'a) -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val init_nd : ('a, 'b) kind -> int array -> (int array -> 'a) -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val zeros : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val ones : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val eye : ('a, 'b) kind -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val uniform : ('a, 'b) kind -> ?a:'a -> ?b:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val gaussian : ('a, 'b) kind -> ?mu:'a -> ?sigma:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val sequential : ('a, 'b) kind -> ?a:'a -> ?step:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val bernoulli : ('a, 'b) kind -> ?p:float -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

Obtain basic properties
val shape : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val num_dims : ('a, 'b) t -> int

Refer to :doc:`owl_dense_ndarray_generic`

val nth_dim : ('a, 'b) t -> int -> int

Refer to :doc:`owl_dense_ndarray_generic`

val numel : ('a, 'b) t -> int

Refer to :doc:`owl_dense_ndarray_generic`

val kind : ('a, 'b) t -> ('a, 'b) kind

Refer to :doc:`owl_dense_ndarray_generic`

val strides : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val slice_size : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

Manipulate Ndarrays
val get : ('a, 'b) t -> int array -> 'a

Refer to :doc:`owl_dense_ndarray_generic`

val set : ('a, 'b) t -> int array -> 'a -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> ('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val reset : ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val fill : ('a, 'b) t -> 'a -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val copy : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val reverse : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val tile : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val repeat : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val pad : ?v:'a -> int list list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val squeeze : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val expand : ?hi:bool -> ('a, 'b) t -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val split : ?axis:int -> int array -> ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_ndarray_generic`

val draw : ?axis:int -> ('a, 'b) t -> int -> ('a, 'b) t * int array

Refer to :doc:`owl_dense_ndarray_generic`

val one_hot : int -> ('a, 'b) t -> ('a, 'b) t

TODO: not implemented

Iterate array elements
val iteri : (int -> 'a -> unit) -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val iter : ('a -> unit) -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val mapi : (int -> 'a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val map : ('a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val filteri : (int -> 'a -> bool) -> ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val filter : ('a -> bool) -> ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val foldi : ?axis:int -> (int -> 'a -> 'a -> 'a) -> 'a -> @@ -236,7 +236,7 @@ (float, 'a) t -> int array -> (float, 'a) t -> - (float, 'a) t

Refer to :doc:`owl_dense_ndarray_generic`

Helper functions
val sum_slices : ?axis:int -> (float, 'b) t -> (float, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

Matrix functions
val row_num : ('a, 'b) t -> int

Refer to :doc:`owl_dense_matrix_generic`

val col_num : ('a, 'b) t -> int

Refer to :doc:`owl_dense_matrix_generic`

val row : ('a, 'b) t -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val rows : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val copy_row_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

Refer to :doc:`owl_dense_matrix_generic`

val copy_col_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

Refer to :doc:`owl_dense_matrix_generic`

val dot : (float, 'b) t -> (float, 'b) t -> (float, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val diag : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val trace : (float, 'b) t -> float

Refer to :doc:`owl_dense_matrix_generic`

val to_rows : ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_matrix_generic`

val of_rows : ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val to_cols : ('a, 'b) t -> ('a, 'b) t array

TODO

val of_cols : ('a, 'b) t array -> ('a, 'b) t

TODO

val of_arrays : ('a, 'b) kind -> 'a array array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val draw_rows : + (float, 'a) t

Refer to :doc:`owl_dense_ndarray_generic`

Helper functions
val sum_slices : ?axis:int -> (float, 'b) t -> (float, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

Matrix functions
val row_num : ('a, 'b) t -> int

Refer to :doc:`owl_dense_matrix_generic`

val col_num : ('a, 'b) t -> int

Refer to :doc:`owl_dense_matrix_generic`

val row : ('a, 'b) t -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val rows : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val copy_row_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

Refer to :doc:`owl_dense_matrix_generic`

val copy_col_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

Refer to :doc:`owl_dense_matrix_generic`

val dot : (float, 'b) t -> (float, 'b) t -> (float, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val diag : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val trace : (float, 'b) t -> float

Refer to :doc:`owl_dense_matrix_generic`

val to_rows : ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_matrix_generic`

val of_rows : ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val to_cols : ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_matrix_generic`

val of_cols : ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val of_arrays : ('a, 'b) kind -> 'a array array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val draw_rows : ?replacement:bool -> ('a, 'b) t -> int -> diff --git a/docs/owl-base/Owl_base_dense_ndarray/Operator/index.html b/docs/owl-base/Owl_base_dense_ndarray/Operator/index.html index b7db1cbd6..9cb60c299 100644 --- a/docs/owl-base/Owl_base_dense_ndarray/Operator/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray/Operator/index.html @@ -1,5 +1,5 @@ -Operator (owl-base.Owl_base_dense_ndarray.Operator)

Module Owl_base_dense_ndarray.Operator

include sig ... end
val (+) : +Operator (owl-base.Owl_base_dense_ndarray.Operator)

Module Owl_base_dense_ndarray.Operator

include sig ... end
val (-) : diff --git a/docs/owl-base/Owl_base_dense_ndarray/S/index.html b/docs/owl-base/Owl_base_dense_ndarray/S/index.html index 9ee8f8727..00529d1fa 100644 --- a/docs/owl-base/Owl_base_dense_ndarray/S/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray/S/index.html @@ -1,5 +1,5 @@ -S (owl-base.Owl_base_dense_ndarray.S)

Module Owl_base_dense_ndarray.S

include module type of struct include Owl_base_dense_ndarray_s end
type elt = float
type arr = +S (owl-base.Owl_base_dense_ndarray.S)

Module Owl_base_dense_ndarray.S

include module type of struct include Owl_base_dense_ndarray_s end
type elt = float
type arr = (float, Stdlib.Bigarray.float32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_dense_ndarray/Z/index.html b/docs/owl-base/Owl_base_dense_ndarray/Z/index.html index 7cec26752..7313b22ba 100644 --- a/docs/owl-base/Owl_base_dense_ndarray/Z/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray/Z/index.html @@ -1,5 +1,5 @@ -Z (owl-base.Owl_base_dense_ndarray.Z)

Module Owl_base_dense_ndarray.Z

include module type of struct include Owl_base_dense_ndarray_z end
type elt = Stdlib.Complex.t
type arr = +Z (owl-base.Owl_base_dense_ndarray.Z)

Module Owl_base_dense_ndarray.Z

include module type of struct include Owl_base_dense_ndarray_z end
type elt = Stdlib.Complex.t
type arr = (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_dense_ndarray/index.html b/docs/owl-base/Owl_base_dense_ndarray/index.html index de0313d44..a60895f24 100644 --- a/docs/owl-base/Owl_base_dense_ndarray/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray/index.html @@ -1,2 +1,2 @@ -Owl_base_dense_ndarray (owl-base.Owl_base_dense_ndarray)

Module Owl_base_dense_ndarray

Ndarray: module aliases

module Operator : sig ... end
module Generic : sig ... end
module S : sig ... end
module D : sig ... end
module C : sig ... end
module Z : sig ... end
+Owl_base_dense_ndarray (owl-base.Owl_base_dense_ndarray)

Module Owl_base_dense_ndarray

Ndarray: module aliases

module Operator : sig ... end
module Generic : sig ... end
module S : sig ... end
module D : sig ... end
module C : sig ... end
module Z : sig ... end
diff --git a/docs/owl-base/Owl_base_dense_ndarray_c/index.html b/docs/owl-base/Owl_base_dense_ndarray_c/index.html index 2d5a228a9..55bbad4bd 100644 --- a/docs/owl-base/Owl_base_dense_ndarray_c/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray_c/index.html @@ -1,5 +1,5 @@ -Owl_base_dense_ndarray_c (owl-base.Owl_base_dense_ndarray_c)

Module Owl_base_dense_ndarray_c

type elt = Stdlib.Complex.t
type arr = +Owl_base_dense_ndarray_c (owl-base.Owl_base_dense_ndarray_c)

Module Owl_base_dense_ndarray_c

type elt = Stdlib.Complex.t
type arr = (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_dense_ndarray_d/index.html b/docs/owl-base/Owl_base_dense_ndarray_d/index.html index 03fee2bda..0d279570f 100644 --- a/docs/owl-base/Owl_base_dense_ndarray_d/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray_d/index.html @@ -1,5 +1,5 @@ -Owl_base_dense_ndarray_d (owl-base.Owl_base_dense_ndarray_d)

Module Owl_base_dense_ndarray_d

type elt = float
type arr = +Owl_base_dense_ndarray_d (owl-base.Owl_base_dense_ndarray_d)

Module Owl_base_dense_ndarray_d

type elt = float
type arr = (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_dense_ndarray_generic/index.html b/docs/owl-base/Owl_base_dense_ndarray_generic/index.html index 0679ee617..58b17f392 100644 --- a/docs/owl-base/Owl_base_dense_ndarray_generic/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray_generic/index.html @@ -1,5 +1,5 @@ -Owl_base_dense_ndarray_generic (owl-base.Owl_base_dense_ndarray_generic)

Module Owl_base_dense_ndarray_generic

N-dimensional array module: including creation, manipulation, and various vectorised mathematical operations.

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

Refer to :doc:`owl_dense_ndarray_generic`

type ('a, 'b) kind = ('a, 'b) Stdlib.Bigarray.kind

Refer to :doc:`owl_dense_ndarray_generic`

Create Ndarrays
val empty : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val create : ('a, 'b) kind -> int array -> 'a -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val init : ('a, 'b) kind -> int array -> (int -> 'a) -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val init_nd : ('a, 'b) kind -> int array -> (int array -> 'a) -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val zeros : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val ones : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val eye : ('a, 'b) kind -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val uniform : ('a, 'b) kind -> ?a:'a -> ?b:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val gaussian : ('a, 'b) kind -> ?mu:'a -> ?sigma:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val sequential : ('a, 'b) kind -> ?a:'a -> ?step:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val bernoulli : ('a, 'b) kind -> ?p:float -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

Obtain basic properties
val shape : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val num_dims : ('a, 'b) t -> int

Refer to :doc:`owl_dense_ndarray_generic`

val nth_dim : ('a, 'b) t -> int -> int

Refer to :doc:`owl_dense_ndarray_generic`

val numel : ('a, 'b) t -> int

Refer to :doc:`owl_dense_ndarray_generic`

val kind : ('a, 'b) t -> ('a, 'b) kind

Refer to :doc:`owl_dense_ndarray_generic`

val strides : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val slice_size : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

Manipulate Ndarrays
val get : ('a, 'b) t -> int array -> 'a

Refer to :doc:`owl_dense_ndarray_generic`

val set : ('a, 'b) t -> int array -> 'a -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> ('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val reset : ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val fill : ('a, 'b) t -> 'a -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val copy : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val reverse : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val tile : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val repeat : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val pad : ?v:'a -> int list list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val squeeze : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val expand : ?hi:bool -> ('a, 'b) t -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val split : ?axis:int -> int array -> ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_ndarray_generic`

val draw : ?axis:int -> ('a, 'b) t -> int -> ('a, 'b) t * int array

Refer to :doc:`owl_dense_ndarray_generic`

val one_hot : int -> ('a, 'b) t -> ('a, 'b) t

TODO: not implemented

Iterate array elements
val iteri : (int -> 'a -> unit) -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val iter : ('a -> unit) -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val mapi : (int -> 'a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val map : ('a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val filteri : (int -> 'a -> bool) -> ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val filter : ('a -> bool) -> ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val foldi : +Owl_base_dense_ndarray_generic (owl-base.Owl_base_dense_ndarray_generic)

Module Owl_base_dense_ndarray_generic

N-dimensional array module: including creation, manipulation, and various vectorised mathematical operations.

About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

Type definition
type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

Refer to :doc:`owl_dense_ndarray_generic`

type ('a, 'b) kind = ('a, 'b) Stdlib.Bigarray.kind

Refer to :doc:`owl_dense_ndarray_generic`

Create Ndarrays
val empty : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val create : ('a, 'b) kind -> int array -> 'a -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val init : ('a, 'b) kind -> int array -> (int -> 'a) -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val init_nd : ('a, 'b) kind -> int array -> (int array -> 'a) -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val zeros : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val ones : ('a, 'b) kind -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val eye : ('a, 'b) kind -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val uniform : ('a, 'b) kind -> ?a:'a -> ?b:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val gaussian : ('a, 'b) kind -> ?mu:'a -> ?sigma:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val sequential : ('a, 'b) kind -> ?a:'a -> ?step:'a -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val bernoulli : ('a, 'b) kind -> ?p:float -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

Obtain basic properties
val shape : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val num_dims : ('a, 'b) t -> int

Refer to :doc:`owl_dense_ndarray_generic`

val nth_dim : ('a, 'b) t -> int -> int

Refer to :doc:`owl_dense_ndarray_generic`

val numel : ('a, 'b) t -> int

Refer to :doc:`owl_dense_ndarray_generic`

val kind : ('a, 'b) t -> ('a, 'b) kind

Refer to :doc:`owl_dense_ndarray_generic`

val strides : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val slice_size : ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

Manipulate Ndarrays
val get : ('a, 'b) t -> int array -> 'a

Refer to :doc:`owl_dense_ndarray_generic`

val set : ('a, 'b) t -> int array -> 'a -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val get_slice : int list list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val set_slice : int list list -> ('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val get_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val set_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val reset : ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val fill : ('a, 'b) t -> 'a -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val copy : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val reshape : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val flatten : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val reverse : ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val transpose : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val tile : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val repeat : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val pad : ?v:'a -> int list list -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val concatenate : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val stack : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val squeeze : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val expand : ?hi:bool -> ('a, 'b) t -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val split : ?axis:int -> int array -> ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_ndarray_generic`

val draw : ?axis:int -> ('a, 'b) t -> int -> ('a, 'b) t * int array

Refer to :doc:`owl_dense_ndarray_generic`

val one_hot : int -> ('a, 'b) t -> ('a, 'b) t

TODO: not implemented

Iterate array elements
val iteri : (int -> 'a -> unit) -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val iter : ('a -> unit) -> ('a, 'b) t -> unit

Refer to :doc:`owl_dense_ndarray_generic`

val mapi : (int -> 'a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val map : ('a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

val filteri : (int -> 'a -> bool) -> ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val filter : ('a -> bool) -> ('a, 'b) t -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val foldi : ?axis:int -> (int -> 'a -> 'a -> 'a) -> 'a -> @@ -236,7 +236,7 @@ (float, 'a) t -> int array -> (float, 'a) t -> - (float, 'a) t

Refer to :doc:`owl_dense_ndarray_generic`

Helper functions
val sum_slices : ?axis:int -> (float, 'b) t -> (float, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

Matrix functions
val row_num : ('a, 'b) t -> int

Refer to :doc:`owl_dense_matrix_generic`

val col_num : ('a, 'b) t -> int

Refer to :doc:`owl_dense_matrix_generic`

val row : ('a, 'b) t -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val rows : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val copy_row_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

Refer to :doc:`owl_dense_matrix_generic`

val copy_col_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

Refer to :doc:`owl_dense_matrix_generic`

val dot : (float, 'b) t -> (float, 'b) t -> (float, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val diag : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val trace : (float, 'b) t -> float

Refer to :doc:`owl_dense_matrix_generic`

val to_rows : ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_matrix_generic`

val of_rows : ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val to_cols : ('a, 'b) t -> ('a, 'b) t array

TODO

val of_cols : ('a, 'b) t array -> ('a, 'b) t

TODO

val of_arrays : ('a, 'b) kind -> 'a array array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val draw_rows : + (float, 'a) t

Refer to :doc:`owl_dense_ndarray_generic`

Helper functions
val sum_slices : ?axis:int -> (float, 'b) t -> (float, 'b) t

Refer to :doc:`owl_dense_ndarray_generic`

Matrix functions
val row_num : ('a, 'b) t -> int

Refer to :doc:`owl_dense_matrix_generic`

val col_num : ('a, 'b) t -> int

Refer to :doc:`owl_dense_matrix_generic`

val row : ('a, 'b) t -> int -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val rows : ('a, 'b) t -> int array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val copy_row_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

Refer to :doc:`owl_dense_matrix_generic`

val copy_col_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

Refer to :doc:`owl_dense_matrix_generic`

val dot : (float, 'b) t -> (float, 'b) t -> (float, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val diag : ?k:int -> ('a, 'b) t -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val trace : (float, 'b) t -> float

Refer to :doc:`owl_dense_matrix_generic`

val to_rows : ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_matrix_generic`

val of_rows : ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val to_cols : ('a, 'b) t -> ('a, 'b) t array

Refer to :doc:`owl_dense_matrix_generic`

val of_cols : ('a, 'b) t array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val of_arrays : ('a, 'b) kind -> 'a array array -> ('a, 'b) t

Refer to :doc:`owl_dense_matrix_generic`

val draw_rows : ?replacement:bool -> ('a, 'b) t -> int -> diff --git a/docs/owl-base/Owl_base_dense_ndarray_intf/index.html b/docs/owl-base/Owl_base_dense_ndarray_intf/index.html index 61083bc61..914978b97 100644 --- a/docs/owl-base/Owl_base_dense_ndarray_intf/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray_intf/index.html @@ -1,2 +1,2 @@ -Owl_base_dense_ndarray_intf (owl-base.Owl_base_dense_ndarray_intf)

Module Owl_base_dense_ndarray_intf

module type Common = sig ... end
module type Real = sig ... end
module type NN = sig ... end
+Owl_base_dense_ndarray_intf (owl-base.Owl_base_dense_ndarray_intf)

Module Owl_base_dense_ndarray_intf

module type Common = sig ... end
module type Real = sig ... end
module type NN = sig ... end
diff --git a/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-Common/index.html b/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-Common/index.html index 72aa659ae..ad7a7fa85 100644 --- a/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-Common/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-Common/index.html @@ -1,5 +1,5 @@ -Common (owl-base.Owl_base_dense_ndarray_intf.Common)

Module type Owl_base_dense_ndarray_intf.Common

type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:float -> int array -> arr
val shape : arr -> int array
val numel : arr -> int
val strides : arr -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val slice_size : arr -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val flatten : arr -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val squeeze : ?axis:int array -> arr -> arr
val expand : ?hi:bool -> arr -> int -> arr
val split : ?axis:int -> int array -> arr -> arr array
val draw : ?axis:int -> arr -> int -> arr * int array
val pad : ?v:elt -> int list list -> arr -> arr
val one_hot : int -> arr -> arr
val print : +Common (owl-base.Owl_base_dense_ndarray_intf.Common)

Module type Owl_base_dense_ndarray_intf.Common

type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:float -> int array -> arr
val shape : arr -> int array
val numel : arr -> int
val strides : arr -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val slice_size : arr -> int array

Refer to :doc:`owl_dense_ndarray_generic`

val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val flatten : arr -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val squeeze : ?axis:int array -> arr -> arr
val expand : ?hi:bool -> arr -> int -> arr
val split : ?axis:int -> int array -> arr -> arr array
val draw : ?axis:int -> arr -> int -> arr * int array
val pad : ?v:elt -> int list list -> arr -> arr
val one_hot : int -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-NN/index.html b/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-NN/index.html index 6c6b82b45..0b3314b77 100644 --- a/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-NN/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_base_dense_ndarray_intf.NN)

Module type Owl_base_dense_ndarray_intf.NN

type arr
val conv1d : +NN (owl-base.Owl_base_dense_ndarray_intf.NN)

Module type Owl_base_dense_ndarray_intf.NN

type arr
val conv1d : ?padding:Owl_types_common.padding -> arr -> arr -> diff --git a/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-Real/index.html b/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-Real/index.html index 7782e343a..59b21f90d 100644 --- a/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-Real/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray_intf/module-type-Real/index.html @@ -1,2 +1,2 @@ -Real (owl-base.Owl_base_dense_ndarray_intf.Real)

Module type Owl_base_dense_ndarray_intf.Real

type elt
type arr
val log_sum_exp' : arr -> elt
val log_sum_exp : ?axis:int -> ?keep_dims:bool -> arr -> arr
val sum_slices : ?axis:int -> arr -> arr
val signum : arr -> arr
val sigmoid : arr -> arr
val relu : arr -> arr
val dawsn : arr -> arr
val l1norm' : arr -> elt
val l2norm' : arr -> elt
val l2norm_sqr' : arr -> elt
val clip_by_value : ?amin:elt -> ?amax:elt -> arr -> arr
val clip_by_l2norm : elt -> arr -> arr
val atan2 : arr -> arr -> arr
val scalar_atan2 : elt -> arr -> arr
val atan2_scalar : arr -> elt -> arr
val approx_equal : ?eps:float -> arr -> arr -> bool
val approx_equal_scalar : ?eps:float -> arr -> float -> bool
val approx_elt_equal : ?eps:float -> arr -> arr -> arr
val approx_elt_equal_scalar : ?eps:float -> arr -> float -> arr
val dot : arr -> arr -> arr
val trace : arr -> elt
Helper functions
val float_to_elt : float -> elt
val elt_to_float : elt -> float
+Real (owl-base.Owl_base_dense_ndarray_intf.Real)

Module type Owl_base_dense_ndarray_intf.Real

type elt
type arr
val log_sum_exp' : arr -> elt
val log_sum_exp : ?axis:int -> ?keep_dims:bool -> arr -> arr
val sum_slices : ?axis:int -> arr -> arr
val signum : arr -> arr
val sigmoid : arr -> arr
val relu : arr -> arr
val dawsn : arr -> arr
val l1norm' : arr -> elt
val l2norm' : arr -> elt
val l2norm_sqr' : arr -> elt
val clip_by_value : ?amin:elt -> ?amax:elt -> arr -> arr
val clip_by_l2norm : elt -> arr -> arr
val atan2 : arr -> arr -> arr
val scalar_atan2 : elt -> arr -> arr
val atan2_scalar : arr -> elt -> arr
val approx_equal : ?eps:float -> arr -> arr -> bool
val approx_equal_scalar : ?eps:float -> arr -> float -> bool
val approx_elt_equal : ?eps:float -> arr -> arr -> arr
val approx_elt_equal_scalar : ?eps:float -> arr -> float -> arr
val dot : arr -> arr -> arr
val trace : arr -> elt
Helper functions
val float_to_elt : float -> elt
val elt_to_float : elt -> float
diff --git a/docs/owl-base/Owl_base_dense_ndarray_s/index.html b/docs/owl-base/Owl_base_dense_ndarray_s/index.html index 0f707f0ee..98ab48748 100644 --- a/docs/owl-base/Owl_base_dense_ndarray_s/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray_s/index.html @@ -1,5 +1,5 @@ -Owl_base_dense_ndarray_s (owl-base.Owl_base_dense_ndarray_s)

Module Owl_base_dense_ndarray_s

type elt = float
type arr = +Owl_base_dense_ndarray_s (owl-base.Owl_base_dense_ndarray_s)

Module Owl_base_dense_ndarray_s

type elt = float
type arr = (float, Stdlib.Bigarray.float32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_dense_ndarray_z/index.html b/docs/owl-base/Owl_base_dense_ndarray_z/index.html index 7678daeb8..2cc2867c6 100644 --- a/docs/owl-base/Owl_base_dense_ndarray_z/index.html +++ b/docs/owl-base/Owl_base_dense_ndarray_z/index.html @@ -1,5 +1,5 @@ -Owl_base_dense_ndarray_z (owl-base.Owl_base_dense_ndarray_z)

Module Owl_base_dense_ndarray_z

type elt = Stdlib.Complex.t
type arr = +Owl_base_dense_ndarray_z (owl-base.Owl_base_dense_ndarray_z)

Module Owl_base_dense_ndarray_z

type elt = Stdlib.Complex.t
type arr = (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
include Owl_base_dense_ndarray_intf.Common with type arr := arr diff --git a/docs/owl-base/Owl_base_linalg_c/index.html b/docs/owl-base/Owl_base_linalg_c/index.html index 5d9852fed..fb2a58356 100644 --- a/docs/owl-base/Owl_base_linalg_c/index.html +++ b/docs/owl-base/Owl_base_linalg_c/index.html @@ -1,5 +1,5 @@ -Owl_base_linalg_c (owl-base.Owl_base_linalg_c)

Module Owl_base_linalg_c

type elt = Stdlib.Complex.t
type int32_mat = +Owl_base_linalg_c (owl-base.Owl_base_linalg_c)

Module Owl_base_linalg_c

type elt = Stdlib.Complex.t
type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_base_dense_matrix_generic.t
include Owl_base_linalg_intf.Common with type elt := elt and type mat := mat diff --git a/docs/owl-base/Owl_base_linalg_d/index.html b/docs/owl-base/Owl_base_linalg_d/index.html index 5867659bf..d6f01e154 100644 --- a/docs/owl-base/Owl_base_linalg_d/index.html +++ b/docs/owl-base/Owl_base_linalg_d/index.html @@ -1,5 +1,5 @@ -Owl_base_linalg_d (owl-base.Owl_base_linalg_d)

Module Owl_base_linalg_d

type elt = float
type complex_mat = Owl_base_dense_matrix_z.mat
type int32_mat = +Owl_base_linalg_d (owl-base.Owl_base_linalg_d)

Module Owl_base_linalg_d

type elt = float
type complex_mat = Owl_base_dense_matrix_z.mat
type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_base_dense_matrix_generic.t
include Owl_base_linalg_intf.Common with type elt := elt and type mat := mat diff --git a/docs/owl-base/Owl_base_linalg_generic/index.html b/docs/owl-base/Owl_base_linalg_generic/index.html index 1c3cd53c2..94e29e144 100644 --- a/docs/owl-base/Owl_base_linalg_generic/index.html +++ b/docs/owl-base/Owl_base_linalg_generic/index.html @@ -1,5 +1,5 @@ -Owl_base_linalg_generic (owl-base.Owl_base_linalg_generic)

Module Owl_base_linalg_generic

Types and constants
type ('a, 'b) t = ('a, 'b) Owl_base_dense_ndarray_generic.t
Basic functions
val inv : ('a, 'b) t -> ('a, 'b) t

inv x calculates the inverse of an invertible square matrix x such that x *@ x = I wherein I is an identity matrix. (If x is singular, inv will return a useless result.)

val det : ('a, 'b) t -> 'a

det x computes the determinant of a square matrix x.

val logdet : ('a, 'b) t -> 'a

Refer to :doc:`owl_dense_matrix_generic`

Check matrix types
val is_tril : ('a, 'b) t -> bool

is_tril x returns true if x is lower triangular otherwise false.

val is_triu : ('a, 'b) t -> bool

is_triu x returns true if x is upper triangular otherwise false.

val is_diag : ('a, 'b) t -> bool

is_diag x returns true if x is diagonal otherwise false.

val is_symmetric : ('a, 'b) t -> bool

is_symmetric x returns true if x is symmetric otherwise false.

val is_hermitian : (Stdlib.Complex.t, 'b) t -> bool

is_hermitian x returns true if x is hermitian otherwise false.

Factorisation
val lu : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t * int array

lu x -> (l, u, ipiv) calculates LU decomposition of x. The pivoting is used by default.

val qr : +Owl_base_linalg_generic (owl-base.Owl_base_linalg_generic)

Module Owl_base_linalg_generic

Types and constants
type ('a, 'b) t = ('a, 'b) Owl_base_dense_ndarray_generic.t
Basic functions
val inv : ('a, 'b) t -> ('a, 'b) t

inv x calculates the inverse of an invertible square matrix x such that x *@ x = I wherein I is an identity matrix. (If x is singular, inv will return a useless result.)

val det : ('a, 'b) t -> 'a

det x computes the determinant of a square matrix x.

val logdet : ('a, 'b) t -> 'a

Refer to :doc:`owl_dense_matrix_generic`

Check matrix types
val is_tril : ('a, 'b) t -> bool

is_tril x returns true if x is lower triangular otherwise false.

val is_triu : ('a, 'b) t -> bool

is_triu x returns true if x is upper triangular otherwise false.

val is_diag : ('a, 'b) t -> bool

is_diag x returns true if x is diagonal otherwise false.

val is_symmetric : ('a, 'b) t -> bool

is_symmetric x returns true if x is symmetric otherwise false.

val is_hermitian : (Stdlib.Complex.t, 'b) t -> bool

is_hermitian x returns true if x is hermitian otherwise false.

Factorisation
val lu : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t * int array

lu x -> (l, u, ipiv) calculates LU decomposition of x. The pivoting is used by default.

val qr : ?thin:bool -> ?pivot:bool -> ('a, 'b) t -> diff --git a/docs/owl-base/Owl_base_linalg_intf/index.html b/docs/owl-base/Owl_base_linalg_intf/index.html index cf4b26395..e26c2781d 100644 --- a/docs/owl-base/Owl_base_linalg_intf/index.html +++ b/docs/owl-base/Owl_base_linalg_intf/index.html @@ -1,2 +1,2 @@ -Owl_base_linalg_intf (owl-base.Owl_base_linalg_intf)

Module Owl_base_linalg_intf

module type Common = sig ... end
module type Real = sig ... end
+Owl_base_linalg_intf (owl-base.Owl_base_linalg_intf)

Module Owl_base_linalg_intf

module type Common = sig ... end
module type Real = sig ... end
diff --git a/docs/owl-base/Owl_base_linalg_intf/module-type-Common/index.html b/docs/owl-base/Owl_base_linalg_intf/module-type-Common/index.html index 2dec38bda..2fc9d3d60 100644 --- a/docs/owl-base/Owl_base_linalg_intf/module-type-Common/index.html +++ b/docs/owl-base/Owl_base_linalg_intf/module-type-Common/index.html @@ -1,5 +1,5 @@ -Common (owl-base.Owl_base_linalg_intf.Common)

Module type Owl_base_linalg_intf.Common

type elt
type mat
type complex_mat
type int32_mat
Basic functions
val inv : mat -> mat
val det : mat -> elt
val logdet : mat -> elt
val is_triu : mat -> bool
val is_tril : mat -> bool
val is_symmetric : mat -> bool
val is_diag : mat -> bool
Factorisation
val svd : ?thin:bool -> mat -> mat * mat * mat
val chol : ?upper:bool -> mat -> mat
val qr : ?thin:bool -> ?pivot:bool -> mat -> mat * mat * int32_mat
val lq : ?thin:bool -> mat -> mat * mat
Linear system of equations
val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> mat -> mat -> mat
val sylvester : mat -> mat -> mat -> mat
val lyapunov : mat -> mat -> mat
val discrete_lyapunov : +Common (owl-base.Owl_base_linalg_intf.Common)

Module type Owl_base_linalg_intf.Common

type elt
type mat
type complex_mat
type int32_mat
Basic functions
val inv : mat -> mat
val det : mat -> elt
val logdet : mat -> elt
val is_triu : mat -> bool
val is_tril : mat -> bool
val is_symmetric : mat -> bool
val is_diag : mat -> bool
Factorisation
val svd : ?thin:bool -> mat -> mat * mat * mat
val chol : ?upper:bool -> mat -> mat
val qr : ?thin:bool -> ?pivot:bool -> mat -> mat * mat * int32_mat
val lq : ?thin:bool -> mat -> mat * mat
Linear system of equations
val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> mat -> mat -> mat
val sylvester : mat -> mat -> mat -> mat
val lyapunov : mat -> mat -> mat
val discrete_lyapunov : ?solver:[ `default | `direct | `bilinear ] -> mat -> mat -> diff --git a/docs/owl-base/Owl_base_linalg_intf/module-type-Real/index.html b/docs/owl-base/Owl_base_linalg_intf/module-type-Real/index.html index d81d7e817..9c8db3190 100644 --- a/docs/owl-base/Owl_base_linalg_intf/module-type-Real/index.html +++ b/docs/owl-base/Owl_base_linalg_intf/module-type-Real/index.html @@ -1,2 +1,2 @@ -Real (owl-base.Owl_base_linalg_intf.Real)

Module type Owl_base_linalg_intf.Real

type elt
type mat
val care : ?diag_r:bool -> mat -> mat -> mat -> mat -> mat
val dare : ?diag_r:bool -> mat -> mat -> mat -> mat -> mat
+Real (owl-base.Owl_base_linalg_intf.Real)

Module type Owl_base_linalg_intf.Real

type elt
type mat
val care : ?diag_r:bool -> mat -> mat -> mat -> mat -> mat
val dare : ?diag_r:bool -> mat -> mat -> mat -> mat -> mat
diff --git a/docs/owl-base/Owl_base_linalg_s/index.html b/docs/owl-base/Owl_base_linalg_s/index.html index 32e818d48..235c01f4c 100644 --- a/docs/owl-base/Owl_base_linalg_s/index.html +++ b/docs/owl-base/Owl_base_linalg_s/index.html @@ -1,5 +1,5 @@ -Owl_base_linalg_s (owl-base.Owl_base_linalg_s)

Module Owl_base_linalg_s

type elt = float
type complex_mat = Owl_base_dense_matrix_c.mat
type int32_mat = +Owl_base_linalg_s (owl-base.Owl_base_linalg_s)

Module Owl_base_linalg_s

type elt = float
type complex_mat = Owl_base_dense_matrix_c.mat
type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_base_dense_matrix_generic.t
include Owl_base_linalg_intf.Common with type elt := elt and type mat := mat diff --git a/docs/owl-base/Owl_base_linalg_z/index.html b/docs/owl-base/Owl_base_linalg_z/index.html index 1d875972c..b7849bbf0 100644 --- a/docs/owl-base/Owl_base_linalg_z/index.html +++ b/docs/owl-base/Owl_base_linalg_z/index.html @@ -1,5 +1,5 @@ -Owl_base_linalg_z (owl-base.Owl_base_linalg_z)

Module Owl_base_linalg_z

type elt = Stdlib.Complex.t
type int32_mat = +Owl_base_linalg_z (owl-base.Owl_base_linalg_z)

Module Owl_base_linalg_z

type elt = Stdlib.Complex.t
type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_base_dense_matrix_generic.t
include Owl_base_linalg_intf.Common with type elt := elt and type mat := mat diff --git a/docs/owl-base/Owl_base_maths/index.html b/docs/owl-base/Owl_base_maths/index.html index ee98fa4c6..4c5307b3c 100644 --- a/docs/owl-base/Owl_base_maths/index.html +++ b/docs/owl-base/Owl_base_maths/index.html @@ -1,2 +1,19 @@ -Owl_base_maths (owl-base.Owl_base_maths)

Module Owl_base_maths

Maths: fundamental and advanced mathematical functions.

Basic functions
val add : float -> float -> float

add x y

val sub : float -> float -> float

sub x y

val mul : float -> float -> float

mul x y

val div : float -> float -> float

div x y

val fmod : float -> float -> float

fmod x y

val atan2 : float -> float -> float

atan2 x y

val hypot : float -> float -> float

hypot x y

val abs : float -> float

abs x

val neg : float -> float

neg x

val reci : float -> float

reci x

val floor : float -> float

floor x

val ceil : float -> float

ceil x

val round : float -> float

round x

val trunc : float -> float

trunc x

val fix : float -> float

fix x

val sqr : float -> float

sqr x

val sqrt : float -> float

sqrt x

val cbrt : float -> float

cbrt x

val pow : float -> float -> float

pow x

val exp : float -> float

exp x

val exp2 : float -> float

exp2 x

val exp10 : float -> float

exp10 x

val expm1 : float -> float

expm1 x

val log : float -> float

log x

val log2 : float -> float

log2 x

val log10 : float -> float

log10 x

val log1p : float -> float

log1p x

val sigmoid : float -> float

sigmod x

val signum : float -> float

signum x

val softsign : float -> float

softsign x

val softplus : float -> float

softplus x

val relu : float -> float

relu x

val dawsn : float -> float

dawsn x

val sin : float -> float

sin x

val cos : float -> float

cos x

val tan : float -> float

tan x

val cot : float -> float

cot x

val sec : float -> float

sec x

val csc : float -> float

csc x

val asin : float -> float

asin x

val acos : float -> float

acos x

val atan : float -> float

atan x

val acot : float -> float

cot x

val asec : float -> float

sec x

val acsc : float -> float

csc x

val sinh : float -> float

sinh x

val cosh : float -> float

cosh x

val tanh : float -> float

tanh x

val asinh : float -> float

asinh x

val acosh : float -> float

acosh x

val atanh : float -> float

atanh x

val acoth : float -> float

coth x

val asech : float -> float

sech x

val acsch : float -> float

csch x

val xlogy : float -> float -> float

xlogy(x, y)

val xlog1py : float -> float -> float

xlog1py(x, y)

val logit : float -> float

logit(x)

val expit : float -> float

expit(x)

val log1mexp : float -> float

log1mexp(x)

val log1pexp : float -> float

log1pexp(x)

Error functions
val erf : float -> float

erf(x)

val erfc : float -> float

erfc(x)

val erfcx : float -> float

erfcx(x)

Helper functions
val is_nan : float -> bool

is_nan x returns true if x is nan.

val is_inf : float -> bool

is_inf x returns true if x is infinity or neg_infinity.

val is_normal : float -> bool

is_normal x returns true if x is a normal float number.

val is_subnormal : float -> bool

is_nan x returns true if x is subnormal float number.

val is_odd : int -> bool

is_odd x returns true if x is odd.

val is_even : int -> bool

is_even x returns true if x is even.

val is_pow2 : int -> bool

is_pow2 x return true if x is integer power of 2, e.g. 32, 64, etc.

val same_sign : float -> float -> bool

same_sign x y returns true if x and y have the same sign, otherwise it returns false. Positive and negative zeros are special cases and always returns true.

val is_simplex : float array -> bool

is_simplex x checks whether x is simplex. In other words, :math:`\sum_i^K x_i = 1` and :math:`x_i \ge 0, \forall x_i \in 1,K`.

val is_int : float -> bool
val is_sqr : int -> bool

is_sqr x checks if x is the square of an integer.

val mulmod : int -> int -> int -> int

mulmod a b m computes (a*b) mod m.

val powmod : int -> int -> int -> int

powmod a b m computes (a^b) mod m.

val is_prime : int -> bool

is_prime x returns true if x is a prime number. The function is deterministic for all numbers representable by an int. The function uses the Rabin–Miller primality test.

val fermat_fact : int -> int * int

fermat_fact x performs Fermat factorisation over x, i.e. into two roughly equal factors. x must be an odd number.

+Owl_base_maths (owl-base.Owl_base_maths)

Module Owl_base_maths

Maths: fundamental and advanced mathematical functions.

Basic functions
val add : float -> float -> float

add x y

val sub : float -> float -> float

sub x y

val mul : float -> float -> float

mul x y

val div : float -> float -> float

div x y

val fmod : float -> float -> float

fmod x y

val atan2 : float -> float -> float

atan2 x y

val hypot : float -> float -> float

hypot x y

val abs : float -> float

abs x

val neg : float -> float

neg x

val reci : float -> float

reci x

val floor : float -> float

floor x

val ceil : float -> float

ceil x

val round : float -> float

round x

val trunc : float -> float

trunc x

val fix : float -> float

fix x

val sqr : float -> float

sqr x

val sqrt : float -> float

sqrt x

val cbrt : float -> float

cbrt x

val pow : float -> float -> float

pow x

val exp : float -> float

exp x

val exp2 : float -> float

exp2 x

val exp10 : float -> float

exp10 x

val expm1 : float -> float

expm1 x

val log : float -> float

log x

val log2 : float -> float

log2 x

val log10 : float -> float

log10 x

val log1p : float -> float

log1p x

val sigmoid : float -> float

sigmod x

val signum : float -> float

signum x

val softsign : float -> float

softsign x

val softplus : float -> float

softplus x

val relu : float -> float

relu x

val dawsn : float -> float

dawsn x

val sin : float -> float

sin x

val cos : float -> float

cos x

val tan : float -> float

tan x

val cot : float -> float

cot x

val sec : float -> float

sec x

val csc : float -> float

csc x

val asin : float -> float

asin x

val acos : float -> float

acos x

val atan : float -> float

atan x

val acot : float -> float

cot x

val asec : float -> float

sec x

val acsc : float -> float

csc x

val sinh : float -> float

sinh x

val cosh : float -> float

cosh x

val tanh : float -> float

tanh x

val asinh : float -> float

asinh x

val acosh : float -> float

acosh x

val atanh : float -> float

atanh x

val acoth : float -> float

coth x

val asech : float -> float

sech x

val acsch : float -> float

csch x

val xlogy : float -> float -> float

xlogy(x, y)

val xlog1py : float -> float -> float

xlog1py(x, y)

val logit : float -> float

logit(x)

val expit : float -> float

expit(x)

val log1mexp : float -> float

log1mexp(x)

val log1pexp : float -> float

log1pexp(x)

Error functions
val erf : float -> float

erf(x)

val erfc : float -> float

erfc(x)

val erfcx : float -> float

erfcx(x)

Helper functions
val is_nan : float -> bool

is_nan x returns true if x is nan.

val is_inf : float -> bool

is_inf x returns true if x is infinity or neg_infinity.

val is_normal : float -> bool

is_normal x returns true if x is a normal float number.

val is_subnormal : float -> bool

is_nan x returns true if x is subnormal float number.

val is_odd : int -> bool

is_odd x returns true if x is odd.

val is_even : int -> bool

is_even x returns true if x is even.

val is_pow2 : int -> bool

is_pow2 x return true if x is integer power of 2, e.g. 32, 64, etc.

val same_sign : float -> float -> bool

same_sign x y returns true if x and y have the same sign, otherwise it returns false. Positive and negative zeros are special cases and always returns true.

val is_simplex : float array -> bool

is_simplex x checks whether x is simplex. In other words, \sum_i^K x_i = 1 and x_i \ge 0, \forall x_i \in [1,K].

val is_int : float -> bool
val is_sqr : int -> bool

is_sqr x checks if x is the square of an integer.

val mulmod : int -> int -> int -> int

mulmod a b m computes (a*b) mod m.

val powmod : int -> int -> int -> int

powmod a b m computes (a^b) mod m.

val is_prime : int -> bool

is_prime x returns true if x is a prime number. The function is deterministic for all numbers representable by an int. The function uses the Rabin–Miller primality test.

val fermat_fact : int -> int * int

fermat_fact x performs Fermat factorisation over x, i.e. into two roughly equal factors. x must be an odd number.

diff --git a/docs/owl-base/Owl_base_slicing/index.html b/docs/owl-base/Owl_base_slicing/index.html index a09968b90..65d3d0395 100644 --- a/docs/owl-base/Owl_base_slicing/index.html +++ b/docs/owl-base/Owl_base_slicing/index.html @@ -1,5 +1,5 @@ -Owl_base_slicing (owl-base.Owl_base_slicing)

Module Owl_base_slicing

val sdlist_to_sdarray : Owl_types.index list -> Owl_types.index_ array
val sdarray_to_sdarray : Owl_types.index array -> Owl_types.index_ array
val is_basic_slicing : Owl_types.index_ array -> bool
val check_slice_definition : +Owl_base_slicing (owl-base.Owl_base_slicing)

Module Owl_base_slicing

val sdlist_to_sdarray : Owl_types.index list -> Owl_types.index_ array
val sdarray_to_sdarray : Owl_types.index array -> Owl_types.index_ array
val is_basic_slicing : Owl_types.index_ array -> bool
val check_slice_definition : Owl_types.index_ array -> int array -> Owl_types.index_ array
val calc_continuous_blksz : Owl_types.index_ array -> int array -> int * int
val calc_slice_shape : Owl_types.index_ array -> int array
val __foreach_continuous_blk : diff --git a/docs/owl-base/Owl_base_stats/index.html b/docs/owl-base/Owl_base_stats/index.html index 7a32986cf..22339eda8 100644 --- a/docs/owl-base/Owl_base_stats/index.html +++ b/docs/owl-base/Owl_base_stats/index.html @@ -1,5 +1,5 @@ -Owl_base_stats (owl-base.Owl_base_stats)

Module Owl_base_stats

Statistics: random number generators, PDF and CDF functions, and hypothesis tests. The module also includes some basic statistical functions such as mean, variance, skew, and etc.

Randomisation functions
val shuffle : 'a array -> 'a array

Refer to :doc:`owl_stats`.

val choose : 'a array -> int -> 'a array

Refer to :doc:`owl_stats`.

val sample : 'a array -> int -> 'a array

Refer to :doc:`owl_stats`.

Basic statistical functions
val sum : float array -> float

Refer to :doc:`owl_stats`.

val mean : float array -> float

Refer to :doc:`owl_stats`.

val var : ?mean:float -> float array -> float

Refer to :doc:`owl_stats`.

val std : ?mean:float -> float array -> float

Refer to :doc:`owl_stats`.

val sem : ?mean:float -> float array -> float

Refer to :doc:`owl_stats`.

val absdev : ?mean:float -> float array -> float

Refer to :doc:`owl_stats`.

val skew : ?mean:float -> ?sd:float -> float array -> float

Refer to :doc:`owl_stats`.

val kurtosis : ?mean:float -> ?sd:float -> float array -> float

Refer to :doc:`owl_stats`.

val central_moment : int -> float array -> float

Refer to :doc:`owl_stats`.

val cov : ?m0:float -> ?m1:float -> float array -> float array -> float

Refer to :doc:`owl_stats`.

val concordant : 'a array -> 'b array -> int

Refer to :doc:`owl_stats`.

val discordant : 'a array -> 'b array -> int

Refer to :doc:`owl_stats`.

val kendall_tau : float array -> float array -> float

Refer to :doc:`owl_stats`.

val min : float array -> float

Refer to :doc:`owl_stats`.

val max : float array -> float

Refer to :doc:`owl_stats`.

val minmax : float array -> float * float

Refer to :doc:`owl_stats`.

val min_i : float array -> int

Refer to :doc:`owl_stats`.

val max_i : float array -> int

Refer to :doc:`owl_stats`.

val minmax_i : float array -> int * int

Refer to :doc:`owl_stats`.

val sort : ?inc:bool -> float array -> float array

Refer to :doc:`owl_stats`.

val argsort : ?inc:bool -> float array -> int array

Refer to :doc:`owl_stats`.

val rank : +Owl_base_stats (owl-base.Owl_base_stats)

Module Owl_base_stats

Statistics: random number generators, PDF and CDF functions, and hypothesis tests. The module also includes some basic statistical functions such as mean, variance, skew, and etc.

Randomisation functions
val shuffle : 'a array -> 'a array

Refer to :doc:`owl_stats`.

val choose : 'a array -> int -> 'a array

Refer to :doc:`owl_stats`.

val sample : 'a array -> int -> 'a array

Refer to :doc:`owl_stats`.

Basic statistical functions
val sum : float array -> float

Refer to :doc:`owl_stats`.

val mean : float array -> float

Refer to :doc:`owl_stats`.

val var : ?mean:float -> float array -> float

Refer to :doc:`owl_stats`.

val std : ?mean:float -> float array -> float

Refer to :doc:`owl_stats`.

val sem : ?mean:float -> float array -> float

Refer to :doc:`owl_stats`.

val absdev : ?mean:float -> float array -> float

Refer to :doc:`owl_stats`.

val skew : ?mean:float -> ?sd:float -> float array -> float

Refer to :doc:`owl_stats`.

val kurtosis : ?mean:float -> ?sd:float -> float array -> float

Refer to :doc:`owl_stats`.

val central_moment : int -> float array -> float

Refer to :doc:`owl_stats`.

val cov : ?m0:float -> ?m1:float -> float array -> float array -> float

Refer to :doc:`owl_stats`.

val concordant : 'a array -> 'b array -> int

Refer to :doc:`owl_stats`.

val discordant : 'a array -> 'b array -> int

Refer to :doc:`owl_stats`.

val kendall_tau : float array -> float array -> float

Refer to :doc:`owl_stats`.

val min : float array -> float

Refer to :doc:`owl_stats`.

val max : float array -> float

Refer to :doc:`owl_stats`.

val minmax : float array -> float * float

Refer to :doc:`owl_stats`.

val min_i : float array -> int

Refer to :doc:`owl_stats`.

val max_i : float array -> int

Refer to :doc:`owl_stats`.

val minmax_i : float array -> int * int

Refer to :doc:`owl_stats`.

val sort : ?inc:bool -> float array -> float array

Refer to :doc:`owl_stats`.

val argsort : ?inc:bool -> float array -> int array

Refer to :doc:`owl_stats`.

val rank : ?ties_strategy:[ `Average | `Min | `Max ] -> float array -> float array

Refer to :doc:`owl_stats`.

val percentile : float array -> float -> float

Refer to :doc:`owl_stats`.

val quantile : float array -> float -> float

Refer to :doc:`owl_stats`.

val first_quartile : float array -> float

Refer to :doc:`owl_stats`.

val third_quartile : float array -> float

Refer to :doc:`owl_stats`.

val interquartile : float array -> float

Refer to :doc:`owl_stats`.

val median : float array -> float

Refer to :doc:`owl_stats`.

type histogram = {
  1. bins : float array;
  2. counts : int array;
  3. weighted_counts : float array option;
  4. normalised_counts : float array option;
  5. density : float array option;
}

Refer to :doc:`owl_stats`.

val histogram : diff --git a/docs/owl-base/Owl_base_stats_dist_bernoulli/index.html b/docs/owl-base/Owl_base_stats_dist_bernoulli/index.html index e2209c213..c50c087d7 100644 --- a/docs/owl-base/Owl_base_stats_dist_bernoulli/index.html +++ b/docs/owl-base/Owl_base_stats_dist_bernoulli/index.html @@ -1,2 +1,2 @@ -Owl_base_stats_dist_bernoulli (owl-base.Owl_base_stats_dist_bernoulli)

Module Owl_base_stats_dist_bernoulli

val bernoulli_rvs : p:float -> float
+Owl_base_stats_dist_bernoulli (owl-base.Owl_base_stats_dist_bernoulli)

Module Owl_base_stats_dist_bernoulli

val bernoulli_rvs : p:float -> float
diff --git a/docs/owl-base/Owl_base_stats_dist_cauchy/index.html b/docs/owl-base/Owl_base_stats_dist_cauchy/index.html index f2cb2b011..22e7976f7 100644 --- a/docs/owl-base/Owl_base_stats_dist_cauchy/index.html +++ b/docs/owl-base/Owl_base_stats_dist_cauchy/index.html @@ -1,2 +1,2 @@ -Owl_base_stats_dist_cauchy (owl-base.Owl_base_stats_dist_cauchy)

Module Owl_base_stats_dist_cauchy

val std_cauchy_rvs : unit -> float
val cauchy_rvs : loc:float -> scale:float -> float
+Owl_base_stats_dist_cauchy (owl-base.Owl_base_stats_dist_cauchy)

Module Owl_base_stats_dist_cauchy

val std_cauchy_rvs : unit -> float
val cauchy_rvs : loc:float -> scale:float -> float
diff --git a/docs/owl-base/Owl_base_stats_dist_exponential/index.html b/docs/owl-base/Owl_base_stats_dist_exponential/index.html index 9252429b4..81925d46a 100644 --- a/docs/owl-base/Owl_base_stats_dist_exponential/index.html +++ b/docs/owl-base/Owl_base_stats_dist_exponential/index.html @@ -1,2 +1,2 @@ -Owl_base_stats_dist_exponential (owl-base.Owl_base_stats_dist_exponential)

Module Owl_base_stats_dist_exponential

val std_exponential_rvs : unit -> float
val exponential_rvs : lambda:float -> float
+Owl_base_stats_dist_exponential (owl-base.Owl_base_stats_dist_exponential)

Module Owl_base_stats_dist_exponential

val std_exponential_rvs : unit -> float
val exponential_rvs : lambda:float -> float
diff --git a/docs/owl-base/Owl_base_stats_dist_gamma/index.html b/docs/owl-base/Owl_base_stats_dist_gamma/index.html index 845896a32..abc2fb7ae 100644 --- a/docs/owl-base/Owl_base_stats_dist_gamma/index.html +++ b/docs/owl-base/Owl_base_stats_dist_gamma/index.html @@ -1,2 +1,2 @@ -Owl_base_stats_dist_gamma (owl-base.Owl_base_stats_dist_gamma)

Module Owl_base_stats_dist_gamma

val std_gamma_rvs : shape:float -> float
val gamma_rvs : shape:float -> scale:float -> float
+Owl_base_stats_dist_gamma (owl-base.Owl_base_stats_dist_gamma)

Module Owl_base_stats_dist_gamma

val std_gamma_rvs : shape:float -> float
val gamma_rvs : shape:float -> scale:float -> float
diff --git a/docs/owl-base/Owl_base_stats_dist_gaussian/index.html b/docs/owl-base/Owl_base_stats_dist_gaussian/index.html index e8a1d8b7c..b7f73068e 100644 --- a/docs/owl-base/Owl_base_stats_dist_gaussian/index.html +++ b/docs/owl-base/Owl_base_stats_dist_gaussian/index.html @@ -1,2 +1,2 @@ -Owl_base_stats_dist_gaussian (owl-base.Owl_base_stats_dist_gaussian)

Module Owl_base_stats_dist_gaussian

val _u1 : float Stdlib.ref
val _u2 : float Stdlib.ref
val _case : bool Stdlib.ref
val _z0 : float Stdlib.ref
val _z1 : float Stdlib.ref
val std_gaussian_rvs : unit -> float
val gaussian_rvs : mu:float -> sigma:float -> float
+Owl_base_stats_dist_gaussian (owl-base.Owl_base_stats_dist_gaussian)

Module Owl_base_stats_dist_gaussian

val _u1 : float Stdlib.ref
val _u2 : float Stdlib.ref
val _case : bool Stdlib.ref
val _z0 : float Stdlib.ref
val _z1 : float Stdlib.ref
val std_gaussian_rvs : unit -> float
val gaussian_rvs : mu:float -> sigma:float -> float
diff --git a/docs/owl-base/Owl_base_stats_dist_gumbel1/index.html b/docs/owl-base/Owl_base_stats_dist_gumbel1/index.html index 728761ff6..e38756893 100644 --- a/docs/owl-base/Owl_base_stats_dist_gumbel1/index.html +++ b/docs/owl-base/Owl_base_stats_dist_gumbel1/index.html @@ -1,2 +1,2 @@ -Owl_base_stats_dist_gumbel1 (owl-base.Owl_base_stats_dist_gumbel1)

Module Owl_base_stats_dist_gumbel1

val gumbel1_rvs : a:float -> b:float -> float
+Owl_base_stats_dist_gumbel1 (owl-base.Owl_base_stats_dist_gumbel1)

Module Owl_base_stats_dist_gumbel1

val gumbel1_rvs : a:float -> b:float -> float
diff --git a/docs/owl-base/Owl_base_stats_dist_gumbel2/index.html b/docs/owl-base/Owl_base_stats_dist_gumbel2/index.html index df8a4c545..e4f9d6dfa 100644 --- a/docs/owl-base/Owl_base_stats_dist_gumbel2/index.html +++ b/docs/owl-base/Owl_base_stats_dist_gumbel2/index.html @@ -1,2 +1,2 @@ -Owl_base_stats_dist_gumbel2 (owl-base.Owl_base_stats_dist_gumbel2)

Module Owl_base_stats_dist_gumbel2

val gumbel2_rvs : a:float -> b:float -> float
+Owl_base_stats_dist_gumbel2 (owl-base.Owl_base_stats_dist_gumbel2)

Module Owl_base_stats_dist_gumbel2

val gumbel2_rvs : a:float -> b:float -> float
diff --git a/docs/owl-base/Owl_base_stats_dist_uniform/index.html b/docs/owl-base/Owl_base_stats_dist_uniform/index.html index 628684a53..e690614d0 100644 --- a/docs/owl-base/Owl_base_stats_dist_uniform/index.html +++ b/docs/owl-base/Owl_base_stats_dist_uniform/index.html @@ -1,2 +1,2 @@ -Owl_base_stats_dist_uniform (owl-base.Owl_base_stats_dist_uniform)

Module Owl_base_stats_dist_uniform

val uniform_int_rvs : int -> int
val std_uniform_rvs : unit -> float
val uniform_rvs : a:float -> b:float -> float
+Owl_base_stats_dist_uniform (owl-base.Owl_base_stats_dist_uniform)

Module Owl_base_stats_dist_uniform

val uniform_int_rvs : int -> int
val std_uniform_rvs : unit -> float
val uniform_rvs : a:float -> b:float -> float
val rand01_exclusive : unit -> float
diff --git a/docs/owl-base/Owl_base_stats_prng/index.html b/docs/owl-base/Owl_base_stats_prng/index.html index 66592c49e..a052e9e7d 100644 --- a/docs/owl-base/Owl_base_stats_prng/index.html +++ b/docs/owl-base/Owl_base_stats_prng/index.html @@ -1,2 +1,2 @@ -Owl_base_stats_prng (owl-base.Owl_base_stats_prng)

Module Owl_base_stats_prng

val init : int -> unit
val self_init : unit -> unit
val get_state : unit -> Stdlib.Random.State.t
val set_state : Stdlib.Random.State.t -> unit
+Owl_base_stats_prng (owl-base.Owl_base_stats_prng)

Module Owl_base_stats_prng

val init : int -> unit
val self_init : unit -> unit
val get_state : unit -> Stdlib.Random.State.t
val set_state : Stdlib.Random.State.t -> unit
diff --git a/docs/owl-base/Owl_computation/index.html b/docs/owl-base/Owl_computation/index.html index 7a4888a71..d5cf9e6a6 100644 --- a/docs/owl-base/Owl_computation/index.html +++ b/docs/owl-base/Owl_computation/index.html @@ -1,2 +1,2 @@ -Owl_computation (owl-base.Owl_computation)

Module Owl_computation

module Type = Owl_computation_type
module Shape = Owl_computation_shape
module Symbol = Owl_computation_symbol
module Operator = Owl_computation_operator
module Optimiser = Owl_computation_optimiser
module Graph = Owl_computation_graph
module Engine = Owl_computation_engine
+Owl_computation (owl-base.Owl_computation)

Module Owl_computation

module Type = Owl_computation_type
module Shape = Owl_computation_shape
module Symbol = Owl_computation_symbol
module Operator = Owl_computation_operator
module Optimiser = Owl_computation_optimiser
module Graph = Owl_computation_graph
module Engine = Owl_computation_engine
diff --git a/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Linalg/index.html index 82b7750a1..60ab2278e 100644 --- a/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_cpu_device.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_computation_cpu_device.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Mat/index.html b/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Mat/index.html index c234a3a35..e236fb2be 100644 --- a/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_device.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_computation_cpu_device.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Scalar/index.html index 375357424..bbebfed5e 100644 --- a/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_cpu_device.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_computation_cpu_device.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/index.html b/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/index.html index 20abd1c34..32656a756 100644 --- a/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/index.html +++ b/docs/owl-base/Owl_computation_cpu_device/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_cpu_device.Make.A)

Parameter Make.A

include Owl_types_ndarray_mutable.Sig
include Owl_types_ndarray_algodiff.Sig
include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_computation_cpu_device.Make.A)

Parameter Make.A

include Owl_types_ndarray_mutable.Sig
include Owl_types_ndarray_algodiff.Sig
include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_cpu_device/Make/index.html b/docs/owl-base/Owl_computation_cpu_device/Make/index.html index bcde10259..2f2d2b5e1 100644 --- a/docs/owl-base/Owl_computation_cpu_device/Make/index.html +++ b/docs/owl-base/Owl_computation_cpu_device/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_computation_cpu_device.Make)

Module Owl_computation_cpu_device.Make

Parameters

Signature

module A = A
type device = {
  1. device_type : Owl_types.device_type;
  2. initialised : bool;
}
type value =
  1. | ArrVal of A.arr
  2. | EltVal of A.elt
val make_device : unit -> device
val arr_to_value : A.arr -> value
val value_to_arr : value -> A.arr
val elt_to_value : A.elt -> value
val value_to_elt : value -> A.elt
val value_to_float : value -> float
val is_arr : value -> bool
val is_elt : value -> bool
+Make (owl-base.Owl_computation_cpu_device.Make)

Module Owl_computation_cpu_device.Make

Parameters

Signature

module A = A
type device = {
  1. device_type : Owl_types.device_type;
  2. initialised : bool;
}
type value =
  1. | ArrVal of A.arr
  2. | EltVal of A.elt
val make_device : unit -> device
val arr_to_value : A.arr -> value
val value_to_arr : value -> A.arr
val elt_to_value : A.elt -> value
val value_to_elt : value -> A.elt
val value_to_float : value -> float
val is_arr : value -> bool
val is_elt : value -> bool
diff --git a/docs/owl-base/Owl_computation_cpu_device/index.html b/docs/owl-base/Owl_computation_cpu_device/index.html index 5c164271b..3d9f6ee25 100644 --- a/docs/owl-base/Owl_computation_cpu_device/index.html +++ b/docs/owl-base/Owl_computation_cpu_device/index.html @@ -1,2 +1,2 @@ -Owl_computation_cpu_device (owl-base.Owl_computation_cpu_device)

Module Owl_computation_cpu_device

module Make (A : Owl_types.Ndarray_Mutable) : sig ... end
+Owl_computation_cpu_device (owl-base.Owl_computation_cpu_device)

Module Owl_computation_cpu_device

module Make (A : Owl_types.Ndarray_Mutable) : sig ... end
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Linalg/index.html index 14e932ee5..d8fa74c65 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Linalg)

Module Operator.Linalg

val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr
val svd : +Linalg (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Linalg)

Module Operator.Linalg

val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr
val sylvester : diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Mat/index.html index 25e3d0db2..a81da2413 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Mat)

Module Operator.Mat

+Mat (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Mat)

Module Operator.Mat

diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Scalar/index.html index 61d079b00..ba5faa801 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Scalar/index.html @@ -1,5 +1,5 @@ -Scalar (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Scalar)

Module Operator.Scalar

val add : +Scalar (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Scalar)

Module Operator.Scalar

val sub : diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 4141f3927..b7a0ee2b3 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index e40f92056..cbd64fced 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index 38f025f64..3b89d1afc 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index c513b8766..a5f4b1dea 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

Module Device.A

type arr = +A (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

Module Device.A

val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index 0c515e567..838435674 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,4 +1,4 @@ -Device (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

Module Type.Device

module A : sig ... end
type device = +Device (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

Module Type.Device

module A : sig ... end
val make_device : unit -> device
val arr_to_value : A.arr -> value
val value_to_arr : value -> A.arr
val elt_to_value : A.elt -> value
val value_to_elt : value -> A.elt
val value_to_float : value -> float
val is_arr : value -> bool
val is_elt : value -> bool
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index be5dc173e..1fe864b3c 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type)

Module Shape.Type

module Device : sig ... end
type state = +Type (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape.Type)

Module Shape.Type

module Device : sig ... end
and block = Make_Nested(Owl_computation_engine.Make_Graph(Owl_computation_cpu_device.Make(A))).Graph.Optimiser.Operator.Symbol.Shape.Type.block = diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/index.html index 30d41b300..e914303f9 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape)

Module Symbol.Shape

module Type : sig ... end
val infer_shape : +Shape (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol.Shape)

Module Symbol.Shape

module Type : sig ... end
val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/index.html index abc9c7a37..fc73c4d82 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol)

Module Operator.Symbol

module Shape : sig ... end
val op_to_str : Shape.Type.op -> string
val is_random_variable : Shape.Type.op -> bool
val refnum : 'a Owl_graph.node -> int
val node_shape : Shape.Type.attr Owl_graph.node -> int array
val node_numel : Shape.Type.attr Owl_graph.node -> int
val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool
val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit
val shape_to_str : int array option array -> string
val node_to_str : Shape.Type.attr Owl_graph.node -> string
val node_to_arr : Shape.Type.t -> Shape.Type.arr
val arr_to_node : Shape.Type.arr -> Shape.Type.t
val node_to_elt : Shape.Type.t -> Shape.Type.elt
val elt_to_node : Shape.Type.elt -> Shape.Type.t
val make_node : +Symbol (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator.Symbol)

Module Operator.Symbol

module Shape : sig ... end
val op_to_str : Shape.Type.op -> string
val is_random_variable : Shape.Type.op -> bool
val refnum : 'a Owl_graph.node -> int
val node_shape : Shape.Type.attr Owl_graph.node -> int array
val node_numel : Shape.Type.attr Owl_graph.node -> int
val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool
val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit
val shape_to_str : int array option array -> string
val node_to_str : Shape.Type.attr Owl_graph.node -> string
val node_to_arr : Shape.Type.t -> Shape.Type.arr
val arr_to_node : Shape.Type.arr -> Shape.Type.t
val node_to_elt : Shape.Type.t -> Shape.Type.elt
val elt_to_node : Shape.Type.elt -> Shape.Type.t
val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/index.html index e0724acbf..699420f38 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/Operator/index.html @@ -1,5 +1,5 @@ -Operator (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator)

Module Optimiser.Operator

module Symbol : sig ... end
val empty : int array -> Symbol.Shape.Type.arr
val zeros : int array -> Symbol.Shape.Type.arr
val ones : int array -> Symbol.Shape.Type.arr
val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr
val sequential : +Operator (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser.Operator)

Module Optimiser.Operator

module Symbol : sig ... end
val empty : int array -> Symbol.Shape.Type.arr
val zeros : int array -> Symbol.Shape.Type.arr
val ones : int array -> Symbol.Shape.Type.arr
val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr
val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/index.html index 0406cc9fa..5205de95c 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser)

Module Graph.Optimiser

module Operator : sig ... end
val estimate_complexity : 'a Owl_graph.node array -> int * int
val optimise_nodes : +Optimiser (owl-base.Owl_computation_cpu_engine.Make.Graph.Optimiser)

Module Graph.Optimiser

module Operator : sig ... end
val estimate_complexity : 'a Owl_graph.node array -> int * int
val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/index.html index 6202f97b6..cedb987e7 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_computation_cpu_engine.Make.Graph)

Module Make.Graph

module Optimiser : sig ... end
type graph = +Graph (owl-base.Owl_computation_cpu_engine.Make.Graph)

Module Make.Graph

module Optimiser : sig ... end
val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string
val graph_to_dot : graph -> string
val graph_to_trace : graph -> string
val save_graph : 'a -> string -> unit
val load_graph : string -> 'a * 'b
val invalidate_rvs : graph -> unit
val make_graph : diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Linalg/index.html index 0cb1f6aeb..b44d4a00e 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_cpu_engine.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_computation_cpu_engine.Make.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Mat/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Mat/index.html index dd10358a1..c9b807aba 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_engine.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_computation_cpu_engine.Make.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Scalar/index.html index 60814df9e..e6e732748 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_cpu_engine.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_computation_cpu_engine.Make.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/index.html index 52a6c08d7..ea1cd09ec 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_cpu_engine.Make.A)

Parameter Make.A

include Owl_types_ndarray_mutable.Sig
include Owl_types_ndarray_algodiff.Sig
include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_computation_cpu_engine.Make.A)

Parameter Make.A

include Owl_types_ndarray_mutable.Sig
include Owl_types_ndarray_algodiff.Sig
include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make/index.html index c0b222a5c..30b0efee8 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_computation_cpu_engine.Make)

Module Owl_computation_cpu_engine.Make

Parameters

Signature

include sig ... end
module Graph : sig ... end
val eval_graph : Graph.graph -> unit
module Optimiser = Graph.Optimiser
type graph = +Make (owl-base.Owl_computation_cpu_engine.Make)

Module Owl_computation_cpu_engine.Make

Parameters

Signature

include sig ... end
module Graph : sig ... end
val eval_graph : Graph.graph -> unit
module Optimiser = Graph.Optimiser
val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string
val graph_to_dot : graph -> string
val graph_to_trace : graph -> string
val save_graph : 'a -> string -> unit
val load_graph : string -> 'a * 'b
val invalidate_rvs : graph -> unit
val make_graph : diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Eval/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Eval/index.html index 0706fabc2..a49530bbb 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Eval/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Eval/index.html @@ -1,5 +1,5 @@ -CG_Eval (owl-base.Owl_computation_cpu_engine.Make_Nested.CG_Eval)

Module Make_Nested.CG_Eval

val invalidate_opt : +CG_Eval (owl-base.Owl_computation_cpu_engine.Make_Nested.CG_Eval)

Module Make_Nested.CG_Eval

val update_validity : Graph.Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Init/MultiMap/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Init/MultiMap/index.html index 58ce5e119..b95a7ef46 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Init/MultiMap/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Init/MultiMap/index.html @@ -1,2 +1,2 @@ -MultiMap (owl-base.Owl_computation_cpu_engine.Make_Nested.CG_Init.MultiMap)

Module CG_Init.MultiMap

type key = int
val empty : 'a t
val is_empty : 'a t -> bool
val mem : key -> 'a t -> bool
val add : key -> 'a -> 'a t -> 'a t
val remove : key -> 'a t -> 'a t
val find : key -> 'a t -> 'a
val max_binding : 'a t -> key * 'a
val find_first_opt : (key -> bool) -> 'a t -> (key * 'a) option
+MultiMap (owl-base.Owl_computation_cpu_engine.Make_Nested.CG_Init.MultiMap)

Module CG_Init.MultiMap

type key = int
val empty : 'a t
val is_empty : 'a t -> bool
val mem : key -> 'a t -> bool
val add : key -> 'a -> 'a t -> 'a t
val remove : key -> 'a t -> 'a t
val find : key -> 'a t -> 'a
val max_binding : 'a t -> key * 'a
val find_first_opt : (key -> bool) -> 'a t -> (key * 'a) option
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Init/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Init/index.html index e4743c27b..51d8d420a 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Init/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/CG_Init/index.html @@ -1,5 +1,5 @@ -CG_Init (owl-base.Owl_computation_cpu_engine.Make_Nested.CG_Init)

Module Make_Nested.CG_Init

module MultiMap : sig ... end
val split_00 : 'a -> 'b array * 'a
val split_01 : 'a -> 'a * 'b array
val split_02 : +CG_Init (owl-base.Owl_computation_cpu_engine.Make_Nested.CG_Init)

Module Make_Nested.CG_Init

module MultiMap : sig ... end
val split_00 : 'a -> 'b array * 'a
val split_01 : 'a -> 'a * 'b array
val split_02 : Graph.Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node -> Graph.Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> Graph.Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Linalg/index.html index 0cb5731a5..4d68f32ed 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Linalg)

Module Operator.Linalg

val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

TODO

val svd : +Linalg (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Linalg)

Module Operator.Linalg

inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

  • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

  • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
val lyapunov : + Symbol.Shape.Type.arr

sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

val discrete_lyapunov : + Symbol.Shape.Type.arr

lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

TODO

val linsolve : + Symbol.Shape.Type.arr

discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

  • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

TODO

linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

  • trans specifies whether to transpose the matrix A.
  • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

  • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
+ Symbol.Shape.Type.arr

dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

  • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Mat/index.html index d48a49368..7dbe27780 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Mat)

Module Operator.Mat

val eye : int -> Symbol.Shape.Type.arr

TODO

TODO

TODO

TODO

+Mat (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Mat)

Module Operator.Mat

val eye : int -> Symbol.Shape.Type.arr

eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

diagm ?k v creates a diagonal matrix from the array v.

  • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Scalar/index.html index 5f257458a..0bf7fcc85 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Scalar)

Module Operator.Scalar

val add : +Scalar (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Scalar)

Module Operator.Scalar

add a b returns the sum of the scalars a and b.

sub a b returns the difference of the scalars a and b.

mul a b returns the product of the scalars a and b.

div a b returns the quotient of the scalars a and b.

val atan2 : + Symbol.Shape.Type.elt

pow a b returns the scalar a raised to the power of b.

+ Symbol.Shape.Type.elt

atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

abs a returns the absolute value of the scalar a.

neg a returns the negation of the scalar a.

sqr a returns the square of the scalar a.

sqrt a returns the square root of the scalar a.

exp a returns the exponential of the scalar a.

log a returns the natural logarithm of the scalar a.

log2 a returns the base-2 logarithm of the scalar a.

log10 a returns the base-10 logarithm of the scalar a.

signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

floor a returns the greatest integer less than or equal to the scalar a.

ceil a returns the smallest integer greater than or equal to the scalar a.

round a returns the nearest integer to the scalar a.

sin a returns the sine of the scalar a.

cos a returns the cosine of the scalar a.

tan a returns the tangent of the scalar a.

sinh a returns the hyperbolic sine of the scalar a.

cosh a returns the hyperbolic cosine of the scalar a.

tanh a returns the hyperbolic tangent of the scalar a.

asin a returns the arcsine of the scalar a.

acos a returns the arccosine of the scalar a.

atan a returns the arctangent of the scalar a.

asinh a returns the inverse hyperbolic sine of the scalar a.

acosh a returns the inverse hyperbolic cosine of the scalar a.

atanh a returns the inverse hyperbolic tangent of the scalar a.

relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

dawsn a returns Dawson's function of the scalar a.

sigmoid a returns the sigmoid function of the scalar a.

diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 91c1fc3fa..fa58c99d8 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : +Linalg (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

Module A.Linalg

val inv : arr -> arr
val logdet : arr -> elt
val chol : ?upper:bool -> arr -> arr
val svd : ?thin:bool -> arr -> arr * arr * arr
val qr : arr -> arr * arr
val lq : arr -> arr * arr
val sylvester : arr -> arr -> arr -> arr
val lyapunov : arr -> arr -> arr
val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index c6bf71694..f643b9e97 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
+Mat (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

Module A.Mat

val diagm : ?k:int -> arr -> arr
val triu : ?k:int -> arr -> arr
val tril : ?k:int -> arr -> arr
val eye : int -> arr
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index 260d8520b..bda33aad8 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
+Scalar (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

Module A.Scalar

val add : elt -> elt -> elt
val sub : elt -> elt -> elt
val mul : elt -> elt -> elt
val div : elt -> elt -> elt
val pow : elt -> elt -> elt
val atan2 : elt -> elt -> elt
val abs : elt -> elt
val neg : elt -> elt
val sqr : elt -> elt
val sqrt : elt -> elt
val exp : elt -> elt
val log : elt -> elt
val log2 : elt -> elt
val log10 : elt -> elt
val signum : elt -> elt
val floor : elt -> elt
val ceil : elt -> elt
val round : elt -> elt
val sin : elt -> elt
val cos : elt -> elt
val tan : elt -> elt
val sinh : elt -> elt
val cosh : elt -> elt
val tanh : elt -> elt
val asin : elt -> elt
val acos : elt -> elt
val atan : elt -> elt
val asinh : elt -> elt
val acosh : elt -> elt
val atanh : elt -> elt
val relu : elt -> elt
val dawsn : elt -> elt
val sigmoid : elt -> elt
diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index d85bc9d0f..a2ff9c39b 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

Module Device.A

include Owl_types_ndarray_algodiff.Sig
include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : +A (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

Module Device.A

include Owl_types_ndarray_algodiff.Sig
include Owl_types_ndarray_eltcmp.Sig
include Owl_types_ndarray_basic.Sig
type arr
type elt
val empty : int array -> arr
val zeros : int array -> arr
val ones : int array -> arr
val create : int array -> elt -> arr
val sequential : ?a:elt -> ?step:elt -> int array -> arr
val uniform : ?a:elt -> ?b:elt -> int array -> arr
val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
val bernoulli : ?p:elt -> int array -> arr
val init : int array -> (int -> elt) -> arr
val init_nd : int array -> (int array -> elt) -> arr
val shape : arr -> int array
val numel : arr -> int
val get : arr -> int array -> elt
val set : arr -> int array -> elt -> unit
val get_slice : int list list -> arr -> arr
val set_slice : int list list -> arr -> arr -> unit
val get_fancy : Owl_types_common.index list -> arr -> arr
val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
val copy : arr -> arr
val copy_ : out:arr -> arr -> unit
val reset : arr -> unit
val reshape : arr -> int array -> arr
val reverse : arr -> arr
val tile : arr -> int array -> arr
val repeat : arr -> int array -> arr
val concatenate : ?axis:int -> arr array -> arr
val stack : ?axis:int -> arr array -> arr
val split : ?axis:int -> int array -> arr -> arr array
val expand : ?hi:bool -> arr -> int -> arr
val squeeze : ?axis:int array -> arr -> arr
val draw : ?axis:int -> arr -> int -> arr * int array
val map : (elt -> elt) -> arr -> arr
val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
val one_hot : int -> arr -> arr
val pad : ?v:elt -> int list list -> arr -> arr
val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index 211aecdf1..428d80057 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

Module Type.Device

Type definition
type device

TODO

type value

TODO

Core functions
val make_device : unit -> device

TODO

val arr_to_value : A.arr -> value

TODO

val value_to_arr : value -> A.arr

TODO

val elt_to_value : A.elt -> value

TODO

val value_to_elt : value -> A.elt

TODO

val value_to_float : value -> float

TODO

val is_arr : value -> bool

TODO

val is_elt : value -> bool

TODO

+Device (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

Module Type.Device

Type definition
type device

TODO

type value

TODO

Core functions
val make_device : unit -> device

TODO

val arr_to_value : A.arr -> value

TODO

val value_to_arr : value -> A.arr

TODO

val elt_to_value : A.elt -> value

TODO

val value_to_elt : value -> A.elt

TODO

val value_to_float : value -> float

TODO

val is_arr : value -> bool

TODO

val is_elt : value -> bool

TODO

diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index 7cc9a412e..9fdbe3817 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type)

Module Shape.Type

Type definition
type state =
  1. | Valid
  2. | Invalid
    (*

    TODO

    *)

TODO

and block = {
  1. size : int;
  2. block_id : int;
  3. mutable active : t option;
  4. mutable memory : Device.value;
  5. mutable nodes : t list;
}

block type keeps a reference to a block of memory and to the nodes sharing that block.

and attr = {
  1. mutable op : op;
  2. mutable freeze : bool;
  3. mutable reuse : bool;
  4. mutable state : state;
  5. mutable shape : int array option array;
  6. mutable value : Device.value array;
  7. mutable block : block array option;
}

TODO

and arr =
  1. | Arr of t
and elt =
  1. | Elt of t
and op =
  1. | Noop
  2. | Var
  3. | Const
  4. | Empty of int array
  5. | Zeros of int array
  6. | Ones of int array
  7. | Create of int array
  8. | Sequential of int array
  9. | Uniform of int array
  10. | Gaussian of int array
  11. | Bernoulli of int array
  12. | Init of int array * int -> elt
  13. | Get of int array
  14. | Set of int array
  15. | GetSlice of int list list
  16. | SetSlice of int list list
  17. | GetFancy of Owl_types_common.index list
  18. | SetFancy of Owl_types_common.index list
  19. | Copy
  20. | Reset
  21. | Reshape of int array
  22. | Reverse
  23. | Tile of int array
  24. | Repeat of int array
  25. | Pad of elt * int list list
  26. | Concatenate of int
  27. | Stack of int
  28. | Split of int * int array
  29. | Draw of int * int
  30. | Map of elt -> elt
  31. | Fold of int * elt -> elt -> elt
  32. | Scan of int * elt -> elt -> elt
  33. | OneHot of int
  34. | OfArray of int array
  35. | Delay of Device.A.arr -> Device.A.arr
  36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
  37. | LazyPrint of int option +Type (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape.Type)

    Module Shape.Type

    Type definition
    type state =
    1. | Valid
    2. | Invalid
      (*

      TODO

      *)

    TODO

    and block = {
    1. size : int;
    2. block_id : int;
    3. mutable active : t option;
    4. mutable memory : Device.value;
    5. mutable nodes : t list;
    }

    block type keeps a reference to a block of memory and to the nodes sharing that block.

    and attr = {
    1. mutable op : op;
    2. mutable freeze : bool;
    3. mutable reuse : bool;
    4. mutable state : state;
    5. mutable shape : int array option array;
    6. mutable value : Device.value array;
    7. mutable block : block array option;
    }

    TODO

    and arr =
    1. | Arr of t
    and elt =
    1. | Elt of t
    and op =
    1. | Noop
    2. | Var
    3. | Const
    4. | Empty of int array
    5. | Zeros of int array
    6. | Ones of int array
    7. | Create of int array
    8. | Sequential of int array
    9. | Uniform of int array
    10. | Gaussian of int array
    11. | Bernoulli of int array
    12. | Init of int array * int -> elt
    13. | Get of int array
    14. | Set of int array
    15. | GetSlice of int list list
    16. | SetSlice of int list list
    17. | GetFancy of Owl_types_common.index list
    18. | SetFancy of Owl_types_common.index list
    19. | Copy
    20. | Reset
    21. | Reshape of int array
    22. | Reverse
    23. | Tile of int array
    24. | Repeat of int array
    25. | Pad of elt * int list list
    26. | Concatenate of int
    27. | Stack of int
    28. | Split of int * int array
    29. | Draw of int * int
    30. | Map of elt -> elt
    31. | Fold of int * elt -> elt -> elt
    32. | Scan of int * elt -> elt -> elt
    33. | OneHot of int
    34. | OfArray of int array
    35. | Delay of Device.A.arr -> Device.A.arr
    36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
    37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
    38. | Abs
    39. | Neg
    40. | Floor
    41. | Ceil
    42. | Round
    43. | Sqr
    44. | Sqrt
    45. | Log
    46. | Log2
    47. | Log10
    48. | Exp
    49. | Sin
    50. | Cos
    51. | Tan
    52. | Sinh
    53. | Cosh
    54. | Tanh
    55. | Asin
    56. | Acos
    57. | Atan
    58. | Asinh
    59. | Acosh
    60. | Atanh
    61. | Min of bool * int
    62. | Max of bool * int
    63. | Sum of bool * int
    64. | SumReduce of int array
    65. | Signum
    66. | Sigmoid
    67. | Relu
    68. | Dawsn
    69. | Min'
    70. | Max'
    71. | Sum'
    72. | LogSumExp'
    73. | LogSumExp of bool * int
    74. | L1norm'
    75. | L2norm'
    76. | L2NormSqr'
    77. | ClipByValue
    78. | ClipByL2norm
    79. | Pow
    80. | ScalarPow
    81. | PowScalar
    82. | Atan2
    83. | ScalarAtan2
    84. | Atan2Scalar
    85. | Hypot
    86. | Min2
    87. | Max2
    88. | Add
    89. | Sub
    90. | Mul
    91. | Div
    92. | AddScalar
    93. | SubScalar
    94. | MulScalar
    95. | DivScalar
    96. | ScalarAdd
    97. | ScalarSub
    98. | ScalarMul
    99. | ScalarDiv
    100. | FMA
    101. | EltEqual
    102. | EltNotEqual
    103. | EltLess
    104. | EltGreater
    105. | EltLessEqual
    106. | EltGreaterEqual
    107. | EltEqualScalar
    108. | EltNotEqualScalar
    109. | EltLessScalar
    110. | EltGreaterScalar
    111. | EltLessEqualScalar
    112. | EltGreaterEqualScalar
    113. | Conv1d of Owl_types_common.padding * int array
    114. | Conv2d of Owl_types_common.padding * int array
    115. | Conv3d of Owl_types_common.padding * int array
    116. | TransposeConv1d of Owl_types_common.padding * int array
    117. | TransposeConv2d of Owl_types_common.padding * int array
    118. | TransposeConv3d of Owl_types_common.padding * int array
    119. | DilatedConv1d of Owl_types_common.padding * int array * int array
    120. | DilatedConv2d of Owl_types_common.padding * int array * int array
    121. | DilatedConv3d of Owl_types_common.padding * int array * int array
    122. | MaxPool1d of Owl_types_common.padding * int array * int array
    123. | MaxPool2d of Owl_types_common.padding * int array * int array
    124. | MaxPool3d of Owl_types_common.padding * int array * int array
    125. | AvgPool1d of Owl_types_common.padding * int array * int array
    126. | AvgPool2d of Owl_types_common.padding * int array * int array
    127. | AvgPool3d of Owl_types_common.padding * int array * int array
    128. | UpSampling2d of int array
    129. | Conv1dBackwardInput of int array
    130. | Conv1dBackwardKernel of int array
    131. | Conv2dBackwardInput of int array
    132. | Conv2dBackwardKernel of int array
    133. | Conv3dBackwardInput of int array
    134. | Conv3dBackwardKernel of int array
    135. | TransposeConv1dBackwardInput of int array
    136. | TransposeConv1dBackwardKernel of int array
    137. | TransposeConv2dBackwardInput of int array
    138. | TransposeConv2dBackwardKernel of int array
    139. | TransposeConv3dBackwardInput of int array
    140. | TransposeConv3dBackwardKernel of int array
    141. | DilatedConv1dBackwardInput of int array * int array
    142. | DilatedConv1dBackwardKernel of int array * int array
    143. | DilatedConv2dBackwardInput of int array * int array
    144. | DilatedConv2dBackwardKernel of int array * int array
    145. | DilatedConv3dBackwardInput of int array * int array
    146. | DilatedConv3dBackwardKernel of int array * int array
    147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
    148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
    149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
    150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
    151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
    152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
    153. | UpSampling2dBackward of int array
    154. | RowNum
    155. | ColNum
    156. | Row
    157. | Rows of int array
    158. | CopyRowTo
    159. | CopyColTo
    160. | Dot of bool * bool * elt * elt
    161. | Inv
    162. | Trace
    163. | Transpose of int array
    164. | ToRows
    165. | OfRows
    166. | Scalar_Add
    167. | Scalar_Sub
    168. | Scalar_Mul
    169. | Scalar_Div
    170. | Scalar_Pow
    171. | Scalar_Atan2
    172. | Scalar_Abs
    173. | Scalar_Neg
    174. | Scalar_Sqr
    175. | Scalar_Sqrt
    176. | Scalar_Exp
    177. | Scalar_Log
    178. | Scalar_Log2
    179. | Scalar_Log10
    180. | Scalar_Signum
    181. | Scalar_Floor
    182. | Scalar_Ceil
    183. | Scalar_Round
    184. | Scalar_Sin
    185. | Scalar_Cos
    186. | Scalar_Tan
    187. | Scalar_Sinh
    188. | Scalar_Cosh
    189. | Scalar_Tanh
    190. | Scalar_Asin
    191. | Scalar_Acos
    192. | Scalar_Atan
    193. | Scalar_Asinh
    194. | Scalar_Acosh
    195. | Scalar_Atanh
    196. | Scalar_Relu
    197. | Scalar_Dawsn
    198. | Scalar_Sigmoid
    199. | Fused_Adagrad of float * float
      (*

      TODO

      *)
    diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html index 9db09c229..60c0208b3 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape)

    Module Symbol.Shape

    Core functions
    val infer_shape : +Shape (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol.Shape)

    Module Symbol.Shape

    Core functions
    val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

    TODO

    diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/index.html index 080e852f9..9b54a442b 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol)

    Module Operator.Symbol

    Core functions
    val op_to_str : Shape.Type.op -> string

    TODO

    val is_random_variable : Shape.Type.op -> bool

    TODO

    val refnum : 'a Owl_graph.node -> int

    TODO

    val node_shape : Shape.Type.attr Owl_graph.node -> int array

    TODO

    val node_numel : Shape.Type.attr Owl_graph.node -> int

    TODO

    val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

    TODO

    val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

    TODO

    val shape_to_str : int array option array -> string

    TODO

    val node_to_str : Shape.Type.attr Owl_graph.node -> string

    TODO

    val node_to_arr : Shape.Type.t -> Shape.Type.arr

    TODO

    val arr_to_node : Shape.Type.arr -> Shape.Type.t

    TODO

    val node_to_elt : Shape.Type.t -> Shape.Type.elt

    TODO

    val elt_to_node : Shape.Type.elt -> Shape.Type.t

    TODO

    val make_node : +Symbol (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator.Symbol)

    Module Operator.Symbol

    Core functions
    val op_to_str : Shape.Type.op -> string

    TODO

    val is_random_variable : Shape.Type.op -> bool

    TODO

    val refnum : 'a Owl_graph.node -> int

    TODO

    val node_shape : Shape.Type.attr Owl_graph.node -> int array

    TODO

    val node_numel : Shape.Type.attr Owl_graph.node -> int

    TODO

    val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

    TODO

    val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

    TODO

    val shape_to_str : int array option array -> string

    TODO

    val node_to_str : Shape.Type.attr Owl_graph.node -> string

    TODO

    val node_to_arr : Shape.Type.t -> Shape.Type.arr

    TODO

    val arr_to_node : Shape.Type.arr -> Shape.Type.t

    TODO

    val node_to_elt : Shape.Type.t -> Shape.Type.elt

    TODO

    val elt_to_node : Shape.Type.elt -> Shape.Type.t

    TODO

    val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/index.html index b874c8837..4918df05e 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator)

    Module Optimiser.Operator

    Vectorised functions
    val empty : int array -> Symbol.Shape.Type.arr

    TODO

    val zeros : int array -> Symbol.Shape.Type.arr

    TODO

    val ones : int array -> Symbol.Shape.Type.arr

    TODO

    val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

    TODO

    val sequential : +Operator (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser.Operator)

    Module Optimiser.Operator

    Vectorised functions

    noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

    val empty : int array -> Symbol.Shape.Type.arr

    empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

    val zeros : int array -> Symbol.Shape.Type.arr

    zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

    val ones : int array -> Symbol.Shape.Type.arr

    ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

    val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

    create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

    val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

    TODO

    val uniform : + Symbol.Shape.Type.arr

    sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

    val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

    TODO

    val gaussian : + Symbol.Shape.Type.arr

    uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

    val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

    TODO

    val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

    TODO

    val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

    TODO

    val init_nd : + Symbol.Shape.Type.arr

    gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

    val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

    bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

    val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

    init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

    val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

    TODO

    val shape : Symbol.Shape.Type.arr -> int array

    TODO

    val numel : Symbol.Shape.Type.arr -> int

    TODO

    TODO

    val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

    TODO

    val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

    TODO

    val set_slice : + Symbol.Shape.Type.arr

    init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

    val shape : Symbol.Shape.Type.arr -> int array

    shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

    val numel : Symbol.Shape.Type.arr -> int

    numel arr returns the total number of elements in the array arr.

    get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

    val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

    set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

    val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

    get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

    val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

    TODO

    val get_fancy : + unit

    set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

    val set_fancy : + Symbol.Shape.Type.arr

    get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

    val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

    TODO

    val copy_ : out:'a -> 'b -> 'c

    TODO

    val reset : Symbol.Shape.Type.arr -> unit

    TODO

    val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    TODO

    val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    TODO

    val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    TODO

    val pad : + unit

    set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

    copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

    val copy_ : out:'a -> 'b -> 'c

    copy_ ~out src copies the contents of the array src into the pre-allocated array out.

    val reset : Symbol.Shape.Type.arr -> unit

    reset arr sets all elements of the array arr to zero.

    val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

    reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

    val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

    val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

    TODO

    val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

    TODO

    val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

    TODO

    val concatenate : + Symbol.Shape.Type.arr

    pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

    val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

    expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

    val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

    squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

    val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

    TODO

    val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

    TODO

    val concat : + Symbol.Shape.Type.arr

    concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

    val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

    stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

    val split : ?axis:int -> 'a -> 'b -> 'c

    TODO

    concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

    val split : ?axis:int -> 'a -> 'b -> 'c

    split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

    • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
    val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

    TODO

    val map : + Symbol.Shape.Type.arr * 'a array

    draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

    map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

    fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

    TODO

    val delay : + Symbol.Shape.Type.arr

    scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

    one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

    delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

    val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

    val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

    TODO

    lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

    val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

    print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

    • max_row is an optional parameter specifying the maximum number of rows to print.
    • max_col is an optional parameter specifying the maximum number of columns to print.
    • header is an optional parameter to include a header in the output.
    • fmt is an optional parameter to specify the format of the output.

    abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

    neg arr negates each element in the array arr. Returns a new array with each element negated.

    floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

    ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

    round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

    sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

    sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

    log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

    log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

    log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

    exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

    sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

    cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

    tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

    sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

    cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

    tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

    asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

    acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

    atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

    asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

    acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

    atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

    val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

    • axis specifies the axis along which to compute the minimum.
    • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
    val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

    • axis specifies the axis along which to compute the maximum.
    • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
    val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val sum_reduce : + Symbol.Shape.Type.arr

    sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

    • axis specifies the axis along which to compute the sum.
    • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
    val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val log_sum_exp : + Symbol.Shape.Type.arr

    sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

    • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

    signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

    sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

    relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

    dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

    min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

    max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

    sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

    log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

    val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val clip_by_value : + Symbol.Shape.Type.arr

    log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

    • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
    • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

    l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

    l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

    l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

    val clip_by_l2norm : + Symbol.Shape.Type.arr

    clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

    • amin specifies the minimum value to clip to.
    • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

    clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

    val scalar_pow : + Symbol.Shape.Type.arr

    pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

    val pow_scalar : + Symbol.Shape.Type.arr

    scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

    val atan2 : + Symbol.Shape.Type.arr

    pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

    val scalar_atan2 : + Symbol.Shape.Type.arr

    atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

    val atan2_scalar : + Symbol.Shape.Type.arr

    scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

    val hypot : + Symbol.Shape.Type.arr

    atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

    hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

    min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

    max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

    add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

    sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

    mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

    val add_scalar : + Symbol.Shape.Type.arr

    div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

    val sub_scalar : + Symbol.Shape.Type.arr

    add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

    val mul_scalar : + Symbol.Shape.Type.arr

    sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

    val div_scalar : + Symbol.Shape.Type.arr

    mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

    val scalar_add : + Symbol.Shape.Type.arr

    div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

    val scalar_sub : + Symbol.Shape.Type.arr

    scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

    val scalar_mul : + Symbol.Shape.Type.arr

    scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

    val scalar_div : + Symbol.Shape.Type.arr

    scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

    scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

    val elt_equal : + Symbol.Shape.Type.arr

    fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

    val elt_not_equal : + Symbol.Shape.Type.arr

    elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

    val elt_less : + Symbol.Shape.Type.arr

    elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

    val elt_greater : + Symbol.Shape.Type.arr

    elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

    val elt_less_equal : + Symbol.Shape.Type.arr

    elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

    val elt_greater_equal : + Symbol.Shape.Type.arr

    elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

    val elt_equal_scalar : + Symbol.Shape.Type.arr

    elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

    val elt_not_equal_scalar : + Symbol.Shape.Type.arr

    elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

    val elt_less_scalar : + Symbol.Shape.Type.arr

    elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

    val elt_greater_scalar : + Symbol.Shape.Type.arr

    elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

    val elt_less_equal_scalar : + Symbol.Shape.Type.arr

    elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

    TODO

    val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

    elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

    TODO

    val conv1d : + Symbol.Shape.Type.arr

    elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

    val conv2d : + Symbol.Shape.Type.arr

    conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

    • padding specifies the padding strategy (default is "valid").
    • strides specifies the stride length. Returns a new array with the result of the convolution.
    val conv3d : + Symbol.Shape.Type.arr

    conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

    • padding specifies the padding strategy (default is "valid").
    • strides specifies the stride length. Returns a new array with the result of the convolution.
    val transpose_conv1d : + Symbol.Shape.Type.arr

    conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

    • padding specifies the padding strategy (default is "valid").
    • strides specifies the stride length. Returns a new array with the result of the convolution.
    val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

    TODO

    val transpose_conv2d : + Symbol.Shape.Type.arr

    transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

    • padding specifies the padding strategy (default is "valid").
    • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
    val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

    TODO

    val transpose_conv3d : + Symbol.Shape.Type.arr

    transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

    • padding specifies the padding strategy (default is "valid").
    • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
    val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

    TODO

    val dilated_conv1d : + Symbol.Shape.Type.arr

    transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

    • padding specifies the padding strategy (default is "valid").
    • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
    val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val dilated_conv2d : + Symbol.Shape.Type.arr

    dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

    • padding specifies the padding strategy (default is "valid").
    • strides specifies the stride length.
    • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
    val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val dilated_conv3d : + Symbol.Shape.Type.arr

    dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

    • padding specifies the padding strategy (default is "valid").
    • strides specifies the stride length.
    • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
    val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val max_pool1d : + Symbol.Shape.Type.arr

    dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

    • padding specifies the padding strategy (default is "valid").
    • strides specifies the stride length.
    • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
    val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val max_pool2d : + Symbol.Shape.Type.arr

    max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

    • padding specifies the padding strategy (default is "valid").
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length. Returns a new array with the result of the max pooling.
    val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val max_pool3d : + Symbol.Shape.Type.arr

    max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

    • padding specifies the padding strategy (default is "valid").
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length. Returns a new array with the result of the max pooling.
    val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val avg_pool1d : + Symbol.Shape.Type.arr

    max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

    • padding specifies the padding strategy (default is "valid").
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length. Returns a new array with the result of the max pooling.
    val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val avg_pool2d : + Symbol.Shape.Type.arr

    avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

    • padding specifies the padding strategy (default is "valid").
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length. Returns a new array with the result of the average pooling.
    val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val avg_pool3d : + Symbol.Shape.Type.arr

    avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

    • padding specifies the padding strategy (default is "valid").
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length. Returns a new array with the result of the average pooling.
    val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    TODO

    val conv1d_backward_input : + Symbol.Shape.Type.arr

    avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

    • padding specifies the padding strategy (default is "valid").
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length. Returns a new array with the result of the average pooling.
    val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    upsampling2d input size performs a 2-dimensional upsampling on the input array.

    • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

    TODO

    val conv1d_backward_kernel : + Symbol.Shape.Type.arr

    conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

    • input is the original input array.
    • kernel is the convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
    val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val conv2d_backward_input : + Symbol.Shape.Type.arr

    conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

    • input is the original input array.
    • kernel is the convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

    TODO

    val conv2d_backward_kernel : + Symbol.Shape.Type.arr

    conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

    • input is the original input array.
    • kernel is the convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
    val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val conv3d_backward_input : + Symbol.Shape.Type.arr

    conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

    • input is the original input array.
    • kernel is the convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

    TODO

    val conv3d_backward_kernel : + Symbol.Shape.Type.arr

    conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

    • input is the original input array.
    • kernel is the convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
    val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

    conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

    • input is the original input array.
    • kernel is the convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
    val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

    transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

    • input is the original input array.
    • kernel is the transposed convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
    val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

    transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

    • input is the original input array.
    • kernel is the transposed convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
    val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

    transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

    • input is the original input array.
    • kernel is the transposed convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
    val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

    transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

    • input is the original input array.
    • kernel is the transposed convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
    val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

    transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

    • input is the original input array.
    • kernel is the transposed convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
    val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

    transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

    • input is the original input array.
    • kernel is the transposed convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
    val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

    dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

    • input is the original input array.
    • kernel is the dilated convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • dilations specifies the dilation rate.
    • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
    val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

    dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

    • input is the original input array.
    • kernel is the dilated convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • dilations specifies the dilation rate.
    • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
    val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

    dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

    • input is the original input array.
    • kernel is the dilated convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • dilations specifies the dilation rate.
    • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
    val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

    dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

    • input is the original input array.
    • kernel is the dilated convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • dilations specifies the dilation rate.
    • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
    val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

    dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

    • input is the original input array.
    • kernel is the dilated convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • dilations specifies the dilation rate.
    • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
    val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val max_pool1d_backward : + Symbol.Shape.Type.arr

    dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

    • input is the original input array.
    • kernel is the dilated convolutional kernel used during the forward pass.
    • strides specifies the stride length.
    • dilations specifies the dilation rate.
    • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
    val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val max_pool2d_backward : + Symbol.Shape.Type.arr

    max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

    • padding specifies the padding strategy used during the forward pass.
    • input is the original input array.
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
    val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val max_pool3d_backward : + Symbol.Shape.Type.arr

    max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

    • padding specifies the padding strategy used during the forward pass.
    • input is the original input array.
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
    val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val avg_pool1d_backward : + Symbol.Shape.Type.arr

    max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

    • padding specifies the padding strategy used during the forward pass.
    • input is the original input array.
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
    val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val avg_pool2d_backward : + Symbol.Shape.Type.arr

    avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

    • padding specifies the padding strategy used during the forward pass.
    • input is the original input array.
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
    val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val avg_pool3d_backward : + Symbol.Shape.Type.arr

    avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

    • padding specifies the padding strategy used during the forward pass.
    • input is the original input array.
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
    val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val upsampling2d_backward : + Symbol.Shape.Type.arr

    avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

    • padding specifies the padding strategy used during the forward pass.
    • input is the original input array.
    • pool_size specifies the size of the pooling window.
    • strides specifies the stride length.
    • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
    val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val row_num : Symbol.Shape.Type.arr -> int

    TODO

    val col_num : Symbol.Shape.Type.arr -> int

    TODO

    val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    TODO

    val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

    TODO

    val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

    TODO

    TODO

    upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

    • input is the original input array.
    • size specifies the upsampling factors for each dimension.
    • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
    val row_num : Symbol.Shape.Type.arr -> int

    row_num arr returns the number of rows in the array arr.

    val col_num : Symbol.Shape.Type.arr -> int

    col_num arr returns the number of columns in the array arr.

    row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

    val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

    rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

    val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

    copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

    val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

    copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

    diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

    trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

    val transpose : + Symbol.Shape.Type.arr

    dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

    val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val to_rows : Symbol.Shape.Type.arr -> 'a array

    TODO

    TODO

    val to_cols : Symbol.Shape.Type.arr -> 'a array

    TODO

    TODO

    val of_array : + Symbol.Shape.Type.arr

    transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

    val to_rows : Symbol.Shape.Type.arr -> 'a array

    to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

    of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

    val to_cols : Symbol.Shape.Type.arr -> 'a array

    to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

    of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

    val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

    TODO

    val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

    TODO

    val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

    TODO

    Scalar functions
    module Scalar : sig ... end
    module Mat : sig ... end
    module Linalg : sig ... end
    + Symbol.Shape.Type.arr

    of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

    val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

    of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

    val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

    to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

    Scalar functions
    module Scalar : sig ... end
    module Mat : sig ... end
    module Linalg : sig ... end
    diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/index.html index 31426e937..7f39fe904 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser)

    Module Graph.Optimiser

    Core functions
    val estimate_complexity : 'a Owl_graph.node array -> int * int

    TODO

    val optimise_nodes : +Optimiser (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph.Optimiser)

    Module Graph.Optimiser

    Core functions
    val estimate_complexity : 'a Owl_graph.node array -> int * int

    TODO

    val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

    TODO

    diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/index.html index 71e8abbeb..1959f41bc 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/argument-1-Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph)

    Parameter Make_Nested.Graph

    Type definition
    type graph

    TODO

    Core functions
    val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

    TODO

    val graph_to_dot : graph -> string

    TODO

    val graph_to_trace : graph -> string

    TODO

    val save_graph : 'a -> string -> unit

    TODO

    val load_graph : string -> 'a * 'b

    TODO

    val collect_rvs : +Graph (owl-base.Owl_computation_cpu_engine.Make_Nested.Graph)

    Parameter Make_Nested.Graph

    Type definition
    type graph

    TODO

    Core functions
    val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

    TODO

    val graph_to_dot : graph -> string

    TODO

    val graph_to_trace : graph -> string

    TODO

    val save_graph : 'a -> string -> unit

    TODO

    val load_graph : string -> 'a * 'b

    TODO

    val invalidate_rvs : graph -> unit

    TODO

    val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/index.html b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/index.html index 85234d86d..d024484c9 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/Make_Nested/index.html @@ -1,4 +1,4 @@ -Make_Nested (owl-base.Owl_computation_cpu_engine.Make_Nested)

    Module Owl_computation_cpu_engine.Make_Nested

    Parameters

    Signature

    module Graph = Graph
    module CG_Init : sig ... end
    module CG_Eval : sig ... end
    val eval_gen : +Make_Nested (owl-base.Owl_computation_cpu_engine.Make_Nested)

    Module Owl_computation_cpu_engine.Make_Nested

    Parameters

    Signature

    module Graph = Graph
    module CG_Init : sig ... end
    module CG_Eval : sig ... end
    val eval_graph : Graph.graph -> unit
    diff --git a/docs/owl-base/Owl_computation_cpu_engine/index.html b/docs/owl-base/Owl_computation_cpu_engine/index.html index f54f94f26..3a1c2aa79 100644 --- a/docs/owl-base/Owl_computation_cpu_engine/index.html +++ b/docs/owl-base/Owl_computation_cpu_engine/index.html @@ -1,2 +1,2 @@ -Owl_computation_cpu_engine (owl-base.Owl_computation_cpu_engine)

    Module Owl_computation_cpu_engine

    module Make (A : Owl_types.Ndarray_Mutable) : sig ... end
    +Owl_computation_cpu_engine (owl-base.Owl_computation_cpu_engine)

    Module Owl_computation_cpu_engine

    module Make (A : Owl_types.Ndarray_Mutable) : sig ... end
    diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Linalg/index.html index 32cfb3251..f6b66af4d 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Linalg)

    Module Operator.Linalg

    val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

    TODO

    val svd : +Linalg (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Linalg)

    Module Operator.Linalg

    inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

    logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

    val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

    chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

    • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

    qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

    lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

    svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

    • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
    val lyapunov : + Symbol.Shape.Type.arr

    sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

    val discrete_lyapunov : + Symbol.Shape.Type.arr

    lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

    val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    val linsolve : + Symbol.Shape.Type.arr

    discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

    • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
    val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

    TODO

    linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

    • trans specifies whether to transpose the matrix A.
    • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

    care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

    • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
    + Symbol.Shape.Type.arr

    dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

    • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
    diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Mat/index.html index bf982b3f7..7d820225f 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Mat)

    Module Operator.Mat

    val eye : int -> Symbol.Shape.Type.arr

    TODO

    TODO

    TODO

    TODO

    +Mat (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Mat)

    Module Operator.Mat

    val eye : int -> Symbol.Shape.Type.arr

    eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

    diagm ?k v creates a diagonal matrix from the array v.

    • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

    triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

    tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

    diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Scalar/index.html index 9903501bf..8a8ea925f 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Scalar)

    Module Operator.Scalar

    val add : +Scalar (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Scalar)

    Module Operator.Scalar

    add a b returns the sum of the scalars a and b.

    sub a b returns the difference of the scalars a and b.

    mul a b returns the product of the scalars a and b.

    div a b returns the quotient of the scalars a and b.

    val atan2 : + Symbol.Shape.Type.elt

    pow a b returns the scalar a raised to the power of b.

    + Symbol.Shape.Type.elt

    atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

    abs a returns the absolute value of the scalar a.

    neg a returns the negation of the scalar a.

    sqr a returns the square of the scalar a.

    sqrt a returns the square root of the scalar a.

    exp a returns the exponential of the scalar a.

    log a returns the natural logarithm of the scalar a.

    log2 a returns the base-2 logarithm of the scalar a.

    log10 a returns the base-10 logarithm of the scalar a.

    signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

    floor a returns the greatest integer less than or equal to the scalar a.

    ceil a returns the smallest integer greater than or equal to the scalar a.

    round a returns the nearest integer to the scalar a.

    sin a returns the sine of the scalar a.

    cos a returns the cosine of the scalar a.

    tan a returns the tangent of the scalar a.

    sinh a returns the hyperbolic sine of the scalar a.

    cosh a returns the hyperbolic cosine of the scalar a.

    tanh a returns the hyperbolic tangent of the scalar a.

    asin a returns the arcsine of the scalar a.

    acos a returns the arccosine of the scalar a.

    atan a returns the arctangent of the scalar a.

    asinh a returns the inverse hyperbolic sine of the scalar a.

    acosh a returns the inverse hyperbolic cosine of the scalar a.

    atanh a returns the inverse hyperbolic tangent of the scalar a.

    relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

    dawsn a returns Dawson's function of the scalar a.

    sigmoid a returns the sigmoid function of the scalar a.

    diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index d81d0f741..4de8b8ab1 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

    Module A.Linalg

    val inv : arr -> arr
    val logdet : arr -> elt
    val chol : ?upper:bool -> arr -> arr
    val svd : ?thin:bool -> arr -> arr * arr * arr
    val qr : arr -> arr * arr
    val lq : arr -> arr * arr
    val sylvester : arr -> arr -> arr -> arr
    val lyapunov : arr -> arr -> arr
    val discrete_lyapunov : +Linalg (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

    Module A.Linalg

    val inv : arr -> arr
    val logdet : arr -> elt
    val chol : ?upper:bool -> arr -> arr
    val svd : ?thin:bool -> arr -> arr * arr * arr
    val qr : arr -> arr * arr
    val lq : arr -> arr * arr
    val sylvester : arr -> arr -> arr -> arr
    val lyapunov : arr -> arr -> arr
    val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 31fdb1ca3..85519c305 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

    Module A.Mat

    val diagm : ?k:int -> arr -> arr
    val triu : ?k:int -> arr -> arr
    val tril : ?k:int -> arr -> arr
    val eye : int -> arr
    +Mat (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

    Module A.Mat

    val diagm : ?k:int -> arr -> arr
    val triu : ?k:int -> arr -> arr
    val tril : ?k:int -> arr -> arr
    val eye : int -> arr
    diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index 46c52a027..2fa4d271b 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

    Module A.Scalar

    val add : elt -> elt -> elt
    val sub : elt -> elt -> elt
    val mul : elt -> elt -> elt
    val div : elt -> elt -> elt
    val pow : elt -> elt -> elt
    val atan2 : elt -> elt -> elt
    val abs : elt -> elt
    val neg : elt -> elt
    val sqr : elt -> elt
    val sqrt : elt -> elt
    val exp : elt -> elt
    val log : elt -> elt
    val log2 : elt -> elt
    val log10 : elt -> elt
    val signum : elt -> elt
    val floor : elt -> elt
    val ceil : elt -> elt
    val round : elt -> elt
    val sin : elt -> elt
    val cos : elt -> elt
    val tan : elt -> elt
    val sinh : elt -> elt
    val cosh : elt -> elt
    val tanh : elt -> elt
    val asin : elt -> elt
    val acos : elt -> elt
    val atan : elt -> elt
    val asinh : elt -> elt
    val acosh : elt -> elt
    val atanh : elt -> elt
    val relu : elt -> elt
    val dawsn : elt -> elt
    val sigmoid : elt -> elt
    +Scalar (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

    Module A.Scalar

    val add : elt -> elt -> elt
    val sub : elt -> elt -> elt
    val mul : elt -> elt -> elt
    val div : elt -> elt -> elt
    val pow : elt -> elt -> elt
    val atan2 : elt -> elt -> elt
    val abs : elt -> elt
    val neg : elt -> elt
    val sqr : elt -> elt
    val sqrt : elt -> elt
    val exp : elt -> elt
    val log : elt -> elt
    val log2 : elt -> elt
    val log10 : elt -> elt
    val signum : elt -> elt
    val floor : elt -> elt
    val ceil : elt -> elt
    val round : elt -> elt
    val sin : elt -> elt
    val cos : elt -> elt
    val tan : elt -> elt
    val sinh : elt -> elt
    val cosh : elt -> elt
    val tanh : elt -> elt
    val asin : elt -> elt
    val acos : elt -> elt
    val atan : elt -> elt
    val asinh : elt -> elt
    val acosh : elt -> elt
    val atanh : elt -> elt
    val relu : elt -> elt
    val dawsn : elt -> elt
    val sigmoid : elt -> elt
    diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index bd91e4a19..2587f6607 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

    Module Device.A

    include Owl_types_ndarray_algodiff.Sig
    include Owl_types_ndarray_eltcmp.Sig
    include Owl_types_ndarray_basic.Sig
    type arr
    type elt
    val empty : int array -> arr
    val zeros : int array -> arr
    val ones : int array -> arr
    val create : int array -> elt -> arr
    val sequential : ?a:elt -> ?step:elt -> int array -> arr
    val uniform : ?a:elt -> ?b:elt -> int array -> arr
    val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
    val bernoulli : ?p:elt -> int array -> arr
    val init : int array -> (int -> elt) -> arr
    val init_nd : int array -> (int array -> elt) -> arr
    val shape : arr -> int array
    val numel : arr -> int
    val get : arr -> int array -> elt
    val set : arr -> int array -> elt -> unit
    val get_slice : int list list -> arr -> arr
    val set_slice : int list list -> arr -> arr -> unit
    val get_fancy : Owl_types_common.index list -> arr -> arr
    val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
    val copy : arr -> arr
    val copy_ : out:arr -> arr -> unit
    val reset : arr -> unit
    val reshape : arr -> int array -> arr
    val reverse : arr -> arr
    val tile : arr -> int array -> arr
    val repeat : arr -> int array -> arr
    val concatenate : ?axis:int -> arr array -> arr
    val stack : ?axis:int -> arr array -> arr
    val split : ?axis:int -> int array -> arr -> arr array
    val expand : ?hi:bool -> arr -> int -> arr
    val squeeze : ?axis:int array -> arr -> arr
    val draw : ?axis:int -> arr -> int -> arr * int array
    val map : (elt -> elt) -> arr -> arr
    val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
    val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
    val one_hot : int -> arr -> arr
    val pad : ?v:elt -> int list list -> arr -> arr
    val print : +A (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

    Module Device.A

    include Owl_types_ndarray_algodiff.Sig
    include Owl_types_ndarray_eltcmp.Sig
    include Owl_types_ndarray_basic.Sig
    type arr
    type elt
    val empty : int array -> arr
    val zeros : int array -> arr
    val ones : int array -> arr
    val create : int array -> elt -> arr
    val sequential : ?a:elt -> ?step:elt -> int array -> arr
    val uniform : ?a:elt -> ?b:elt -> int array -> arr
    val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
    val bernoulli : ?p:elt -> int array -> arr
    val init : int array -> (int -> elt) -> arr
    val init_nd : int array -> (int array -> elt) -> arr
    val shape : arr -> int array
    val numel : arr -> int
    val get : arr -> int array -> elt
    val set : arr -> int array -> elt -> unit
    val get_slice : int list list -> arr -> arr
    val set_slice : int list list -> arr -> arr -> unit
    val get_fancy : Owl_types_common.index list -> arr -> arr
    val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
    val copy : arr -> arr
    val copy_ : out:arr -> arr -> unit
    val reset : arr -> unit
    val reshape : arr -> int array -> arr
    val reverse : arr -> arr
    val tile : arr -> int array -> arr
    val repeat : arr -> int array -> arr
    val concatenate : ?axis:int -> arr array -> arr
    val stack : ?axis:int -> arr array -> arr
    val split : ?axis:int -> int array -> arr -> arr array
    val expand : ?hi:bool -> arr -> int -> arr
    val squeeze : ?axis:int array -> arr -> arr
    val draw : ?axis:int -> arr -> int -> arr * int array
    val map : (elt -> elt) -> arr -> arr
    val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
    val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
    val one_hot : int -> arr -> arr
    val pad : ?v:elt -> int list list -> arr -> arr
    val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index 62246b14d..993055c56 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

    Module Type.Device

    Type definition
    type device

    TODO

    type value

    TODO

    Core functions
    val make_device : unit -> device

    TODO

    val arr_to_value : A.arr -> value

    TODO

    val value_to_arr : value -> A.arr

    TODO

    val elt_to_value : A.elt -> value

    TODO

    val value_to_elt : value -> A.elt

    TODO

    val value_to_float : value -> float

    TODO

    val is_arr : value -> bool

    TODO

    val is_elt : value -> bool

    TODO

    +Device (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

    Module Type.Device

    Type definition
    type device

    TODO

    type value

    TODO

    Core functions
    val make_device : unit -> device

    TODO

    val arr_to_value : A.arr -> value

    TODO

    val value_to_arr : value -> A.arr

    TODO

    val elt_to_value : A.elt -> value

    TODO

    val value_to_elt : value -> A.elt

    TODO

    val value_to_float : value -> float

    TODO

    val is_arr : value -> bool

    TODO

    val is_elt : value -> bool

    TODO

    diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index d16c8e0e7..fa7d05784 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type)

    Module Shape.Type

    Type definition
    type state =
    1. | Valid
    2. | Invalid
      (*

      TODO

      *)

    TODO

    and block = {
    1. size : int;
    2. block_id : int;
    3. mutable active : t option;
    4. mutable memory : Device.value;
    5. mutable nodes : t list;
    }

    block type keeps a reference to a block of memory and to the nodes sharing that block.

    and attr = {
    1. mutable op : op;
    2. mutable freeze : bool;
    3. mutable reuse : bool;
    4. mutable state : state;
    5. mutable shape : int array option array;
    6. mutable value : Device.value array;
    7. mutable block : block array option;
    }

    TODO

    and arr =
    1. | Arr of t
    and elt =
    1. | Elt of t
    and op =
    1. | Noop
    2. | Var
    3. | Const
    4. | Empty of int array
    5. | Zeros of int array
    6. | Ones of int array
    7. | Create of int array
    8. | Sequential of int array
    9. | Uniform of int array
    10. | Gaussian of int array
    11. | Bernoulli of int array
    12. | Init of int array * int -> elt
    13. | Get of int array
    14. | Set of int array
    15. | GetSlice of int list list
    16. | SetSlice of int list list
    17. | GetFancy of Owl_types_common.index list
    18. | SetFancy of Owl_types_common.index list
    19. | Copy
    20. | Reset
    21. | Reshape of int array
    22. | Reverse
    23. | Tile of int array
    24. | Repeat of int array
    25. | Pad of elt * int list list
    26. | Concatenate of int
    27. | Stack of int
    28. | Split of int * int array
    29. | Draw of int * int
    30. | Map of elt -> elt
    31. | Fold of int * elt -> elt -> elt
    32. | Scan of int * elt -> elt -> elt
    33. | OneHot of int
    34. | OfArray of int array
    35. | Delay of Device.A.arr -> Device.A.arr
    36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
    37. | LazyPrint of int option +Type (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape.Type)

      Module Shape.Type

      Type definition
      type state =
      1. | Valid
      2. | Invalid
        (*

        TODO

        *)

      TODO

      and block = {
      1. size : int;
      2. block_id : int;
      3. mutable active : t option;
      4. mutable memory : Device.value;
      5. mutable nodes : t list;
      }

      block type keeps a reference to a block of memory and to the nodes sharing that block.

      and attr = {
      1. mutable op : op;
      2. mutable freeze : bool;
      3. mutable reuse : bool;
      4. mutable state : state;
      5. mutable shape : int array option array;
      6. mutable value : Device.value array;
      7. mutable block : block array option;
      }

      TODO

      and arr =
      1. | Arr of t
      and elt =
      1. | Elt of t
      and op =
      1. | Noop
      2. | Var
      3. | Const
      4. | Empty of int array
      5. | Zeros of int array
      6. | Ones of int array
      7. | Create of int array
      8. | Sequential of int array
      9. | Uniform of int array
      10. | Gaussian of int array
      11. | Bernoulli of int array
      12. | Init of int array * int -> elt
      13. | Get of int array
      14. | Set of int array
      15. | GetSlice of int list list
      16. | SetSlice of int list list
      17. | GetFancy of Owl_types_common.index list
      18. | SetFancy of Owl_types_common.index list
      19. | Copy
      20. | Reset
      21. | Reshape of int array
      22. | Reverse
      23. | Tile of int array
      24. | Repeat of int array
      25. | Pad of elt * int list list
      26. | Concatenate of int
      27. | Stack of int
      28. | Split of int * int array
      29. | Draw of int * int
      30. | Map of elt -> elt
      31. | Fold of int * elt -> elt -> elt
      32. | Scan of int * elt -> elt -> elt
      33. | OneHot of int
      34. | OfArray of int array
      35. | Delay of Device.A.arr -> Device.A.arr
      36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
      37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
      38. | Abs
      39. | Neg
      40. | Floor
      41. | Ceil
      42. | Round
      43. | Sqr
      44. | Sqrt
      45. | Log
      46. | Log2
      47. | Log10
      48. | Exp
      49. | Sin
      50. | Cos
      51. | Tan
      52. | Sinh
      53. | Cosh
      54. | Tanh
      55. | Asin
      56. | Acos
      57. | Atan
      58. | Asinh
      59. | Acosh
      60. | Atanh
      61. | Min of bool * int
      62. | Max of bool * int
      63. | Sum of bool * int
      64. | SumReduce of int array
      65. | Signum
      66. | Sigmoid
      67. | Relu
      68. | Dawsn
      69. | Min'
      70. | Max'
      71. | Sum'
      72. | LogSumExp'
      73. | LogSumExp of bool * int
      74. | L1norm'
      75. | L2norm'
      76. | L2NormSqr'
      77. | ClipByValue
      78. | ClipByL2norm
      79. | Pow
      80. | ScalarPow
      81. | PowScalar
      82. | Atan2
      83. | ScalarAtan2
      84. | Atan2Scalar
      85. | Hypot
      86. | Min2
      87. | Max2
      88. | Add
      89. | Sub
      90. | Mul
      91. | Div
      92. | AddScalar
      93. | SubScalar
      94. | MulScalar
      95. | DivScalar
      96. | ScalarAdd
      97. | ScalarSub
      98. | ScalarMul
      99. | ScalarDiv
      100. | FMA
      101. | EltEqual
      102. | EltNotEqual
      103. | EltLess
      104. | EltGreater
      105. | EltLessEqual
      106. | EltGreaterEqual
      107. | EltEqualScalar
      108. | EltNotEqualScalar
      109. | EltLessScalar
      110. | EltGreaterScalar
      111. | EltLessEqualScalar
      112. | EltGreaterEqualScalar
      113. | Conv1d of Owl_types_common.padding * int array
      114. | Conv2d of Owl_types_common.padding * int array
      115. | Conv3d of Owl_types_common.padding * int array
      116. | TransposeConv1d of Owl_types_common.padding * int array
      117. | TransposeConv2d of Owl_types_common.padding * int array
      118. | TransposeConv3d of Owl_types_common.padding * int array
      119. | DilatedConv1d of Owl_types_common.padding * int array * int array
      120. | DilatedConv2d of Owl_types_common.padding * int array * int array
      121. | DilatedConv3d of Owl_types_common.padding * int array * int array
      122. | MaxPool1d of Owl_types_common.padding * int array * int array
      123. | MaxPool2d of Owl_types_common.padding * int array * int array
      124. | MaxPool3d of Owl_types_common.padding * int array * int array
      125. | AvgPool1d of Owl_types_common.padding * int array * int array
      126. | AvgPool2d of Owl_types_common.padding * int array * int array
      127. | AvgPool3d of Owl_types_common.padding * int array * int array
      128. | UpSampling2d of int array
      129. | Conv1dBackwardInput of int array
      130. | Conv1dBackwardKernel of int array
      131. | Conv2dBackwardInput of int array
      132. | Conv2dBackwardKernel of int array
      133. | Conv3dBackwardInput of int array
      134. | Conv3dBackwardKernel of int array
      135. | TransposeConv1dBackwardInput of int array
      136. | TransposeConv1dBackwardKernel of int array
      137. | TransposeConv2dBackwardInput of int array
      138. | TransposeConv2dBackwardKernel of int array
      139. | TransposeConv3dBackwardInput of int array
      140. | TransposeConv3dBackwardKernel of int array
      141. | DilatedConv1dBackwardInput of int array * int array
      142. | DilatedConv1dBackwardKernel of int array * int array
      143. | DilatedConv2dBackwardInput of int array * int array
      144. | DilatedConv2dBackwardKernel of int array * int array
      145. | DilatedConv3dBackwardInput of int array * int array
      146. | DilatedConv3dBackwardKernel of int array * int array
      147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
      148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
      149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
      150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
      151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
      152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
      153. | UpSampling2dBackward of int array
      154. | RowNum
      155. | ColNum
      156. | Row
      157. | Rows of int array
      158. | CopyRowTo
      159. | CopyColTo
      160. | Dot of bool * bool * elt * elt
      161. | Inv
      162. | Trace
      163. | Transpose of int array
      164. | ToRows
      165. | OfRows
      166. | Scalar_Add
      167. | Scalar_Sub
      168. | Scalar_Mul
      169. | Scalar_Div
      170. | Scalar_Pow
      171. | Scalar_Atan2
      172. | Scalar_Abs
      173. | Scalar_Neg
      174. | Scalar_Sqr
      175. | Scalar_Sqrt
      176. | Scalar_Exp
      177. | Scalar_Log
      178. | Scalar_Log2
      179. | Scalar_Log10
      180. | Scalar_Signum
      181. | Scalar_Floor
      182. | Scalar_Ceil
      183. | Scalar_Round
      184. | Scalar_Sin
      185. | Scalar_Cos
      186. | Scalar_Tan
      187. | Scalar_Sinh
      188. | Scalar_Cosh
      189. | Scalar_Tanh
      190. | Scalar_Asin
      191. | Scalar_Acos
      192. | Scalar_Atan
      193. | Scalar_Asinh
      194. | Scalar_Acosh
      195. | Scalar_Atanh
      196. | Scalar_Relu
      197. | Scalar_Dawsn
      198. | Scalar_Sigmoid
      199. | Fused_Adagrad of float * float
        (*

        TODO

        *)
      diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html index d32c59916..a655b7465 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape)

      Module Symbol.Shape

      Core functions
      val infer_shape : +Shape (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol.Shape)

      Module Symbol.Shape

      Core functions
      val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

      TODO

      diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/index.html index c35130f93..0436531cd 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol)

      Module Operator.Symbol

      Core functions
      val op_to_str : Shape.Type.op -> string

      TODO

      val is_random_variable : Shape.Type.op -> bool

      TODO

      val refnum : 'a Owl_graph.node -> int

      TODO

      val node_shape : Shape.Type.attr Owl_graph.node -> int array

      TODO

      val node_numel : Shape.Type.attr Owl_graph.node -> int

      TODO

      val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

      TODO

      val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

      TODO

      val shape_to_str : int array option array -> string

      TODO

      val node_to_str : Shape.Type.attr Owl_graph.node -> string

      TODO

      val node_to_arr : Shape.Type.t -> Shape.Type.arr

      TODO

      val arr_to_node : Shape.Type.arr -> Shape.Type.t

      TODO

      val node_to_elt : Shape.Type.t -> Shape.Type.elt

      TODO

      val elt_to_node : Shape.Type.elt -> Shape.Type.t

      TODO

      val make_node : +Symbol (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator.Symbol)

      Module Operator.Symbol

      Core functions
      val op_to_str : Shape.Type.op -> string

      TODO

      val is_random_variable : Shape.Type.op -> bool

      TODO

      val refnum : 'a Owl_graph.node -> int

      TODO

      val node_shape : Shape.Type.attr Owl_graph.node -> int array

      TODO

      val node_numel : Shape.Type.attr Owl_graph.node -> int

      TODO

      val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

      TODO

      val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

      TODO

      val shape_to_str : int array option array -> string

      TODO

      val node_to_str : Shape.Type.attr Owl_graph.node -> string

      TODO

      val node_to_arr : Shape.Type.t -> Shape.Type.arr

      TODO

      val arr_to_node : Shape.Type.arr -> Shape.Type.t

      TODO

      val node_to_elt : Shape.Type.t -> Shape.Type.elt

      TODO

      val elt_to_node : Shape.Type.elt -> Shape.Type.t

      TODO

      val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/index.html index 6feffccd6..5984ca753 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator)

      Module Optimiser.Operator

      Vectorised functions
      val empty : int array -> Symbol.Shape.Type.arr

      TODO

      val zeros : int array -> Symbol.Shape.Type.arr

      TODO

      val ones : int array -> Symbol.Shape.Type.arr

      TODO

      val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

      TODO

      val sequential : +Operator (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser.Operator)

      Module Optimiser.Operator

      Vectorised functions

      noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

      val empty : int array -> Symbol.Shape.Type.arr

      empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

      val zeros : int array -> Symbol.Shape.Type.arr

      zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

      val ones : int array -> Symbol.Shape.Type.arr

      ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

      val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

      create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

      val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

      TODO

      val uniform : + Symbol.Shape.Type.arr

      sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

      val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

      TODO

      val gaussian : + Symbol.Shape.Type.arr

      uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

      val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

      TODO

      val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

      TODO

      val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

      TODO

      val init_nd : + Symbol.Shape.Type.arr

      gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

      val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

      bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

      val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

      init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

      val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

      TODO

      val shape : Symbol.Shape.Type.arr -> int array

      TODO

      val numel : Symbol.Shape.Type.arr -> int

      TODO

      TODO

      val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

      TODO

      val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

      TODO

      val set_slice : + Symbol.Shape.Type.arr

      init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

      val shape : Symbol.Shape.Type.arr -> int array

      shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

      val numel : Symbol.Shape.Type.arr -> int

      numel arr returns the total number of elements in the array arr.

      get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

      val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

      set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

      val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

      get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

      val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

      TODO

      val get_fancy : + unit

      set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

      val set_fancy : + Symbol.Shape.Type.arr

      get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

      val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

      TODO

      val copy_ : out:'a -> 'b -> 'c

      TODO

      val reset : Symbol.Shape.Type.arr -> unit

      TODO

      val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      TODO

      val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      TODO

      val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      TODO

      val pad : + unit

      set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

      copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

      val copy_ : out:'a -> 'b -> 'c

      copy_ ~out src copies the contents of the array src into the pre-allocated array out.

      val reset : Symbol.Shape.Type.arr -> unit

      reset arr sets all elements of the array arr to zero.

      val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

      reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

      val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

      val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

      TODO

      val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

      TODO

      val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

      TODO

      val concatenate : + Symbol.Shape.Type.arr

      pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

      val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

      expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

      val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

      squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

      val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

      TODO

      val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

      TODO

      val concat : + Symbol.Shape.Type.arr

      concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

      val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

      stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

      val split : ?axis:int -> 'a -> 'b -> 'c

      TODO

      concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

      val split : ?axis:int -> 'a -> 'b -> 'c

      split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

      • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
      val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

      TODO

      val map : + Symbol.Shape.Type.arr * 'a array

      draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

      map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

      fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

      TODO

      val delay : + Symbol.Shape.Type.arr

      scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

      one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

      delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

      val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

      val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

      TODO

      lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

      val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

      print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

      • max_row is an optional parameter specifying the maximum number of rows to print.
      • max_col is an optional parameter specifying the maximum number of columns to print.
      • header is an optional parameter to include a header in the output.
      • fmt is an optional parameter to specify the format of the output.

      abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

      neg arr negates each element in the array arr. Returns a new array with each element negated.

      floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

      ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

      round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

      sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

      sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

      log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

      log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

      log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

      exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

      sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

      cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

      tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

      sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

      cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

      tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

      asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

      acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

      atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

      asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

      acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

      atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

      val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

      • axis specifies the axis along which to compute the minimum.
      • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
      val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

      • axis specifies the axis along which to compute the maximum.
      • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
      val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val sum_reduce : + Symbol.Shape.Type.arr

      sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

      • axis specifies the axis along which to compute the sum.
      • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
      val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val log_sum_exp : + Symbol.Shape.Type.arr

      sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

      • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

      signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

      sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

      relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

      dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

      min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

      max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

      sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

      log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

      val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val clip_by_value : + Symbol.Shape.Type.arr

      log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

      • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
      • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

      l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

      l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

      l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

      val clip_by_l2norm : + Symbol.Shape.Type.arr

      clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

      • amin specifies the minimum value to clip to.
      • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

      clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

      val scalar_pow : + Symbol.Shape.Type.arr

      pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

      val pow_scalar : + Symbol.Shape.Type.arr

      scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

      val atan2 : + Symbol.Shape.Type.arr

      pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

      val scalar_atan2 : + Symbol.Shape.Type.arr

      atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

      val atan2_scalar : + Symbol.Shape.Type.arr

      scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

      val hypot : + Symbol.Shape.Type.arr

      atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

      hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

      min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

      max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

      add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

      sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

      mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

      val add_scalar : + Symbol.Shape.Type.arr

      div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

      val sub_scalar : + Symbol.Shape.Type.arr

      add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

      val mul_scalar : + Symbol.Shape.Type.arr

      sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

      val div_scalar : + Symbol.Shape.Type.arr

      mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

      val scalar_add : + Symbol.Shape.Type.arr

      div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

      val scalar_sub : + Symbol.Shape.Type.arr

      scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

      val scalar_mul : + Symbol.Shape.Type.arr

      scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

      val scalar_div : + Symbol.Shape.Type.arr

      scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

      scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

      val elt_equal : + Symbol.Shape.Type.arr

      fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

      val elt_not_equal : + Symbol.Shape.Type.arr

      elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

      val elt_less : + Symbol.Shape.Type.arr

      elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

      val elt_greater : + Symbol.Shape.Type.arr

      elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

      val elt_less_equal : + Symbol.Shape.Type.arr

      elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

      val elt_greater_equal : + Symbol.Shape.Type.arr

      elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

      val elt_equal_scalar : + Symbol.Shape.Type.arr

      elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

      val elt_not_equal_scalar : + Symbol.Shape.Type.arr

      elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

      val elt_less_scalar : + Symbol.Shape.Type.arr

      elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

      val elt_greater_scalar : + Symbol.Shape.Type.arr

      elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

      val elt_less_equal_scalar : + Symbol.Shape.Type.arr

      elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

      TODO

      val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

      elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

      TODO

      val conv1d : + Symbol.Shape.Type.arr

      elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

      val conv2d : + Symbol.Shape.Type.arr

      conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

      • padding specifies the padding strategy (default is "valid").
      • strides specifies the stride length. Returns a new array with the result of the convolution.
      val conv3d : + Symbol.Shape.Type.arr

      conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

      • padding specifies the padding strategy (default is "valid").
      • strides specifies the stride length. Returns a new array with the result of the convolution.
      val transpose_conv1d : + Symbol.Shape.Type.arr

      conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

      • padding specifies the padding strategy (default is "valid").
      • strides specifies the stride length. Returns a new array with the result of the convolution.
      val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

      TODO

      val transpose_conv2d : + Symbol.Shape.Type.arr

      transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

      • padding specifies the padding strategy (default is "valid").
      • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
      val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

      TODO

      val transpose_conv3d : + Symbol.Shape.Type.arr

      transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

      • padding specifies the padding strategy (default is "valid").
      • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
      val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

      TODO

      val dilated_conv1d : + Symbol.Shape.Type.arr

      transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

      • padding specifies the padding strategy (default is "valid").
      • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
      val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val dilated_conv2d : + Symbol.Shape.Type.arr

      dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

      • padding specifies the padding strategy (default is "valid").
      • strides specifies the stride length.
      • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
      val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val dilated_conv3d : + Symbol.Shape.Type.arr

      dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

      • padding specifies the padding strategy (default is "valid").
      • strides specifies the stride length.
      • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
      val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val max_pool1d : + Symbol.Shape.Type.arr

      dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

      • padding specifies the padding strategy (default is "valid").
      • strides specifies the stride length.
      • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
      val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val max_pool2d : + Symbol.Shape.Type.arr

      max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

      • padding specifies the padding strategy (default is "valid").
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length. Returns a new array with the result of the max pooling.
      val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val max_pool3d : + Symbol.Shape.Type.arr

      max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

      • padding specifies the padding strategy (default is "valid").
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length. Returns a new array with the result of the max pooling.
      val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val avg_pool1d : + Symbol.Shape.Type.arr

      max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

      • padding specifies the padding strategy (default is "valid").
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length. Returns a new array with the result of the max pooling.
      val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val avg_pool2d : + Symbol.Shape.Type.arr

      avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

      • padding specifies the padding strategy (default is "valid").
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length. Returns a new array with the result of the average pooling.
      val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val avg_pool3d : + Symbol.Shape.Type.arr

      avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

      • padding specifies the padding strategy (default is "valid").
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length. Returns a new array with the result of the average pooling.
      val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      TODO

      val conv1d_backward_input : + Symbol.Shape.Type.arr

      avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

      • padding specifies the padding strategy (default is "valid").
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length. Returns a new array with the result of the average pooling.
      val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      upsampling2d input size performs a 2-dimensional upsampling on the input array.

      • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

      TODO

      val conv1d_backward_kernel : + Symbol.Shape.Type.arr

      conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

      • input is the original input array.
      • kernel is the convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
      val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val conv2d_backward_input : + Symbol.Shape.Type.arr

      conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

      • input is the original input array.
      • kernel is the convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

      TODO

      val conv2d_backward_kernel : + Symbol.Shape.Type.arr

      conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

      • input is the original input array.
      • kernel is the convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
      val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val conv3d_backward_input : + Symbol.Shape.Type.arr

      conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

      • input is the original input array.
      • kernel is the convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

      TODO

      val conv3d_backward_kernel : + Symbol.Shape.Type.arr

      conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

      • input is the original input array.
      • kernel is the convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
      val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

      conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

      • input is the original input array.
      • kernel is the convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
      val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

      transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

      • input is the original input array.
      • kernel is the transposed convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
      val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

      transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

      • input is the original input array.
      • kernel is the transposed convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
      val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

      transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

      • input is the original input array.
      • kernel is the transposed convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
      val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

      transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

      • input is the original input array.
      • kernel is the transposed convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
      val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

      transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

      • input is the original input array.
      • kernel is the transposed convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
      val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

      transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

      • input is the original input array.
      • kernel is the transposed convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
      val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

      dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

      • input is the original input array.
      • kernel is the dilated convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • dilations specifies the dilation rate.
      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
      val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

      dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

      • input is the original input array.
      • kernel is the dilated convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • dilations specifies the dilation rate.
      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
      val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

      dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

      • input is the original input array.
      • kernel is the dilated convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • dilations specifies the dilation rate.
      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
      val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

      dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

      • input is the original input array.
      • kernel is the dilated convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • dilations specifies the dilation rate.
      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
      val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

      dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

      • input is the original input array.
      • kernel is the dilated convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • dilations specifies the dilation rate.
      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
      val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val max_pool1d_backward : + Symbol.Shape.Type.arr

      dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

      • input is the original input array.
      • kernel is the dilated convolutional kernel used during the forward pass.
      • strides specifies the stride length.
      • dilations specifies the dilation rate.
      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
      val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val max_pool2d_backward : + Symbol.Shape.Type.arr

      max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

      • padding specifies the padding strategy used during the forward pass.
      • input is the original input array.
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
      val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val max_pool3d_backward : + Symbol.Shape.Type.arr

      max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

      • padding specifies the padding strategy used during the forward pass.
      • input is the original input array.
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
      val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val avg_pool1d_backward : + Symbol.Shape.Type.arr

      max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

      • padding specifies the padding strategy used during the forward pass.
      • input is the original input array.
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
      val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val avg_pool2d_backward : + Symbol.Shape.Type.arr

      avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

      • padding specifies the padding strategy used during the forward pass.
      • input is the original input array.
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
      val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val avg_pool3d_backward : + Symbol.Shape.Type.arr

      avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

      • padding specifies the padding strategy used during the forward pass.
      • input is the original input array.
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
      val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val upsampling2d_backward : + Symbol.Shape.Type.arr

      avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

      • padding specifies the padding strategy used during the forward pass.
      • input is the original input array.
      • pool_size specifies the size of the pooling window.
      • strides specifies the stride length.
      • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
      val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val row_num : Symbol.Shape.Type.arr -> int

      TODO

      val col_num : Symbol.Shape.Type.arr -> int

      TODO

      val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      TODO

      val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

      TODO

      val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

      TODO

      TODO

      upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

      • input is the original input array.
      • size specifies the upsampling factors for each dimension.
      • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
      val row_num : Symbol.Shape.Type.arr -> int

      row_num arr returns the number of rows in the array arr.

      val col_num : Symbol.Shape.Type.arr -> int

      col_num arr returns the number of columns in the array arr.

      row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

      val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

      rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

      val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

      copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

      val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

      copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

      diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

      trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

      val transpose : + Symbol.Shape.Type.arr

      dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

      val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val to_rows : Symbol.Shape.Type.arr -> 'a array

      TODO

      TODO

      val to_cols : Symbol.Shape.Type.arr -> 'a array

      TODO

      TODO

      val of_array : + Symbol.Shape.Type.arr

      transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

      val to_rows : Symbol.Shape.Type.arr -> 'a array

      to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

      of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

      val to_cols : Symbol.Shape.Type.arr -> 'a array

      to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

      of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

      val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

      TODO

      val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

      TODO

      val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

      TODO

      Scalar functions
      module Scalar : sig ... end
      module Mat : sig ... end
      module Linalg : sig ... end
      + Symbol.Shape.Type.arr

      of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

      val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

      of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

      val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

      to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

      Scalar functions
      module Scalar : sig ... end
      module Mat : sig ... end
      module Linalg : sig ... end
      diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/index.html index 457828ea4..48f6911b6 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser)

      Module Graph.Optimiser

      Core functions
      val estimate_complexity : 'a Owl_graph.node array -> int * int

      TODO

      val optimise_nodes : +Optimiser (owl-base.Owl_computation_cpu_eval.Make.Graph.Optimiser)

      Module Graph.Optimiser

      Core functions
      val estimate_complexity : 'a Owl_graph.node array -> int * int

      TODO

      val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

      TODO

      diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/index.html index abd3ae656..16220fff3 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/argument-1-Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_computation_cpu_eval.Make.Graph)

      Parameter Make.Graph

      Type definition
      type graph

      TODO

      Core functions
      val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

      TODO

      val graph_to_dot : graph -> string

      TODO

      val graph_to_trace : graph -> string

      TODO

      val save_graph : 'a -> string -> unit

      TODO

      val load_graph : string -> 'a * 'b

      TODO

      val collect_rvs : +Graph (owl-base.Owl_computation_cpu_eval.Make.Graph)

      Parameter Make.Graph

      Type definition
      type graph

      TODO

      Core functions
      val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

      TODO

      val graph_to_dot : graph -> string

      TODO

      val graph_to_trace : graph -> string

      TODO

      val save_graph : 'a -> string -> unit

      TODO

      val load_graph : string -> 'a * 'b

      TODO

      val invalidate_rvs : graph -> unit

      TODO

      val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_computation_cpu_eval/Make/index.html b/docs/owl-base/Owl_computation_cpu_eval/Make/index.html index 0ae2e9b42..e28259a27 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/Make/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_computation_cpu_eval.Make)

      Module Owl_computation_cpu_eval.Make

      Parameters

      Signature

      val invalidate_opt : +Make (owl-base.Owl_computation_cpu_eval.Make)

      Module Owl_computation_cpu_eval.Make

      Parameters

      Signature

      val update_validity : Graph.Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node -> diff --git a/docs/owl-base/Owl_computation_cpu_eval/index.html b/docs/owl-base/Owl_computation_cpu_eval/index.html index 90e8918a1..7ef5191d4 100644 --- a/docs/owl-base/Owl_computation_cpu_eval/index.html +++ b/docs/owl-base/Owl_computation_cpu_eval/index.html @@ -1,2 +1,2 @@ -Owl_computation_cpu_eval (owl-base.Owl_computation_cpu_eval)

      Module Owl_computation_cpu_eval

      module Make (Graph : Owl_computation_graph_sig.Sig) : sig ... end
      +Owl_computation_cpu_eval (owl-base.Owl_computation_cpu_eval)

      Module Owl_computation_cpu_eval

      module Make (Graph : Owl_computation_graph_sig.Sig) : sig ... end
      diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/MultiMap/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/MultiMap/index.html index d9e71b52c..e96ee9e97 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/MultiMap/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/MultiMap/index.html @@ -1,2 +1,2 @@ -MultiMap (owl-base.Owl_computation_cpu_init.Make.MultiMap)

      Module Make.MultiMap

      type key = int
      type 'a t
      val empty : 'a t
      val is_empty : 'a t -> bool
      val mem : key -> 'a t -> bool
      val add : key -> 'a -> 'a t -> 'a t
      val remove : key -> 'a t -> 'a t
      val find : key -> 'a t -> 'a
      val max_binding : 'a t -> key * 'a
      val find_first_opt : (key -> bool) -> 'a t -> (key * 'a) option
      +MultiMap (owl-base.Owl_computation_cpu_init.Make.MultiMap)

      Module Make.MultiMap

      type key = int
      type 'a t
      val empty : 'a t
      val is_empty : 'a t -> bool
      val mem : key -> 'a t -> bool
      val add : key -> 'a -> 'a t -> 'a t
      val remove : key -> 'a t -> 'a t
      val find : key -> 'a t -> 'a
      val max_binding : 'a t -> key * 'a
      val find_first_opt : (key -> bool) -> 'a t -> (key * 'a) option
      diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Linalg/index.html index 403ffb40a..74a1ec1b0 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Linalg)

      Module Operator.Linalg

      val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

      TODO

      val svd : +Linalg (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Linalg)

      Module Operator.Linalg

      inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

      logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

      val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

      chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

      • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

      qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

      lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

      svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

      • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
      val lyapunov : + Symbol.Shape.Type.arr

      sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

      val discrete_lyapunov : + Symbol.Shape.Type.arr

      lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

      val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      val linsolve : + Symbol.Shape.Type.arr

      discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

      • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
      val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

      TODO

      linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

      • trans specifies whether to transpose the matrix A.
      • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

      care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

      • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
      + Symbol.Shape.Type.arr

      dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

      • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
      diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Mat/index.html index f3a4aa758..8f9d3e1ea 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Mat)

      Module Operator.Mat

      val eye : int -> Symbol.Shape.Type.arr

      TODO

      TODO

      TODO

      TODO

      +Mat (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Mat)

      Module Operator.Mat

      val eye : int -> Symbol.Shape.Type.arr

      eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

      diagm ?k v creates a diagonal matrix from the array v.

      • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

      triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

      tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

      diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Scalar/index.html index 678d4fa73..f22edca29 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Scalar)

      Module Operator.Scalar

      val add : +Scalar (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Scalar)

      Module Operator.Scalar

      add a b returns the sum of the scalars a and b.

      sub a b returns the difference of the scalars a and b.

      mul a b returns the product of the scalars a and b.

      div a b returns the quotient of the scalars a and b.

      val atan2 : + Symbol.Shape.Type.elt

      pow a b returns the scalar a raised to the power of b.

      + Symbol.Shape.Type.elt

      atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

      abs a returns the absolute value of the scalar a.

      neg a returns the negation of the scalar a.

      sqr a returns the square of the scalar a.

      sqrt a returns the square root of the scalar a.

      exp a returns the exponential of the scalar a.

      log a returns the natural logarithm of the scalar a.

      log2 a returns the base-2 logarithm of the scalar a.

      log10 a returns the base-10 logarithm of the scalar a.

      signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

      floor a returns the greatest integer less than or equal to the scalar a.

      ceil a returns the smallest integer greater than or equal to the scalar a.

      round a returns the nearest integer to the scalar a.

      sin a returns the sine of the scalar a.

      cos a returns the cosine of the scalar a.

      tan a returns the tangent of the scalar a.

      sinh a returns the hyperbolic sine of the scalar a.

      cosh a returns the hyperbolic cosine of the scalar a.

      tanh a returns the hyperbolic tangent of the scalar a.

      asin a returns the arcsine of the scalar a.

      acos a returns the arccosine of the scalar a.

      atan a returns the arctangent of the scalar a.

      asinh a returns the inverse hyperbolic sine of the scalar a.

      acosh a returns the inverse hyperbolic cosine of the scalar a.

      atanh a returns the inverse hyperbolic tangent of the scalar a.

      relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

      dawsn a returns Dawson's function of the scalar a.

      sigmoid a returns the sigmoid function of the scalar a.

      diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 8d4569a53..8975332c3 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

      Module A.Linalg

      val inv : arr -> arr
      val logdet : arr -> elt
      val chol : ?upper:bool -> arr -> arr
      val svd : ?thin:bool -> arr -> arr * arr * arr
      val qr : arr -> arr * arr
      val lq : arr -> arr * arr
      val sylvester : arr -> arr -> arr -> arr
      val lyapunov : arr -> arr -> arr
      val discrete_lyapunov : +Linalg (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

      Module A.Linalg

      val inv : arr -> arr
      val logdet : arr -> elt
      val chol : ?upper:bool -> arr -> arr
      val svd : ?thin:bool -> arr -> arr * arr * arr
      val qr : arr -> arr * arr
      val lq : arr -> arr * arr
      val sylvester : arr -> arr -> arr -> arr
      val lyapunov : arr -> arr -> arr
      val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 5418b38c8..dc6630c2b 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

      Module A.Mat

      val diagm : ?k:int -> arr -> arr
      val triu : ?k:int -> arr -> arr
      val tril : ?k:int -> arr -> arr
      val eye : int -> arr
      +Mat (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

      Module A.Mat

      val diagm : ?k:int -> arr -> arr
      val triu : ?k:int -> arr -> arr
      val tril : ?k:int -> arr -> arr
      val eye : int -> arr
      diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index c213332c9..da35967de 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

      Module A.Scalar

      val add : elt -> elt -> elt
      val sub : elt -> elt -> elt
      val mul : elt -> elt -> elt
      val div : elt -> elt -> elt
      val pow : elt -> elt -> elt
      val atan2 : elt -> elt -> elt
      val abs : elt -> elt
      val neg : elt -> elt
      val sqr : elt -> elt
      val sqrt : elt -> elt
      val exp : elt -> elt
      val log : elt -> elt
      val log2 : elt -> elt
      val log10 : elt -> elt
      val signum : elt -> elt
      val floor : elt -> elt
      val ceil : elt -> elt
      val round : elt -> elt
      val sin : elt -> elt
      val cos : elt -> elt
      val tan : elt -> elt
      val sinh : elt -> elt
      val cosh : elt -> elt
      val tanh : elt -> elt
      val asin : elt -> elt
      val acos : elt -> elt
      val atan : elt -> elt
      val asinh : elt -> elt
      val acosh : elt -> elt
      val atanh : elt -> elt
      val relu : elt -> elt
      val dawsn : elt -> elt
      val sigmoid : elt -> elt
      +Scalar (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

      Module A.Scalar

      val add : elt -> elt -> elt
      val sub : elt -> elt -> elt
      val mul : elt -> elt -> elt
      val div : elt -> elt -> elt
      val pow : elt -> elt -> elt
      val atan2 : elt -> elt -> elt
      val abs : elt -> elt
      val neg : elt -> elt
      val sqr : elt -> elt
      val sqrt : elt -> elt
      val exp : elt -> elt
      val log : elt -> elt
      val log2 : elt -> elt
      val log10 : elt -> elt
      val signum : elt -> elt
      val floor : elt -> elt
      val ceil : elt -> elt
      val round : elt -> elt
      val sin : elt -> elt
      val cos : elt -> elt
      val tan : elt -> elt
      val sinh : elt -> elt
      val cosh : elt -> elt
      val tanh : elt -> elt
      val asin : elt -> elt
      val acos : elt -> elt
      val atan : elt -> elt
      val asinh : elt -> elt
      val acosh : elt -> elt
      val atanh : elt -> elt
      val relu : elt -> elt
      val dawsn : elt -> elt
      val sigmoid : elt -> elt
      diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index 0400c9e6a..f53b4031e 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

      Module Device.A

      include Owl_types_ndarray_algodiff.Sig
      include Owl_types_ndarray_eltcmp.Sig
      include Owl_types_ndarray_basic.Sig
      type arr
      type elt
      val empty : int array -> arr
      val zeros : int array -> arr
      val ones : int array -> arr
      val create : int array -> elt -> arr
      val sequential : ?a:elt -> ?step:elt -> int array -> arr
      val uniform : ?a:elt -> ?b:elt -> int array -> arr
      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
      val bernoulli : ?p:elt -> int array -> arr
      val init : int array -> (int -> elt) -> arr
      val init_nd : int array -> (int array -> elt) -> arr
      val shape : arr -> int array
      val numel : arr -> int
      val get : arr -> int array -> elt
      val set : arr -> int array -> elt -> unit
      val get_slice : int list list -> arr -> arr
      val set_slice : int list list -> arr -> arr -> unit
      val get_fancy : Owl_types_common.index list -> arr -> arr
      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
      val copy : arr -> arr
      val copy_ : out:arr -> arr -> unit
      val reset : arr -> unit
      val reshape : arr -> int array -> arr
      val reverse : arr -> arr
      val tile : arr -> int array -> arr
      val repeat : arr -> int array -> arr
      val concatenate : ?axis:int -> arr array -> arr
      val stack : ?axis:int -> arr array -> arr
      val split : ?axis:int -> int array -> arr -> arr array
      val expand : ?hi:bool -> arr -> int -> arr
      val squeeze : ?axis:int array -> arr -> arr
      val draw : ?axis:int -> arr -> int -> arr * int array
      val map : (elt -> elt) -> arr -> arr
      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
      val one_hot : int -> arr -> arr
      val pad : ?v:elt -> int list list -> arr -> arr
      val print : +A (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

      Module Device.A

      include Owl_types_ndarray_algodiff.Sig
      include Owl_types_ndarray_eltcmp.Sig
      include Owl_types_ndarray_basic.Sig
      type arr
      type elt
      val empty : int array -> arr
      val zeros : int array -> arr
      val ones : int array -> arr
      val create : int array -> elt -> arr
      val sequential : ?a:elt -> ?step:elt -> int array -> arr
      val uniform : ?a:elt -> ?b:elt -> int array -> arr
      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
      val bernoulli : ?p:elt -> int array -> arr
      val init : int array -> (int -> elt) -> arr
      val init_nd : int array -> (int array -> elt) -> arr
      val shape : arr -> int array
      val numel : arr -> int
      val get : arr -> int array -> elt
      val set : arr -> int array -> elt -> unit
      val get_slice : int list list -> arr -> arr
      val set_slice : int list list -> arr -> arr -> unit
      val get_fancy : Owl_types_common.index list -> arr -> arr
      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
      val copy : arr -> arr
      val copy_ : out:arr -> arr -> unit
      val reset : arr -> unit
      val reshape : arr -> int array -> arr
      val reverse : arr -> arr
      val tile : arr -> int array -> arr
      val repeat : arr -> int array -> arr
      val concatenate : ?axis:int -> arr array -> arr
      val stack : ?axis:int -> arr array -> arr
      val split : ?axis:int -> int array -> arr -> arr array
      val expand : ?hi:bool -> arr -> int -> arr
      val squeeze : ?axis:int array -> arr -> arr
      val draw : ?axis:int -> arr -> int -> arr * int array
      val map : (elt -> elt) -> arr -> arr
      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
      val one_hot : int -> arr -> arr
      val pad : ?v:elt -> int list list -> arr -> arr
      val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index 0b974d392..7920378fd 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

      Module Type.Device

      Type definition
      type device

      TODO

      type value

      TODO

      Core functions
      val make_device : unit -> device

      TODO

      val arr_to_value : A.arr -> value

      TODO

      val value_to_arr : value -> A.arr

      TODO

      val elt_to_value : A.elt -> value

      TODO

      val value_to_elt : value -> A.elt

      TODO

      val value_to_float : value -> float

      TODO

      val is_arr : value -> bool

      TODO

      val is_elt : value -> bool

      TODO

      +Device (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

      Module Type.Device

      Type definition
      type device

      TODO

      type value

      TODO

      Core functions
      val make_device : unit -> device

      TODO

      val arr_to_value : A.arr -> value

      TODO

      val value_to_arr : value -> A.arr

      TODO

      val elt_to_value : A.elt -> value

      TODO

      val value_to_elt : value -> A.elt

      TODO

      val value_to_float : value -> float

      TODO

      val is_arr : value -> bool

      TODO

      val is_elt : value -> bool

      TODO

      diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index 3c797aeab..30f091835 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type)

      Module Shape.Type

      Type definition
      type state =
      1. | Valid
      2. | Invalid
        (*

        TODO

        *)

      TODO

      and block = {
      1. size : int;
      2. block_id : int;
      3. mutable active : t option;
      4. mutable memory : Device.value;
      5. mutable nodes : t list;
      }

      block type keeps a reference to a block of memory and to the nodes sharing that block.

      and attr = {
      1. mutable op : op;
      2. mutable freeze : bool;
      3. mutable reuse : bool;
      4. mutable state : state;
      5. mutable shape : int array option array;
      6. mutable value : Device.value array;
      7. mutable block : block array option;
      }

      TODO

      and arr =
      1. | Arr of t
      and elt =
      1. | Elt of t
      and op =
      1. | Noop
      2. | Var
      3. | Const
      4. | Empty of int array
      5. | Zeros of int array
      6. | Ones of int array
      7. | Create of int array
      8. | Sequential of int array
      9. | Uniform of int array
      10. | Gaussian of int array
      11. | Bernoulli of int array
      12. | Init of int array * int -> elt
      13. | Get of int array
      14. | Set of int array
      15. | GetSlice of int list list
      16. | SetSlice of int list list
      17. | GetFancy of Owl_types_common.index list
      18. | SetFancy of Owl_types_common.index list
      19. | Copy
      20. | Reset
      21. | Reshape of int array
      22. | Reverse
      23. | Tile of int array
      24. | Repeat of int array
      25. | Pad of elt * int list list
      26. | Concatenate of int
      27. | Stack of int
      28. | Split of int * int array
      29. | Draw of int * int
      30. | Map of elt -> elt
      31. | Fold of int * elt -> elt -> elt
      32. | Scan of int * elt -> elt -> elt
      33. | OneHot of int
      34. | OfArray of int array
      35. | Delay of Device.A.arr -> Device.A.arr
      36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
      37. | LazyPrint of int option +Type (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape.Type)

        Module Shape.Type

        Type definition
        type state =
        1. | Valid
        2. | Invalid
          (*

          TODO

          *)

        TODO

        and block = {
        1. size : int;
        2. block_id : int;
        3. mutable active : t option;
        4. mutable memory : Device.value;
        5. mutable nodes : t list;
        }

        block type keeps a reference to a block of memory and to the nodes sharing that block.

        and attr = {
        1. mutable op : op;
        2. mutable freeze : bool;
        3. mutable reuse : bool;
        4. mutable state : state;
        5. mutable shape : int array option array;
        6. mutable value : Device.value array;
        7. mutable block : block array option;
        }

        TODO

        and arr =
        1. | Arr of t
        and elt =
        1. | Elt of t
        and op =
        1. | Noop
        2. | Var
        3. | Const
        4. | Empty of int array
        5. | Zeros of int array
        6. | Ones of int array
        7. | Create of int array
        8. | Sequential of int array
        9. | Uniform of int array
        10. | Gaussian of int array
        11. | Bernoulli of int array
        12. | Init of int array * int -> elt
        13. | Get of int array
        14. | Set of int array
        15. | GetSlice of int list list
        16. | SetSlice of int list list
        17. | GetFancy of Owl_types_common.index list
        18. | SetFancy of Owl_types_common.index list
        19. | Copy
        20. | Reset
        21. | Reshape of int array
        22. | Reverse
        23. | Tile of int array
        24. | Repeat of int array
        25. | Pad of elt * int list list
        26. | Concatenate of int
        27. | Stack of int
        28. | Split of int * int array
        29. | Draw of int * int
        30. | Map of elt -> elt
        31. | Fold of int * elt -> elt -> elt
        32. | Scan of int * elt -> elt -> elt
        33. | OneHot of int
        34. | OfArray of int array
        35. | Delay of Device.A.arr -> Device.A.arr
        36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
        37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
        38. | Abs
        39. | Neg
        40. | Floor
        41. | Ceil
        42. | Round
        43. | Sqr
        44. | Sqrt
        45. | Log
        46. | Log2
        47. | Log10
        48. | Exp
        49. | Sin
        50. | Cos
        51. | Tan
        52. | Sinh
        53. | Cosh
        54. | Tanh
        55. | Asin
        56. | Acos
        57. | Atan
        58. | Asinh
        59. | Acosh
        60. | Atanh
        61. | Min of bool * int
        62. | Max of bool * int
        63. | Sum of bool * int
        64. | SumReduce of int array
        65. | Signum
        66. | Sigmoid
        67. | Relu
        68. | Dawsn
        69. | Min'
        70. | Max'
        71. | Sum'
        72. | LogSumExp'
        73. | LogSumExp of bool * int
        74. | L1norm'
        75. | L2norm'
        76. | L2NormSqr'
        77. | ClipByValue
        78. | ClipByL2norm
        79. | Pow
        80. | ScalarPow
        81. | PowScalar
        82. | Atan2
        83. | ScalarAtan2
        84. | Atan2Scalar
        85. | Hypot
        86. | Min2
        87. | Max2
        88. | Add
        89. | Sub
        90. | Mul
        91. | Div
        92. | AddScalar
        93. | SubScalar
        94. | MulScalar
        95. | DivScalar
        96. | ScalarAdd
        97. | ScalarSub
        98. | ScalarMul
        99. | ScalarDiv
        100. | FMA
        101. | EltEqual
        102. | EltNotEqual
        103. | EltLess
        104. | EltGreater
        105. | EltLessEqual
        106. | EltGreaterEqual
        107. | EltEqualScalar
        108. | EltNotEqualScalar
        109. | EltLessScalar
        110. | EltGreaterScalar
        111. | EltLessEqualScalar
        112. | EltGreaterEqualScalar
        113. | Conv1d of Owl_types_common.padding * int array
        114. | Conv2d of Owl_types_common.padding * int array
        115. | Conv3d of Owl_types_common.padding * int array
        116. | TransposeConv1d of Owl_types_common.padding * int array
        117. | TransposeConv2d of Owl_types_common.padding * int array
        118. | TransposeConv3d of Owl_types_common.padding * int array
        119. | DilatedConv1d of Owl_types_common.padding * int array * int array
        120. | DilatedConv2d of Owl_types_common.padding * int array * int array
        121. | DilatedConv3d of Owl_types_common.padding * int array * int array
        122. | MaxPool1d of Owl_types_common.padding * int array * int array
        123. | MaxPool2d of Owl_types_common.padding * int array * int array
        124. | MaxPool3d of Owl_types_common.padding * int array * int array
        125. | AvgPool1d of Owl_types_common.padding * int array * int array
        126. | AvgPool2d of Owl_types_common.padding * int array * int array
        127. | AvgPool3d of Owl_types_common.padding * int array * int array
        128. | UpSampling2d of int array
        129. | Conv1dBackwardInput of int array
        130. | Conv1dBackwardKernel of int array
        131. | Conv2dBackwardInput of int array
        132. | Conv2dBackwardKernel of int array
        133. | Conv3dBackwardInput of int array
        134. | Conv3dBackwardKernel of int array
        135. | TransposeConv1dBackwardInput of int array
        136. | TransposeConv1dBackwardKernel of int array
        137. | TransposeConv2dBackwardInput of int array
        138. | TransposeConv2dBackwardKernel of int array
        139. | TransposeConv3dBackwardInput of int array
        140. | TransposeConv3dBackwardKernel of int array
        141. | DilatedConv1dBackwardInput of int array * int array
        142. | DilatedConv1dBackwardKernel of int array * int array
        143. | DilatedConv2dBackwardInput of int array * int array
        144. | DilatedConv2dBackwardKernel of int array * int array
        145. | DilatedConv3dBackwardInput of int array * int array
        146. | DilatedConv3dBackwardKernel of int array * int array
        147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
        148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
        149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
        150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
        151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
        152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
        153. | UpSampling2dBackward of int array
        154. | RowNum
        155. | ColNum
        156. | Row
        157. | Rows of int array
        158. | CopyRowTo
        159. | CopyColTo
        160. | Dot of bool * bool * elt * elt
        161. | Inv
        162. | Trace
        163. | Transpose of int array
        164. | ToRows
        165. | OfRows
        166. | Scalar_Add
        167. | Scalar_Sub
        168. | Scalar_Mul
        169. | Scalar_Div
        170. | Scalar_Pow
        171. | Scalar_Atan2
        172. | Scalar_Abs
        173. | Scalar_Neg
        174. | Scalar_Sqr
        175. | Scalar_Sqrt
        176. | Scalar_Exp
        177. | Scalar_Log
        178. | Scalar_Log2
        179. | Scalar_Log10
        180. | Scalar_Signum
        181. | Scalar_Floor
        182. | Scalar_Ceil
        183. | Scalar_Round
        184. | Scalar_Sin
        185. | Scalar_Cos
        186. | Scalar_Tan
        187. | Scalar_Sinh
        188. | Scalar_Cosh
        189. | Scalar_Tanh
        190. | Scalar_Asin
        191. | Scalar_Acos
        192. | Scalar_Atan
        193. | Scalar_Asinh
        194. | Scalar_Acosh
        195. | Scalar_Atanh
        196. | Scalar_Relu
        197. | Scalar_Dawsn
        198. | Scalar_Sigmoid
        199. | Fused_Adagrad of float * float
          (*

          TODO

          *)
        diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html index 089f695e5..baeff546b 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape)

        Module Symbol.Shape

        Core functions
        val infer_shape : +Shape (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol.Shape)

        Module Symbol.Shape

        Core functions
        val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

        TODO

        diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/index.html index 19a65d579..0b1f16889 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol)

        Module Operator.Symbol

        Core functions
        val op_to_str : Shape.Type.op -> string

        TODO

        val is_random_variable : Shape.Type.op -> bool

        TODO

        val refnum : 'a Owl_graph.node -> int

        TODO

        val node_shape : Shape.Type.attr Owl_graph.node -> int array

        TODO

        val node_numel : Shape.Type.attr Owl_graph.node -> int

        TODO

        val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

        TODO

        val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

        TODO

        val shape_to_str : int array option array -> string

        TODO

        val node_to_str : Shape.Type.attr Owl_graph.node -> string

        TODO

        val node_to_arr : Shape.Type.t -> Shape.Type.arr

        TODO

        val arr_to_node : Shape.Type.arr -> Shape.Type.t

        TODO

        val node_to_elt : Shape.Type.t -> Shape.Type.elt

        TODO

        val elt_to_node : Shape.Type.elt -> Shape.Type.t

        TODO

        val make_node : +Symbol (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator.Symbol)

        Module Operator.Symbol

        Core functions
        val op_to_str : Shape.Type.op -> string

        TODO

        val is_random_variable : Shape.Type.op -> bool

        TODO

        val refnum : 'a Owl_graph.node -> int

        TODO

        val node_shape : Shape.Type.attr Owl_graph.node -> int array

        TODO

        val node_numel : Shape.Type.attr Owl_graph.node -> int

        TODO

        val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

        TODO

        val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

        TODO

        val shape_to_str : int array option array -> string

        TODO

        val node_to_str : Shape.Type.attr Owl_graph.node -> string

        TODO

        val node_to_arr : Shape.Type.t -> Shape.Type.arr

        TODO

        val arr_to_node : Shape.Type.arr -> Shape.Type.t

        TODO

        val node_to_elt : Shape.Type.t -> Shape.Type.elt

        TODO

        val elt_to_node : Shape.Type.elt -> Shape.Type.t

        TODO

        val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/index.html index 673f2cbea..6f69cdd20 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator)

        Module Optimiser.Operator

        Vectorised functions
        val empty : int array -> Symbol.Shape.Type.arr

        TODO

        val zeros : int array -> Symbol.Shape.Type.arr

        TODO

        val ones : int array -> Symbol.Shape.Type.arr

        TODO

        val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

        TODO

        val sequential : +Operator (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser.Operator)

        Module Optimiser.Operator

        Vectorised functions

        noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

        val empty : int array -> Symbol.Shape.Type.arr

        empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

        val zeros : int array -> Symbol.Shape.Type.arr

        zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

        val ones : int array -> Symbol.Shape.Type.arr

        ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

        val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

        create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

        val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

        TODO

        val uniform : + Symbol.Shape.Type.arr

        sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

        val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

        TODO

        val gaussian : + Symbol.Shape.Type.arr

        uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

        val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

        TODO

        val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

        TODO

        val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

        TODO

        val init_nd : + Symbol.Shape.Type.arr

        gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

        val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

        bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

        val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

        init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

        val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

        TODO

        val shape : Symbol.Shape.Type.arr -> int array

        TODO

        val numel : Symbol.Shape.Type.arr -> int

        TODO

        TODO

        val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

        TODO

        val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

        TODO

        val set_slice : + Symbol.Shape.Type.arr

        init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

        val shape : Symbol.Shape.Type.arr -> int array

        shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

        val numel : Symbol.Shape.Type.arr -> int

        numel arr returns the total number of elements in the array arr.

        get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

        val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

        set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

        val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

        get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

        val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

        TODO

        val get_fancy : + unit

        set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

        val set_fancy : + Symbol.Shape.Type.arr

        get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

        val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

        TODO

        val copy_ : out:'a -> 'b -> 'c

        TODO

        val reset : Symbol.Shape.Type.arr -> unit

        TODO

        val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        TODO

        val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        TODO

        val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        TODO

        val pad : + unit

        set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

        copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

        val copy_ : out:'a -> 'b -> 'c

        copy_ ~out src copies the contents of the array src into the pre-allocated array out.

        val reset : Symbol.Shape.Type.arr -> unit

        reset arr sets all elements of the array arr to zero.

        val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

        reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

        val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

        val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

        TODO

        val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

        TODO

        val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

        TODO

        val concatenate : + Symbol.Shape.Type.arr

        pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

        val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

        expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

        val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

        squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

        val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

        TODO

        val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

        TODO

        val concat : + Symbol.Shape.Type.arr

        concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

        val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

        stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

        val split : ?axis:int -> 'a -> 'b -> 'c

        TODO

        concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

        val split : ?axis:int -> 'a -> 'b -> 'c

        split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

        • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
        val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

        TODO

        val map : + Symbol.Shape.Type.arr * 'a array

        draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

        map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

        fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

        TODO

        val delay : + Symbol.Shape.Type.arr

        scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

        one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

        delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

        val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

        val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

        TODO

        lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

        val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

        print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

        • max_row is an optional parameter specifying the maximum number of rows to print.
        • max_col is an optional parameter specifying the maximum number of columns to print.
        • header is an optional parameter to include a header in the output.
        • fmt is an optional parameter to specify the format of the output.

        abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

        neg arr negates each element in the array arr. Returns a new array with each element negated.

        floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

        ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

        round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

        sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

        sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

        log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

        log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

        log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

        exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

        sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

        cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

        tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

        sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

        cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

        tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

        asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

        acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

        atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

        asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

        acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

        atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

        val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

        • axis specifies the axis along which to compute the minimum.
        • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
        val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

        • axis specifies the axis along which to compute the maximum.
        • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
        val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val sum_reduce : + Symbol.Shape.Type.arr

        sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

        • axis specifies the axis along which to compute the sum.
        • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
        val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val log_sum_exp : + Symbol.Shape.Type.arr

        sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

        • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

        signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

        sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

        relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

        dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

        min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

        max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

        sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

        log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

        val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val clip_by_value : + Symbol.Shape.Type.arr

        log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

        • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
        • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

        l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

        l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

        l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

        val clip_by_l2norm : + Symbol.Shape.Type.arr

        clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

        • amin specifies the minimum value to clip to.
        • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

        clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

        val scalar_pow : + Symbol.Shape.Type.arr

        pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

        val pow_scalar : + Symbol.Shape.Type.arr

        scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

        val atan2 : + Symbol.Shape.Type.arr

        pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

        val scalar_atan2 : + Symbol.Shape.Type.arr

        atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

        val atan2_scalar : + Symbol.Shape.Type.arr

        scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

        val hypot : + Symbol.Shape.Type.arr

        atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

        hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

        min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

        max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

        add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

        sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

        mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

        val add_scalar : + Symbol.Shape.Type.arr

        div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

        val sub_scalar : + Symbol.Shape.Type.arr

        add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

        val mul_scalar : + Symbol.Shape.Type.arr

        sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

        val div_scalar : + Symbol.Shape.Type.arr

        mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

        val scalar_add : + Symbol.Shape.Type.arr

        div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

        val scalar_sub : + Symbol.Shape.Type.arr

        scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

        val scalar_mul : + Symbol.Shape.Type.arr

        scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

        val scalar_div : + Symbol.Shape.Type.arr

        scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

        scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

        val elt_equal : + Symbol.Shape.Type.arr

        fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

        val elt_not_equal : + Symbol.Shape.Type.arr

        elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

        val elt_less : + Symbol.Shape.Type.arr

        elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

        val elt_greater : + Symbol.Shape.Type.arr

        elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

        val elt_less_equal : + Symbol.Shape.Type.arr

        elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

        val elt_greater_equal : + Symbol.Shape.Type.arr

        elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

        val elt_equal_scalar : + Symbol.Shape.Type.arr

        elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

        val elt_not_equal_scalar : + Symbol.Shape.Type.arr

        elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

        val elt_less_scalar : + Symbol.Shape.Type.arr

        elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

        val elt_greater_scalar : + Symbol.Shape.Type.arr

        elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

        val elt_less_equal_scalar : + Symbol.Shape.Type.arr

        elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

        TODO

        val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

        elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

        TODO

        val conv1d : + Symbol.Shape.Type.arr

        elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

        val conv2d : + Symbol.Shape.Type.arr

        conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

        • padding specifies the padding strategy (default is "valid").
        • strides specifies the stride length. Returns a new array with the result of the convolution.
        val conv3d : + Symbol.Shape.Type.arr

        conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

        • padding specifies the padding strategy (default is "valid").
        • strides specifies the stride length. Returns a new array with the result of the convolution.
        val transpose_conv1d : + Symbol.Shape.Type.arr

        conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

        • padding specifies the padding strategy (default is "valid").
        • strides specifies the stride length. Returns a new array with the result of the convolution.
        val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

        TODO

        val transpose_conv2d : + Symbol.Shape.Type.arr

        transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

        • padding specifies the padding strategy (default is "valid").
        • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
        val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

        TODO

        val transpose_conv3d : + Symbol.Shape.Type.arr

        transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

        • padding specifies the padding strategy (default is "valid").
        • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
        val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

        TODO

        val dilated_conv1d : + Symbol.Shape.Type.arr

        transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

        • padding specifies the padding strategy (default is "valid").
        • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
        val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val dilated_conv2d : + Symbol.Shape.Type.arr

        dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

        • padding specifies the padding strategy (default is "valid").
        • strides specifies the stride length.
        • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
        val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val dilated_conv3d : + Symbol.Shape.Type.arr

        dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

        • padding specifies the padding strategy (default is "valid").
        • strides specifies the stride length.
        • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
        val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val max_pool1d : + Symbol.Shape.Type.arr

        dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

        • padding specifies the padding strategy (default is "valid").
        • strides specifies the stride length.
        • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
        val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val max_pool2d : + Symbol.Shape.Type.arr

        max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

        • padding specifies the padding strategy (default is "valid").
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length. Returns a new array with the result of the max pooling.
        val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val max_pool3d : + Symbol.Shape.Type.arr

        max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

        • padding specifies the padding strategy (default is "valid").
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length. Returns a new array with the result of the max pooling.
        val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val avg_pool1d : + Symbol.Shape.Type.arr

        max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

        • padding specifies the padding strategy (default is "valid").
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length. Returns a new array with the result of the max pooling.
        val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val avg_pool2d : + Symbol.Shape.Type.arr

        avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

        • padding specifies the padding strategy (default is "valid").
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length. Returns a new array with the result of the average pooling.
        val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val avg_pool3d : + Symbol.Shape.Type.arr

        avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

        • padding specifies the padding strategy (default is "valid").
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length. Returns a new array with the result of the average pooling.
        val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        TODO

        val conv1d_backward_input : + Symbol.Shape.Type.arr

        avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

        • padding specifies the padding strategy (default is "valid").
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length. Returns a new array with the result of the average pooling.
        val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        upsampling2d input size performs a 2-dimensional upsampling on the input array.

        • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

        TODO

        val conv1d_backward_kernel : + Symbol.Shape.Type.arr

        conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

        • input is the original input array.
        • kernel is the convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
        val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val conv2d_backward_input : + Symbol.Shape.Type.arr

        conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

        • input is the original input array.
        • kernel is the convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

        TODO

        val conv2d_backward_kernel : + Symbol.Shape.Type.arr

        conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

        • input is the original input array.
        • kernel is the convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
        val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val conv3d_backward_input : + Symbol.Shape.Type.arr

        conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

        • input is the original input array.
        • kernel is the convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

        TODO

        val conv3d_backward_kernel : + Symbol.Shape.Type.arr

        conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

        • input is the original input array.
        • kernel is the convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
        val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

        conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

        • input is the original input array.
        • kernel is the convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
        val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

        transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

        • input is the original input array.
        • kernel is the transposed convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
        val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

        transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

        • input is the original input array.
        • kernel is the transposed convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
        val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

        transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

        • input is the original input array.
        • kernel is the transposed convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
        val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

        transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

        • input is the original input array.
        • kernel is the transposed convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
        val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

        transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

        • input is the original input array.
        • kernel is the transposed convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
        val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

        transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

        • input is the original input array.
        • kernel is the transposed convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
        val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

        dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

        • input is the original input array.
        • kernel is the dilated convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • dilations specifies the dilation rate.
        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
        val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

        dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

        • input is the original input array.
        • kernel is the dilated convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • dilations specifies the dilation rate.
        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
        val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

        dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

        • input is the original input array.
        • kernel is the dilated convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • dilations specifies the dilation rate.
        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
        val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

        dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

        • input is the original input array.
        • kernel is the dilated convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • dilations specifies the dilation rate.
        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
        val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

        dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

        • input is the original input array.
        • kernel is the dilated convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • dilations specifies the dilation rate.
        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
        val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val max_pool1d_backward : + Symbol.Shape.Type.arr

        dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

        • input is the original input array.
        • kernel is the dilated convolutional kernel used during the forward pass.
        • strides specifies the stride length.
        • dilations specifies the dilation rate.
        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
        val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val max_pool2d_backward : + Symbol.Shape.Type.arr

        max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

        • padding specifies the padding strategy used during the forward pass.
        • input is the original input array.
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
        val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val max_pool3d_backward : + Symbol.Shape.Type.arr

        max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

        • padding specifies the padding strategy used during the forward pass.
        • input is the original input array.
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
        val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val avg_pool1d_backward : + Symbol.Shape.Type.arr

        max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

        • padding specifies the padding strategy used during the forward pass.
        • input is the original input array.
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
        val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val avg_pool2d_backward : + Symbol.Shape.Type.arr

        avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

        • padding specifies the padding strategy used during the forward pass.
        • input is the original input array.
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
        val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val avg_pool3d_backward : + Symbol.Shape.Type.arr

        avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

        • padding specifies the padding strategy used during the forward pass.
        • input is the original input array.
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
        val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val upsampling2d_backward : + Symbol.Shape.Type.arr

        avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

        • padding specifies the padding strategy used during the forward pass.
        • input is the original input array.
        • pool_size specifies the size of the pooling window.
        • strides specifies the stride length.
        • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
        val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val row_num : Symbol.Shape.Type.arr -> int

        TODO

        val col_num : Symbol.Shape.Type.arr -> int

        TODO

        val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        TODO

        val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

        TODO

        val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

        TODO

        TODO

        upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

        • input is the original input array.
        • size specifies the upsampling factors for each dimension.
        • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
        val row_num : Symbol.Shape.Type.arr -> int

        row_num arr returns the number of rows in the array arr.

        val col_num : Symbol.Shape.Type.arr -> int

        col_num arr returns the number of columns in the array arr.

        row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

        val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

        rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

        val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

        copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

        val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

        copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

        diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

        trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

        val transpose : + Symbol.Shape.Type.arr

        dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

        val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val to_rows : Symbol.Shape.Type.arr -> 'a array

        TODO

        TODO

        val to_cols : Symbol.Shape.Type.arr -> 'a array

        TODO

        TODO

        val of_array : + Symbol.Shape.Type.arr

        transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

        val to_rows : Symbol.Shape.Type.arr -> 'a array

        to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

        of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

        val to_cols : Symbol.Shape.Type.arr -> 'a array

        to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

        of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

        val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

        TODO

        val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

        TODO

        val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

        TODO

        Scalar functions
        module Scalar : sig ... end
        module Mat : sig ... end
        module Linalg : sig ... end
        + Symbol.Shape.Type.arr

        of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

        val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

        of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

        val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

        to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

        Scalar functions
        module Scalar : sig ... end
        module Mat : sig ... end
        module Linalg : sig ... end
        diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/index.html index 75e154855..f2a3df2ee 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser)

        Module Graph.Optimiser

        Core functions
        val estimate_complexity : 'a Owl_graph.node array -> int * int

        TODO

        val optimise_nodes : +Optimiser (owl-base.Owl_computation_cpu_init.Make.Graph.Optimiser)

        Module Graph.Optimiser

        Core functions
        val estimate_complexity : 'a Owl_graph.node array -> int * int

        TODO

        val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

        TODO

        diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/index.html index 885502c4c..1a47efab4 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/argument-1-Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_computation_cpu_init.Make.Graph)

        Parameter Make.Graph

        Type definition
        type graph

        TODO

        Core functions
        val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

        TODO

        val graph_to_dot : graph -> string

        TODO

        val graph_to_trace : graph -> string

        TODO

        val save_graph : 'a -> string -> unit

        TODO

        val load_graph : string -> 'a * 'b

        TODO

        val collect_rvs : +Graph (owl-base.Owl_computation_cpu_init.Make.Graph)

        Parameter Make.Graph

        Type definition
        type graph

        TODO

        Core functions
        val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

        TODO

        val graph_to_dot : graph -> string

        TODO

        val graph_to_trace : graph -> string

        TODO

        val save_graph : 'a -> string -> unit

        TODO

        val load_graph : string -> 'a * 'b

        TODO

        val invalidate_rvs : graph -> unit

        TODO

        val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_computation_cpu_init/Make/index.html b/docs/owl-base/Owl_computation_cpu_init/Make/index.html index aa2d2750e..3b127de12 100644 --- a/docs/owl-base/Owl_computation_cpu_init/Make/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_computation_cpu_init.Make)

        Module Owl_computation_cpu_init.Make

        Parameters

        Signature

        module MultiMap : sig ... end
        val split_00 : 'a -> 'b array * 'c
        val split_01 : 'a -> 'b * 'c array
        val split_02 : +Make (owl-base.Owl_computation_cpu_init.Make)

        Module Owl_computation_cpu_init.Make

        Parameters

        Signature

        module MultiMap : sig ... end
        val split_00 : 'a -> 'b array * 'c
        val split_01 : 'a -> 'b * 'c array
        val split_02 : Graph.Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node -> Graph.Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> Graph.Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array diff --git a/docs/owl-base/Owl_computation_cpu_init/index.html b/docs/owl-base/Owl_computation_cpu_init/index.html index 3c9ba2cdc..6db25a8f9 100644 --- a/docs/owl-base/Owl_computation_cpu_init/index.html +++ b/docs/owl-base/Owl_computation_cpu_init/index.html @@ -1,2 +1,2 @@ -Owl_computation_cpu_init (owl-base.Owl_computation_cpu_init)

        Module Owl_computation_cpu_init

        module Make (Graph : Owl_computation_graph_sig.Sig) : sig ... end
        +Owl_computation_cpu_init (owl-base.Owl_computation_cpu_init)

        Module Owl_computation_cpu_init

        module Make (Graph : Owl_computation_graph_sig.Sig) : sig ... end
        diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Linalg/index.html index f16f7b291..31e7aaea9 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Linalg)

        Module Operator.Linalg

        val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

        TODO

        val svd : +Linalg (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Linalg)

        Module Operator.Linalg

        inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

        logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

        val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

        chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

        • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

        qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

        lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

        svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

        • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
        val lyapunov : + Symbol.Shape.Type.arr

        sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

        val discrete_lyapunov : + Symbol.Shape.Type.arr

        lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

        val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        val linsolve : + Symbol.Shape.Type.arr

        discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

        • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
        val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

        TODO

        linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

        • trans specifies whether to transpose the matrix A.
        • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

        care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

        • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
        + Symbol.Shape.Type.arr

        dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

        • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
        diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Mat/index.html index 703d0928b..0363d4070 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Mat)

        Module Operator.Mat

        val eye : int -> Symbol.Shape.Type.arr

        TODO

        TODO

        TODO

        TODO

        +Mat (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Mat)

        Module Operator.Mat

        val eye : int -> Symbol.Shape.Type.arr

        eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

        diagm ?k v creates a diagonal matrix from the array v.

        • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

        triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

        tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

        diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Scalar/index.html index 96387da07..c064778fd 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Scalar)

        Module Operator.Scalar

        val add : +Scalar (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Scalar)

        Module Operator.Scalar

        add a b returns the sum of the scalars a and b.

        sub a b returns the difference of the scalars a and b.

        mul a b returns the product of the scalars a and b.

        div a b returns the quotient of the scalars a and b.

        val atan2 : + Symbol.Shape.Type.elt

        pow a b returns the scalar a raised to the power of b.

        + Symbol.Shape.Type.elt

        atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

        abs a returns the absolute value of the scalar a.

        neg a returns the negation of the scalar a.

        sqr a returns the square of the scalar a.

        sqrt a returns the square root of the scalar a.

        exp a returns the exponential of the scalar a.

        log a returns the natural logarithm of the scalar a.

        log2 a returns the base-2 logarithm of the scalar a.

        log10 a returns the base-10 logarithm of the scalar a.

        signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

        floor a returns the greatest integer less than or equal to the scalar a.

        ceil a returns the smallest integer greater than or equal to the scalar a.

        round a returns the nearest integer to the scalar a.

        sin a returns the sine of the scalar a.

        cos a returns the cosine of the scalar a.

        tan a returns the tangent of the scalar a.

        sinh a returns the hyperbolic sine of the scalar a.

        cosh a returns the hyperbolic cosine of the scalar a.

        tanh a returns the hyperbolic tangent of the scalar a.

        asin a returns the arcsine of the scalar a.

        acos a returns the arccosine of the scalar a.

        atan a returns the arctangent of the scalar a.

        asinh a returns the inverse hyperbolic sine of the scalar a.

        acosh a returns the inverse hyperbolic cosine of the scalar a.

        atanh a returns the inverse hyperbolic tangent of the scalar a.

        relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

        dawsn a returns Dawson's function of the scalar a.

        sigmoid a returns the sigmoid function of the scalar a.

        diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index bc82dfcf8..d4a61c972 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

        Module A.Linalg

        val inv : arr -> arr
        val logdet : arr -> elt
        val chol : ?upper:bool -> arr -> arr
        val svd : ?thin:bool -> arr -> arr * arr * arr
        val qr : arr -> arr * arr
        val lq : arr -> arr * arr
        val sylvester : arr -> arr -> arr -> arr
        val lyapunov : arr -> arr -> arr
        val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

        Module A.Linalg

        val inv : arr -> arr
        val logdet : arr -> elt
        val chol : ?upper:bool -> arr -> arr
        val svd : ?thin:bool -> arr -> arr * arr * arr
        val qr : arr -> arr * arr
        val lq : arr -> arr * arr
        val sylvester : arr -> arr -> arr -> arr
        val lyapunov : arr -> arr -> arr
        val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 136ff7f76..3b0eeb23b 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

        Module A.Mat

        val diagm : ?k:int -> arr -> arr
        val triu : ?k:int -> arr -> arr
        val tril : ?k:int -> arr -> arr
        val eye : int -> arr
        +Mat (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

        Module A.Mat

        val diagm : ?k:int -> arr -> arr
        val triu : ?k:int -> arr -> arr
        val tril : ?k:int -> arr -> arr
        val eye : int -> arr
        diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index c59083aa6..9877f959b 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

        Module A.Scalar

        val add : elt -> elt -> elt
        val sub : elt -> elt -> elt
        val mul : elt -> elt -> elt
        val div : elt -> elt -> elt
        val pow : elt -> elt -> elt
        val atan2 : elt -> elt -> elt
        val abs : elt -> elt
        val neg : elt -> elt
        val sqr : elt -> elt
        val sqrt : elt -> elt
        val exp : elt -> elt
        val log : elt -> elt
        val log2 : elt -> elt
        val log10 : elt -> elt
        val signum : elt -> elt
        val floor : elt -> elt
        val ceil : elt -> elt
        val round : elt -> elt
        val sin : elt -> elt
        val cos : elt -> elt
        val tan : elt -> elt
        val sinh : elt -> elt
        val cosh : elt -> elt
        val tanh : elt -> elt
        val asin : elt -> elt
        val acos : elt -> elt
        val atan : elt -> elt
        val asinh : elt -> elt
        val acosh : elt -> elt
        val atanh : elt -> elt
        val relu : elt -> elt
        val dawsn : elt -> elt
        val sigmoid : elt -> elt
        +Scalar (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

        Module A.Scalar

        val add : elt -> elt -> elt
        val sub : elt -> elt -> elt
        val mul : elt -> elt -> elt
        val div : elt -> elt -> elt
        val pow : elt -> elt -> elt
        val atan2 : elt -> elt -> elt
        val abs : elt -> elt
        val neg : elt -> elt
        val sqr : elt -> elt
        val sqrt : elt -> elt
        val exp : elt -> elt
        val log : elt -> elt
        val log2 : elt -> elt
        val log10 : elt -> elt
        val signum : elt -> elt
        val floor : elt -> elt
        val ceil : elt -> elt
        val round : elt -> elt
        val sin : elt -> elt
        val cos : elt -> elt
        val tan : elt -> elt
        val sinh : elt -> elt
        val cosh : elt -> elt
        val tanh : elt -> elt
        val asin : elt -> elt
        val acos : elt -> elt
        val atan : elt -> elt
        val asinh : elt -> elt
        val acosh : elt -> elt
        val atanh : elt -> elt
        val relu : elt -> elt
        val dawsn : elt -> elt
        val sigmoid : elt -> elt
        diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index 122c45795..73ad5f4d9 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

        Module Device.A

        include Owl_types_ndarray_algodiff.Sig
        include Owl_types_ndarray_eltcmp.Sig
        include Owl_types_ndarray_basic.Sig
        type arr
        type elt
        val empty : int array -> arr
        val zeros : int array -> arr
        val ones : int array -> arr
        val create : int array -> elt -> arr
        val sequential : ?a:elt -> ?step:elt -> int array -> arr
        val uniform : ?a:elt -> ?b:elt -> int array -> arr
        val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
        val bernoulli : ?p:elt -> int array -> arr
        val init : int array -> (int -> elt) -> arr
        val init_nd : int array -> (int array -> elt) -> arr
        val shape : arr -> int array
        val numel : arr -> int
        val get : arr -> int array -> elt
        val set : arr -> int array -> elt -> unit
        val get_slice : int list list -> arr -> arr
        val set_slice : int list list -> arr -> arr -> unit
        val get_fancy : Owl_types_common.index list -> arr -> arr
        val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
        val copy : arr -> arr
        val copy_ : out:arr -> arr -> unit
        val reset : arr -> unit
        val reshape : arr -> int array -> arr
        val reverse : arr -> arr
        val tile : arr -> int array -> arr
        val repeat : arr -> int array -> arr
        val concatenate : ?axis:int -> arr array -> arr
        val stack : ?axis:int -> arr array -> arr
        val split : ?axis:int -> int array -> arr -> arr array
        val expand : ?hi:bool -> arr -> int -> arr
        val squeeze : ?axis:int array -> arr -> arr
        val draw : ?axis:int -> arr -> int -> arr * int array
        val map : (elt -> elt) -> arr -> arr
        val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
        val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
        val one_hot : int -> arr -> arr
        val pad : ?v:elt -> int list list -> arr -> arr
        val print : +A (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

        Module Device.A

        include Owl_types_ndarray_algodiff.Sig
        include Owl_types_ndarray_eltcmp.Sig
        include Owl_types_ndarray_basic.Sig
        type arr
        type elt
        val empty : int array -> arr
        val zeros : int array -> arr
        val ones : int array -> arr
        val create : int array -> elt -> arr
        val sequential : ?a:elt -> ?step:elt -> int array -> arr
        val uniform : ?a:elt -> ?b:elt -> int array -> arr
        val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
        val bernoulli : ?p:elt -> int array -> arr
        val init : int array -> (int -> elt) -> arr
        val init_nd : int array -> (int array -> elt) -> arr
        val shape : arr -> int array
        val numel : arr -> int
        val get : arr -> int array -> elt
        val set : arr -> int array -> elt -> unit
        val get_slice : int list list -> arr -> arr
        val set_slice : int list list -> arr -> arr -> unit
        val get_fancy : Owl_types_common.index list -> arr -> arr
        val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
        val copy : arr -> arr
        val copy_ : out:arr -> arr -> unit
        val reset : arr -> unit
        val reshape : arr -> int array -> arr
        val reverse : arr -> arr
        val tile : arr -> int array -> arr
        val repeat : arr -> int array -> arr
        val concatenate : ?axis:int -> arr array -> arr
        val stack : ?axis:int -> arr array -> arr
        val split : ?axis:int -> int array -> arr -> arr array
        val expand : ?hi:bool -> arr -> int -> arr
        val squeeze : ?axis:int array -> arr -> arr
        val draw : ?axis:int -> arr -> int -> arr * int array
        val map : (elt -> elt) -> arr -> arr
        val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
        val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
        val one_hot : int -> arr -> arr
        val pad : ?v:elt -> int list list -> arr -> arr
        val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index a9adbec56..be6e51a1b 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

        Module Type.Device

        Type definition
        type device

        TODO

        type value

        TODO

        Core functions
        val make_device : unit -> device

        TODO

        val arr_to_value : A.arr -> value

        TODO

        val value_to_arr : value -> A.arr

        TODO

        val elt_to_value : A.elt -> value

        TODO

        val value_to_elt : value -> A.elt

        TODO

        val value_to_float : value -> float

        TODO

        val is_arr : value -> bool

        TODO

        val is_elt : value -> bool

        TODO

        +Device (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

        Module Type.Device

        Type definition
        type device

        TODO

        type value

        TODO

        Core functions
        val make_device : unit -> device

        TODO

        val arr_to_value : A.arr -> value

        TODO

        val value_to_arr : value -> A.arr

        TODO

        val elt_to_value : A.elt -> value

        TODO

        val value_to_elt : value -> A.elt

        TODO

        val value_to_float : value -> float

        TODO

        val is_arr : value -> bool

        TODO

        val is_elt : value -> bool

        TODO

        diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index da3981de1..24814ab7a 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type)

        Module Shape.Type

        Type definition
        type state =
        1. | Valid
        2. | Invalid
          (*

          TODO

          *)

        TODO

        and block = {
        1. size : int;
        2. block_id : int;
        3. mutable active : t option;
        4. mutable memory : Device.value;
        5. mutable nodes : t list;
        }

        block type keeps a reference to a block of memory and to the nodes sharing that block.

        and attr = {
        1. mutable op : op;
        2. mutable freeze : bool;
        3. mutable reuse : bool;
        4. mutable state : state;
        5. mutable shape : int array option array;
        6. mutable value : Device.value array;
        7. mutable block : block array option;
        }

        TODO

        and arr =
        1. | Arr of t
        and elt =
        1. | Elt of t
        and op =
        1. | Noop
        2. | Var
        3. | Const
        4. | Empty of int array
        5. | Zeros of int array
        6. | Ones of int array
        7. | Create of int array
        8. | Sequential of int array
        9. | Uniform of int array
        10. | Gaussian of int array
        11. | Bernoulli of int array
        12. | Init of int array * int -> elt
        13. | Get of int array
        14. | Set of int array
        15. | GetSlice of int list list
        16. | SetSlice of int list list
        17. | GetFancy of Owl_types_common.index list
        18. | SetFancy of Owl_types_common.index list
        19. | Copy
        20. | Reset
        21. | Reshape of int array
        22. | Reverse
        23. | Tile of int array
        24. | Repeat of int array
        25. | Pad of elt * int list list
        26. | Concatenate of int
        27. | Stack of int
        28. | Split of int * int array
        29. | Draw of int * int
        30. | Map of elt -> elt
        31. | Fold of int * elt -> elt -> elt
        32. | Scan of int * elt -> elt -> elt
        33. | OneHot of int
        34. | OfArray of int array
        35. | Delay of Device.A.arr -> Device.A.arr
        36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
        37. | LazyPrint of int option +Type (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type)

          Module Shape.Type

          Type definition
          type state =
          1. | Valid
          2. | Invalid
            (*

            TODO

            *)

          TODO

          and block = {
          1. size : int;
          2. block_id : int;
          3. mutable active : t option;
          4. mutable memory : Device.value;
          5. mutable nodes : t list;
          }

          block type keeps a reference to a block of memory and to the nodes sharing that block.

          and attr = {
          1. mutable op : op;
          2. mutable freeze : bool;
          3. mutable reuse : bool;
          4. mutable state : state;
          5. mutable shape : int array option array;
          6. mutable value : Device.value array;
          7. mutable block : block array option;
          }

          TODO

          and arr =
          1. | Arr of t
          and elt =
          1. | Elt of t
          and op =
          1. | Noop
          2. | Var
          3. | Const
          4. | Empty of int array
          5. | Zeros of int array
          6. | Ones of int array
          7. | Create of int array
          8. | Sequential of int array
          9. | Uniform of int array
          10. | Gaussian of int array
          11. | Bernoulli of int array
          12. | Init of int array * int -> elt
          13. | Get of int array
          14. | Set of int array
          15. | GetSlice of int list list
          16. | SetSlice of int list list
          17. | GetFancy of Owl_types_common.index list
          18. | SetFancy of Owl_types_common.index list
          19. | Copy
          20. | Reset
          21. | Reshape of int array
          22. | Reverse
          23. | Tile of int array
          24. | Repeat of int array
          25. | Pad of elt * int list list
          26. | Concatenate of int
          27. | Stack of int
          28. | Split of int * int array
          29. | Draw of int * int
          30. | Map of elt -> elt
          31. | Fold of int * elt -> elt -> elt
          32. | Scan of int * elt -> elt -> elt
          33. | OneHot of int
          34. | OfArray of int array
          35. | Delay of Device.A.arr -> Device.A.arr
          36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
          37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
          38. | Abs
          39. | Neg
          40. | Floor
          41. | Ceil
          42. | Round
          43. | Sqr
          44. | Sqrt
          45. | Log
          46. | Log2
          47. | Log10
          48. | Exp
          49. | Sin
          50. | Cos
          51. | Tan
          52. | Sinh
          53. | Cosh
          54. | Tanh
          55. | Asin
          56. | Acos
          57. | Atan
          58. | Asinh
          59. | Acosh
          60. | Atanh
          61. | Min of bool * int
          62. | Max of bool * int
          63. | Sum of bool * int
          64. | SumReduce of int array
          65. | Signum
          66. | Sigmoid
          67. | Relu
          68. | Dawsn
          69. | Min'
          70. | Max'
          71. | Sum'
          72. | LogSumExp'
          73. | LogSumExp of bool * int
          74. | L1norm'
          75. | L2norm'
          76. | L2NormSqr'
          77. | ClipByValue
          78. | ClipByL2norm
          79. | Pow
          80. | ScalarPow
          81. | PowScalar
          82. | Atan2
          83. | ScalarAtan2
          84. | Atan2Scalar
          85. | Hypot
          86. | Min2
          87. | Max2
          88. | Add
          89. | Sub
          90. | Mul
          91. | Div
          92. | AddScalar
          93. | SubScalar
          94. | MulScalar
          95. | DivScalar
          96. | ScalarAdd
          97. | ScalarSub
          98. | ScalarMul
          99. | ScalarDiv
          100. | FMA
          101. | EltEqual
          102. | EltNotEqual
          103. | EltLess
          104. | EltGreater
          105. | EltLessEqual
          106. | EltGreaterEqual
          107. | EltEqualScalar
          108. | EltNotEqualScalar
          109. | EltLessScalar
          110. | EltGreaterScalar
          111. | EltLessEqualScalar
          112. | EltGreaterEqualScalar
          113. | Conv1d of Owl_types_common.padding * int array
          114. | Conv2d of Owl_types_common.padding * int array
          115. | Conv3d of Owl_types_common.padding * int array
          116. | TransposeConv1d of Owl_types_common.padding * int array
          117. | TransposeConv2d of Owl_types_common.padding * int array
          118. | TransposeConv3d of Owl_types_common.padding * int array
          119. | DilatedConv1d of Owl_types_common.padding * int array * int array
          120. | DilatedConv2d of Owl_types_common.padding * int array * int array
          121. | DilatedConv3d of Owl_types_common.padding * int array * int array
          122. | MaxPool1d of Owl_types_common.padding * int array * int array
          123. | MaxPool2d of Owl_types_common.padding * int array * int array
          124. | MaxPool3d of Owl_types_common.padding * int array * int array
          125. | AvgPool1d of Owl_types_common.padding * int array * int array
          126. | AvgPool2d of Owl_types_common.padding * int array * int array
          127. | AvgPool3d of Owl_types_common.padding * int array * int array
          128. | UpSampling2d of int array
          129. | Conv1dBackwardInput of int array
          130. | Conv1dBackwardKernel of int array
          131. | Conv2dBackwardInput of int array
          132. | Conv2dBackwardKernel of int array
          133. | Conv3dBackwardInput of int array
          134. | Conv3dBackwardKernel of int array
          135. | TransposeConv1dBackwardInput of int array
          136. | TransposeConv1dBackwardKernel of int array
          137. | TransposeConv2dBackwardInput of int array
          138. | TransposeConv2dBackwardKernel of int array
          139. | TransposeConv3dBackwardInput of int array
          140. | TransposeConv3dBackwardKernel of int array
          141. | DilatedConv1dBackwardInput of int array * int array
          142. | DilatedConv1dBackwardKernel of int array * int array
          143. | DilatedConv2dBackwardInput of int array * int array
          144. | DilatedConv2dBackwardKernel of int array * int array
          145. | DilatedConv3dBackwardInput of int array * int array
          146. | DilatedConv3dBackwardKernel of int array * int array
          147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
          148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
          149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
          150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
          151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
          152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
          153. | UpSampling2dBackward of int array
          154. | RowNum
          155. | ColNum
          156. | Row
          157. | Rows of int array
          158. | CopyRowTo
          159. | CopyColTo
          160. | Dot of bool * bool * elt * elt
          161. | Inv
          162. | Trace
          163. | Transpose of int array
          164. | ToRows
          165. | OfRows
          166. | Scalar_Add
          167. | Scalar_Sub
          168. | Scalar_Mul
          169. | Scalar_Div
          170. | Scalar_Pow
          171. | Scalar_Atan2
          172. | Scalar_Abs
          173. | Scalar_Neg
          174. | Scalar_Sqr
          175. | Scalar_Sqrt
          176. | Scalar_Exp
          177. | Scalar_Log
          178. | Scalar_Log2
          179. | Scalar_Log10
          180. | Scalar_Signum
          181. | Scalar_Floor
          182. | Scalar_Ceil
          183. | Scalar_Round
          184. | Scalar_Sin
          185. | Scalar_Cos
          186. | Scalar_Tan
          187. | Scalar_Sinh
          188. | Scalar_Cosh
          189. | Scalar_Tanh
          190. | Scalar_Asin
          191. | Scalar_Acos
          192. | Scalar_Atan
          193. | Scalar_Asinh
          194. | Scalar_Acosh
          195. | Scalar_Atanh
          196. | Scalar_Relu
          197. | Scalar_Dawsn
          198. | Scalar_Sigmoid
          199. | Fused_Adagrad of float * float
            (*

            TODO

            *)
          diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/index.html index 11760bdc6..31cdc196a 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape)

          Module Symbol.Shape

          Core functions
          val infer_shape : +Shape (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol.Shape)

          Module Symbol.Shape

          Core functions
          val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

          TODO

          diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/index.html index 8ef800701..b01e8c043 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol)

          Module Operator.Symbol

          Core functions
          val op_to_str : Shape.Type.op -> string

          TODO

          val is_random_variable : Shape.Type.op -> bool

          TODO

          val refnum : 'a Owl_graph.node -> int

          TODO

          val node_shape : Shape.Type.attr Owl_graph.node -> int array

          TODO

          val node_numel : Shape.Type.attr Owl_graph.node -> int

          TODO

          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

          TODO

          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

          TODO

          val shape_to_str : int array option array -> string

          TODO

          val node_to_str : Shape.Type.attr Owl_graph.node -> string

          TODO

          val node_to_arr : Shape.Type.t -> Shape.Type.arr

          TODO

          val arr_to_node : Shape.Type.arr -> Shape.Type.t

          TODO

          val node_to_elt : Shape.Type.t -> Shape.Type.elt

          TODO

          val elt_to_node : Shape.Type.elt -> Shape.Type.t

          TODO

          val make_node : +Symbol (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator.Symbol)

          Module Operator.Symbol

          Core functions
          val op_to_str : Shape.Type.op -> string

          TODO

          val is_random_variable : Shape.Type.op -> bool

          TODO

          val refnum : 'a Owl_graph.node -> int

          TODO

          val node_shape : Shape.Type.attr Owl_graph.node -> int array

          TODO

          val node_numel : Shape.Type.attr Owl_graph.node -> int

          TODO

          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

          TODO

          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

          TODO

          val shape_to_str : int array option array -> string

          TODO

          val node_to_str : Shape.Type.attr Owl_graph.node -> string

          TODO

          val node_to_arr : Shape.Type.t -> Shape.Type.arr

          TODO

          val arr_to_node : Shape.Type.arr -> Shape.Type.t

          TODO

          val node_to_elt : Shape.Type.t -> Shape.Type.elt

          TODO

          val elt_to_node : Shape.Type.elt -> Shape.Type.t

          TODO

          val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/index.html index 5d890d5fd..a4ea34caa 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator)

          Module Optimiser.Operator

          Vectorised functions
          val empty : int array -> Symbol.Shape.Type.arr

          TODO

          val zeros : int array -> Symbol.Shape.Type.arr

          TODO

          val ones : int array -> Symbol.Shape.Type.arr

          TODO

          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

          TODO

          val sequential : +Operator (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser.Operator)

          Module Optimiser.Operator

          Vectorised functions

          noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

          val empty : int array -> Symbol.Shape.Type.arr

          empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

          val zeros : int array -> Symbol.Shape.Type.arr

          zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

          val ones : int array -> Symbol.Shape.Type.arr

          ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

          create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

          val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

          TODO

          val uniform : + Symbol.Shape.Type.arr

          sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

          val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

          TODO

          val gaussian : + Symbol.Shape.Type.arr

          uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

          val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

          TODO

          val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

          TODO

          val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

          TODO

          val init_nd : + Symbol.Shape.Type.arr

          gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

          val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

          bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

          val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

          init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

          val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

          TODO

          val shape : Symbol.Shape.Type.arr -> int array

          TODO

          val numel : Symbol.Shape.Type.arr -> int

          TODO

          TODO

          val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

          TODO

          val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

          TODO

          val set_slice : + Symbol.Shape.Type.arr

          init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

          val shape : Symbol.Shape.Type.arr -> int array

          shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

          val numel : Symbol.Shape.Type.arr -> int

          numel arr returns the total number of elements in the array arr.

          get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

          val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

          set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

          val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

          get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

          val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

          TODO

          val get_fancy : + unit

          set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

          val set_fancy : + Symbol.Shape.Type.arr

          get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

          val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

          TODO

          val copy_ : out:'a -> 'b -> 'c

          TODO

          val reset : Symbol.Shape.Type.arr -> unit

          TODO

          val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          TODO

          val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          TODO

          val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          TODO

          val pad : + unit

          set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

          copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

          val copy_ : out:'a -> 'b -> 'c

          copy_ ~out src copies the contents of the array src into the pre-allocated array out.

          val reset : Symbol.Shape.Type.arr -> unit

          reset arr sets all elements of the array arr to zero.

          val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

          reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

          val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

          val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

          TODO

          val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

          TODO

          val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

          TODO

          val concatenate : + Symbol.Shape.Type.arr

          pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

          val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

          expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

          val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

          squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

          val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

          TODO

          val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

          TODO

          val concat : + Symbol.Shape.Type.arr

          concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

          val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

          stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

          val split : ?axis:int -> 'a -> 'b -> 'c

          TODO

          concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

          val split : ?axis:int -> 'a -> 'b -> 'c

          split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

          • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
          val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

          TODO

          val map : + Symbol.Shape.Type.arr * 'a array

          draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

          map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

          fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

          TODO

          val delay : + Symbol.Shape.Type.arr

          scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

          one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

          delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

          val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

          val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

          TODO

          lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

          val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

          print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

          • max_row is an optional parameter specifying the maximum number of rows to print.
          • max_col is an optional parameter specifying the maximum number of columns to print.
          • header is an optional parameter to include a header in the output.
          • fmt is an optional parameter to specify the format of the output.

          abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

          neg arr negates each element in the array arr. Returns a new array with each element negated.

          floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

          ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

          round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

          sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

          sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

          log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

          log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

          log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

          exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

          sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

          cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

          tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

          sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

          cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

          tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

          asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

          acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

          atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

          asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

          acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

          atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

          val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

          • axis specifies the axis along which to compute the minimum.
          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
          val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

          • axis specifies the axis along which to compute the maximum.
          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
          val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val sum_reduce : + Symbol.Shape.Type.arr

          sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

          • axis specifies the axis along which to compute the sum.
          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
          val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val log_sum_exp : + Symbol.Shape.Type.arr

          sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

          • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

          signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

          sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

          relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

          dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

          min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

          max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

          sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

          log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val clip_by_value : + Symbol.Shape.Type.arr

          log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

          • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
          • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

          l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

          l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

          l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

          val clip_by_l2norm : + Symbol.Shape.Type.arr

          clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

          • amin specifies the minimum value to clip to.
          • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

          clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

          val scalar_pow : + Symbol.Shape.Type.arr

          pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

          val pow_scalar : + Symbol.Shape.Type.arr

          scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

          val atan2 : + Symbol.Shape.Type.arr

          pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

          val scalar_atan2 : + Symbol.Shape.Type.arr

          atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

          val atan2_scalar : + Symbol.Shape.Type.arr

          scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

          val hypot : + Symbol.Shape.Type.arr

          atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

          hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

          min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

          max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

          add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

          sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

          mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

          val add_scalar : + Symbol.Shape.Type.arr

          div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

          val sub_scalar : + Symbol.Shape.Type.arr

          add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

          val mul_scalar : + Symbol.Shape.Type.arr

          sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

          val div_scalar : + Symbol.Shape.Type.arr

          mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

          val scalar_add : + Symbol.Shape.Type.arr

          div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

          val scalar_sub : + Symbol.Shape.Type.arr

          scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

          val scalar_mul : + Symbol.Shape.Type.arr

          scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

          val scalar_div : + Symbol.Shape.Type.arr

          scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

          scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

          val elt_equal : + Symbol.Shape.Type.arr

          fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

          val elt_not_equal : + Symbol.Shape.Type.arr

          elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

          val elt_less : + Symbol.Shape.Type.arr

          elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

          val elt_greater : + Symbol.Shape.Type.arr

          elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

          val elt_less_equal : + Symbol.Shape.Type.arr

          elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

          val elt_greater_equal : + Symbol.Shape.Type.arr

          elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

          val elt_equal_scalar : + Symbol.Shape.Type.arr

          elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

          val elt_not_equal_scalar : + Symbol.Shape.Type.arr

          elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

          val elt_less_scalar : + Symbol.Shape.Type.arr

          elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

          val elt_greater_scalar : + Symbol.Shape.Type.arr

          elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

          val elt_less_equal_scalar : + Symbol.Shape.Type.arr

          elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

          TODO

          val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

          elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

          TODO

          val conv1d : + Symbol.Shape.Type.arr

          elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

          val conv2d : + Symbol.Shape.Type.arr

          conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

          • padding specifies the padding strategy (default is "valid").
          • strides specifies the stride length. Returns a new array with the result of the convolution.
          val conv3d : + Symbol.Shape.Type.arr

          conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

          • padding specifies the padding strategy (default is "valid").
          • strides specifies the stride length. Returns a new array with the result of the convolution.
          val transpose_conv1d : + Symbol.Shape.Type.arr

          conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

          • padding specifies the padding strategy (default is "valid").
          • strides specifies the stride length. Returns a new array with the result of the convolution.
          val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

          TODO

          val transpose_conv2d : + Symbol.Shape.Type.arr

          transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

          • padding specifies the padding strategy (default is "valid").
          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
          val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

          TODO

          val transpose_conv3d : + Symbol.Shape.Type.arr

          transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

          • padding specifies the padding strategy (default is "valid").
          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
          val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

          TODO

          val dilated_conv1d : + Symbol.Shape.Type.arr

          transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

          • padding specifies the padding strategy (default is "valid").
          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
          val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val dilated_conv2d : + Symbol.Shape.Type.arr

          dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

          • padding specifies the padding strategy (default is "valid").
          • strides specifies the stride length.
          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
          val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val dilated_conv3d : + Symbol.Shape.Type.arr

          dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

          • padding specifies the padding strategy (default is "valid").
          • strides specifies the stride length.
          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
          val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val max_pool1d : + Symbol.Shape.Type.arr

          dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

          • padding specifies the padding strategy (default is "valid").
          • strides specifies the stride length.
          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
          val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val max_pool2d : + Symbol.Shape.Type.arr

          max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

          • padding specifies the padding strategy (default is "valid").
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length. Returns a new array with the result of the max pooling.
          val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val max_pool3d : + Symbol.Shape.Type.arr

          max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

          • padding specifies the padding strategy (default is "valid").
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length. Returns a new array with the result of the max pooling.
          val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val avg_pool1d : + Symbol.Shape.Type.arr

          max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

          • padding specifies the padding strategy (default is "valid").
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length. Returns a new array with the result of the max pooling.
          val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val avg_pool2d : + Symbol.Shape.Type.arr

          avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

          • padding specifies the padding strategy (default is "valid").
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length. Returns a new array with the result of the average pooling.
          val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val avg_pool3d : + Symbol.Shape.Type.arr

          avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

          • padding specifies the padding strategy (default is "valid").
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length. Returns a new array with the result of the average pooling.
          val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          TODO

          val conv1d_backward_input : + Symbol.Shape.Type.arr

          avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

          • padding specifies the padding strategy (default is "valid").
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length. Returns a new array with the result of the average pooling.
          val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          upsampling2d input size performs a 2-dimensional upsampling on the input array.

          • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

          TODO

          val conv1d_backward_kernel : + Symbol.Shape.Type.arr

          conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

          • input is the original input array.
          • kernel is the convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
          val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val conv2d_backward_input : + Symbol.Shape.Type.arr

          conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

          • input is the original input array.
          • kernel is the convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

          TODO

          val conv2d_backward_kernel : + Symbol.Shape.Type.arr

          conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

          • input is the original input array.
          • kernel is the convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
          val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val conv3d_backward_input : + Symbol.Shape.Type.arr

          conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

          • input is the original input array.
          • kernel is the convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

          TODO

          val conv3d_backward_kernel : + Symbol.Shape.Type.arr

          conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

          • input is the original input array.
          • kernel is the convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
          val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

          conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

          • input is the original input array.
          • kernel is the convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
          val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

          transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

          • input is the original input array.
          • kernel is the transposed convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
          val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

          transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

          • input is the original input array.
          • kernel is the transposed convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
          val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

          transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

          • input is the original input array.
          • kernel is the transposed convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
          val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

          transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

          • input is the original input array.
          • kernel is the transposed convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
          val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

          transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

          • input is the original input array.
          • kernel is the transposed convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
          val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

          transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

          • input is the original input array.
          • kernel is the transposed convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
          val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

          dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

          • input is the original input array.
          • kernel is the dilated convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • dilations specifies the dilation rate.
          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
          val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

          dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

          • input is the original input array.
          • kernel is the dilated convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • dilations specifies the dilation rate.
          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
          val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

          dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

          • input is the original input array.
          • kernel is the dilated convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • dilations specifies the dilation rate.
          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
          val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

          dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

          • input is the original input array.
          • kernel is the dilated convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • dilations specifies the dilation rate.
          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
          val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

          dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

          • input is the original input array.
          • kernel is the dilated convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • dilations specifies the dilation rate.
          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
          val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val max_pool1d_backward : + Symbol.Shape.Type.arr

          dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

          • input is the original input array.
          • kernel is the dilated convolutional kernel used during the forward pass.
          • strides specifies the stride length.
          • dilations specifies the dilation rate.
          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
          val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val max_pool2d_backward : + Symbol.Shape.Type.arr

          max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

          • padding specifies the padding strategy used during the forward pass.
          • input is the original input array.
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
          val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val max_pool3d_backward : + Symbol.Shape.Type.arr

          max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

          • padding specifies the padding strategy used during the forward pass.
          • input is the original input array.
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
          val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val avg_pool1d_backward : + Symbol.Shape.Type.arr

          max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

          • padding specifies the padding strategy used during the forward pass.
          • input is the original input array.
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
          val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val avg_pool2d_backward : + Symbol.Shape.Type.arr

          avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

          • padding specifies the padding strategy used during the forward pass.
          • input is the original input array.
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
          val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val avg_pool3d_backward : + Symbol.Shape.Type.arr

          avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

          • padding specifies the padding strategy used during the forward pass.
          • input is the original input array.
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
          val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val upsampling2d_backward : + Symbol.Shape.Type.arr

          avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

          • padding specifies the padding strategy used during the forward pass.
          • input is the original input array.
          • pool_size specifies the size of the pooling window.
          • strides specifies the stride length.
          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
          val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val row_num : Symbol.Shape.Type.arr -> int

          TODO

          val col_num : Symbol.Shape.Type.arr -> int

          TODO

          val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          TODO

          val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

          TODO

          val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

          TODO

          TODO

          upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

          • input is the original input array.
          • size specifies the upsampling factors for each dimension.
          • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
          val row_num : Symbol.Shape.Type.arr -> int

          row_num arr returns the number of rows in the array arr.

          val col_num : Symbol.Shape.Type.arr -> int

          col_num arr returns the number of columns in the array arr.

          row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

          val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

          rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

          val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

          copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

          val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

          copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

          diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

          trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

          val transpose : + Symbol.Shape.Type.arr

          dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

          val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

          TODO

          val to_rows : Symbol.Shape.Type.arr -> 'a array

          TODO

          TODO

          val to_cols : Symbol.Shape.Type.arr -> 'a array

          TODO

          TODO

          val of_array : + Symbol.Shape.Type.arr

          transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

          val to_rows : Symbol.Shape.Type.arr -> 'a array

          to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

          of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

          val to_cols : Symbol.Shape.Type.arr -> 'a array

          to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

          of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

          val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

          TODO

          val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

          TODO

          val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

          TODO

          Scalar functions
          module Scalar : sig ... end
          module Mat : sig ... end
          module Linalg : sig ... end
          + Symbol.Shape.Type.arr

          of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

          val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

          of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

          val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

          to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

          Scalar functions
          module Scalar : sig ... end
          module Mat : sig ... end
          module Linalg : sig ... end
          diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/index.html index 29b0797d6..0bfe0f365 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser)

          Module Graph.Optimiser

          Core functions
          val estimate_complexity : 'a Owl_graph.node array -> int * int

          TODO

          val optimise_nodes : +Optimiser (owl-base.Owl_computation_engine.Flatten.Engine.Graph.Optimiser)

          Module Graph.Optimiser

          Core functions
          val estimate_complexity : 'a Owl_graph.node array -> int * int

          TODO

          val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

          TODO

          diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/index.html index d7532078c..22380e708 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_computation_engine.Flatten.Engine.Graph)

          Module Engine.Graph

          Type definition
          type graph

          TODO

          Core functions
          val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

          TODO

          val graph_to_dot : graph -> string

          TODO

          val graph_to_trace : graph -> string

          TODO

          val save_graph : 'a -> string -> unit

          TODO

          val load_graph : string -> 'a * 'b

          TODO

          val collect_rvs : +Graph (owl-base.Owl_computation_engine.Flatten.Engine.Graph)

          Module Engine.Graph

          Type definition
          type graph

          TODO

          Core functions
          val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

          TODO

          val graph_to_dot : graph -> string

          TODO

          val graph_to_trace : graph -> string

          TODO

          val save_graph : 'a -> string -> unit

          TODO

          val load_graph : string -> 'a * 'b

          TODO

          val invalidate_rvs : graph -> unit

          TODO

          val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/index.html b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/index.html index ce6bffcfe..d99a285e3 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/argument-1-Engine/index.html @@ -1,2 +1,2 @@ -Engine (owl-base.Owl_computation_engine.Flatten.Engine)

          Parameter Flatten.Engine

          Core evaluation functions of the engine

          TODO

          TODO

          val eval_graph : Graph.graph -> unit

          TODO

          +Engine (owl-base.Owl_computation_engine.Flatten.Engine)

          Parameter Flatten.Engine

          Core evaluation functions of the engine

          TODO

          TODO

          val eval_graph : Graph.graph -> unit

          TODO

          diff --git a/docs/owl-base/Owl_computation_engine/Flatten/index.html b/docs/owl-base/Owl_computation_engine/Flatten/index.html index de0f91be6..e66fc75cb 100644 --- a/docs/owl-base/Owl_computation_engine/Flatten/index.html +++ b/docs/owl-base/Owl_computation_engine/Flatten/index.html @@ -1,5 +1,5 @@ -Flatten (owl-base.Owl_computation_engine.Flatten)

          Module Owl_computation_engine.Flatten

          Parameters

          Signature

          include module type of struct include Engine end
          module Graph = Engine.Graph
          Core evaluation functions of the engine

          TODO

          TODO

          val eval_graph : Graph.graph -> unit

          TODO

          include module type of struct include Graph end
          module Optimiser = Graph.Optimiser
          type graph = Engine.Graph.graph
          val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string
          val graph_to_dot : graph -> string
          val graph_to_trace : graph -> string
          val save_graph : 'a -> string -> unit
          val load_graph : string -> 'a * 'b
          val collect_rvs : +Flatten (owl-base.Owl_computation_engine.Flatten)

          Module Owl_computation_engine.Flatten

          Parameters

          Signature

          include module type of struct include Engine end
          module Graph = Engine.Graph
          Core evaluation functions of the engine

          TODO

          TODO

          val eval_graph : Graph.graph -> unit

          TODO

          include module type of struct include Graph end
          module Optimiser = Graph.Optimiser
          type graph = Engine.Graph.graph
          val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string
          val graph_to_dot : graph -> string
          val graph_to_trace : graph -> string
          val save_graph : 'a -> string -> unit
          val load_graph : string -> 'a * 'b
          val invalidate_rvs : graph -> unit
          val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Linalg/index.html index eb134ec76..29e65cabe 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Linalg)

          Module Operator.Linalg

          val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr
          val svd : +Linalg (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Linalg)

          Module Operator.Linalg

          val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr
          val sylvester : diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Mat/index.html index 4f7fe822a..a86a1944c 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Mat)

          Module Operator.Mat

          +Mat (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Mat)

          Module Operator.Mat

          diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Scalar/index.html index fba0c6a43..0f79919eb 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Scalar/index.html @@ -1,5 +1,5 @@ -Scalar (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Scalar)

          Module Operator.Scalar

          val add : +Scalar (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Scalar)

          Module Operator.Scalar

          val sub : diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 025dfd187..9b4c88f99 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

          Module A.Linalg

          val inv : arr -> arr
          val logdet : arr -> elt
          val chol : ?upper:bool -> arr -> arr
          val svd : ?thin:bool -> arr -> arr * arr * arr
          val qr : arr -> arr * arr
          val lq : arr -> arr * arr
          val sylvester : arr -> arr -> arr -> arr
          val lyapunov : arr -> arr -> arr
          val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

          Module A.Linalg

          val inv : arr -> arr
          val logdet : arr -> elt
          val chol : ?upper:bool -> arr -> arr
          val svd : ?thin:bool -> arr -> arr * arr * arr
          val qr : arr -> arr * arr
          val lq : arr -> arr * arr
          val sylvester : arr -> arr -> arr -> arr
          val lyapunov : arr -> arr -> arr
          val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 19898a24a..d99f54a13 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

          Module A.Mat

          val diagm : ?k:int -> arr -> arr
          val triu : ?k:int -> arr -> arr
          val tril : ?k:int -> arr -> arr
          val eye : int -> arr
          +Mat (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

          Module A.Mat

          val diagm : ?k:int -> arr -> arr
          val triu : ?k:int -> arr -> arr
          val tril : ?k:int -> arr -> arr
          val eye : int -> arr
          diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index 1c54e41f4..e51430166 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

          Module A.Scalar

          val add : elt -> elt -> elt
          val sub : elt -> elt -> elt
          val mul : elt -> elt -> elt
          val div : elt -> elt -> elt
          val pow : elt -> elt -> elt
          val atan2 : elt -> elt -> elt
          val abs : elt -> elt
          val neg : elt -> elt
          val sqr : elt -> elt
          val sqrt : elt -> elt
          val exp : elt -> elt
          val log : elt -> elt
          val log2 : elt -> elt
          val log10 : elt -> elt
          val signum : elt -> elt
          val floor : elt -> elt
          val ceil : elt -> elt
          val round : elt -> elt
          val sin : elt -> elt
          val cos : elt -> elt
          val tan : elt -> elt
          val sinh : elt -> elt
          val cosh : elt -> elt
          val tanh : elt -> elt
          val asin : elt -> elt
          val acos : elt -> elt
          val atan : elt -> elt
          val asinh : elt -> elt
          val acosh : elt -> elt
          val atanh : elt -> elt
          val relu : elt -> elt
          val dawsn : elt -> elt
          val sigmoid : elt -> elt
          +Scalar (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

          Module A.Scalar

          val add : elt -> elt -> elt
          val sub : elt -> elt -> elt
          val mul : elt -> elt -> elt
          val div : elt -> elt -> elt
          val pow : elt -> elt -> elt
          val atan2 : elt -> elt -> elt
          val abs : elt -> elt
          val neg : elt -> elt
          val sqr : elt -> elt
          val sqrt : elt -> elt
          val exp : elt -> elt
          val log : elt -> elt
          val log2 : elt -> elt
          val log10 : elt -> elt
          val signum : elt -> elt
          val floor : elt -> elt
          val ceil : elt -> elt
          val round : elt -> elt
          val sin : elt -> elt
          val cos : elt -> elt
          val tan : elt -> elt
          val sinh : elt -> elt
          val cosh : elt -> elt
          val tanh : elt -> elt
          val asin : elt -> elt
          val acos : elt -> elt
          val atan : elt -> elt
          val asinh : elt -> elt
          val acosh : elt -> elt
          val atanh : elt -> elt
          val relu : elt -> elt
          val dawsn : elt -> elt
          val sigmoid : elt -> elt
          diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index 995a9e34b..c13a58332 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

          Module Device.A

          type arr = +A (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

          Module Device.A

          val empty : int array -> arr
          val zeros : int array -> arr
          val ones : int array -> arr
          val create : int array -> elt -> arr
          val sequential : ?a:elt -> ?step:elt -> int array -> arr
          val uniform : ?a:elt -> ?b:elt -> int array -> arr
          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
          val bernoulli : ?p:elt -> int array -> arr
          val init : int array -> (int -> elt) -> arr
          val init_nd : int array -> (int array -> elt) -> arr
          val shape : arr -> int array
          val numel : arr -> int
          val get : arr -> int array -> elt
          val set : arr -> int array -> elt -> unit
          val get_slice : int list list -> arr -> arr
          val set_slice : int list list -> arr -> arr -> unit
          val get_fancy : Owl_types_common.index list -> arr -> arr
          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
          val copy : arr -> arr
          val copy_ : out:arr -> arr -> unit
          val reset : arr -> unit
          val reshape : arr -> int array -> arr
          val reverse : arr -> arr
          val tile : arr -> int array -> arr
          val repeat : arr -> int array -> arr
          val concatenate : ?axis:int -> arr array -> arr
          val stack : ?axis:int -> arr array -> arr
          val split : ?axis:int -> int array -> arr -> arr array
          val expand : ?hi:bool -> arr -> int -> arr
          val squeeze : ?axis:int array -> arr -> arr
          val draw : ?axis:int -> arr -> int -> arr * int array
          val map : (elt -> elt) -> arr -> arr
          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
          val one_hot : int -> arr -> arr
          val pad : ?v:elt -> int list list -> arr -> arr
          val print : ?max_row:int -> diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index 66ba8cca9..200c333ca 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,4 +1,4 @@ -Device (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

          Module Type.Device

          module A : sig ... end
          type device = +Device (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

          Module Type.Device

          diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index 954284fa4..0f8087abb 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type)

          Module Shape.Type

          module Device : sig ... end
          type state = +Type (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape.Type)

          Module Shape.Type

          module Device : sig ... end
          and block = Owl_computation_optimiser.Make(Owl_computation_operator.Make(Owl_computation_symbol.Make(Owl_computation_shape.Make(Owl_computation_type.Make(Device))))).Operator.Symbol.Shape.Type.block = diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/index.html index 3a0f4dbd7..2e2e0cb89 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape)

          Module Symbol.Shape

          module Type : sig ... end
          val infer_shape : +Shape (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol.Shape)

          Module Symbol.Shape

          module Type : sig ... end
          val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array
          diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/index.html index 493d9c1c4..c12e4de2c 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol)

          Module Operator.Symbol

          module Shape : sig ... end
          val op_to_str : Shape.Type.op -> string
          val is_random_variable : Shape.Type.op -> bool
          val refnum : 'a Owl_graph.node -> int
          val node_shape : Shape.Type.attr Owl_graph.node -> int array
          val node_numel : Shape.Type.attr Owl_graph.node -> int
          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool
          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit
          val shape_to_str : int array option array -> string
          val node_to_str : Shape.Type.attr Owl_graph.node -> string
          val node_to_arr : Shape.Type.t -> Shape.Type.arr
          val arr_to_node : Shape.Type.arr -> Shape.Type.t
          val node_to_elt : Shape.Type.t -> Shape.Type.elt
          val elt_to_node : Shape.Type.elt -> Shape.Type.t
          val make_node : +Symbol (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator.Symbol)

          Module Operator.Symbol

          module Shape : sig ... end
          val op_to_str : Shape.Type.op -> string
          val is_random_variable : Shape.Type.op -> bool
          val refnum : 'a Owl_graph.node -> int
          val node_shape : Shape.Type.attr Owl_graph.node -> int array
          val node_numel : Shape.Type.attr Owl_graph.node -> int
          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool
          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit
          val shape_to_str : int array option array -> string
          val node_to_str : Shape.Type.attr Owl_graph.node -> string
          val node_to_arr : Shape.Type.t -> Shape.Type.arr
          val arr_to_node : Shape.Type.arr -> Shape.Type.t
          val node_to_elt : Shape.Type.t -> Shape.Type.elt
          val elt_to_node : Shape.Type.elt -> Shape.Type.t
          val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/index.html index 4cd4ed9b6..cd6c85906 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/Operator/index.html @@ -1,5 +1,5 @@ -Operator (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator)

          Module Optimiser.Operator

          module Symbol : sig ... end
          val empty : int array -> Symbol.Shape.Type.arr
          val zeros : int array -> Symbol.Shape.Type.arr
          val ones : int array -> Symbol.Shape.Type.arr
          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr
          val sequential : +Operator (owl-base.Owl_computation_engine.Make_Graph.Optimiser.Operator)

          Module Optimiser.Operator

          module Symbol : sig ... end
          val empty : int array -> Symbol.Shape.Type.arr
          val zeros : int array -> Symbol.Shape.Type.arr
          val ones : int array -> Symbol.Shape.Type.arr
          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr
          val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/index.html index 7008afd8d..7f42ab239 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_engine.Make_Graph.Optimiser)

          Module Make_Graph.Optimiser

          module Operator : sig ... end
          val estimate_complexity : 'a Owl_graph.node array -> int * int
          val optimise_nodes : +Optimiser (owl-base.Owl_computation_engine.Make_Graph.Optimiser)

          Module Make_Graph.Optimiser

          module Operator : sig ... end
          val estimate_complexity : 'a Owl_graph.node array -> int * int
          val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit
          diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Linalg/index.html index 9e681a3d2..9da4a6f12 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine.Make_Graph.Device.A.Linalg)

          Module A.Linalg

          val inv : arr -> arr
          val logdet : arr -> elt
          val chol : ?upper:bool -> arr -> arr
          val svd : ?thin:bool -> arr -> arr * arr * arr
          val qr : arr -> arr * arr
          val lq : arr -> arr * arr
          val sylvester : arr -> arr -> arr -> arr
          val lyapunov : arr -> arr -> arr
          val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine.Make_Graph.Device.A.Linalg)

          Module A.Linalg

          val inv : arr -> arr
          val logdet : arr -> elt
          val chol : ?upper:bool -> arr -> arr
          val svd : ?thin:bool -> arr -> arr * arr * arr
          val qr : arr -> arr * arr
          val lq : arr -> arr * arr
          val sylvester : arr -> arr -> arr -> arr
          val lyapunov : arr -> arr -> arr
          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Mat/index.html index 872e5d3bf..0c1c2811a 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine.Make_Graph.Device.A.Mat)

          Module A.Mat

          val diagm : ?k:int -> arr -> arr
          val triu : ?k:int -> arr -> arr
          val tril : ?k:int -> arr -> arr
          val eye : int -> arr
          +Mat (owl-base.Owl_computation_engine.Make_Graph.Device.A.Mat)

          Module A.Mat

          val diagm : ?k:int -> arr -> arr
          val triu : ?k:int -> arr -> arr
          val tril : ?k:int -> arr -> arr
          val eye : int -> arr
          diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Scalar/index.html index 498188a2c..87ee8c0ca 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine.Make_Graph.Device.A.Scalar)

          Module A.Scalar

          val add : elt -> elt -> elt
          val sub : elt -> elt -> elt
          val mul : elt -> elt -> elt
          val div : elt -> elt -> elt
          val pow : elt -> elt -> elt
          val atan2 : elt -> elt -> elt
          val abs : elt -> elt
          val neg : elt -> elt
          val sqr : elt -> elt
          val sqrt : elt -> elt
          val exp : elt -> elt
          val log : elt -> elt
          val log2 : elt -> elt
          val log10 : elt -> elt
          val signum : elt -> elt
          val floor : elt -> elt
          val ceil : elt -> elt
          val round : elt -> elt
          val sin : elt -> elt
          val cos : elt -> elt
          val tan : elt -> elt
          val sinh : elt -> elt
          val cosh : elt -> elt
          val tanh : elt -> elt
          val asin : elt -> elt
          val acos : elt -> elt
          val atan : elt -> elt
          val asinh : elt -> elt
          val acosh : elt -> elt
          val atanh : elt -> elt
          val relu : elt -> elt
          val dawsn : elt -> elt
          val sigmoid : elt -> elt
          +Scalar (owl-base.Owl_computation_engine.Make_Graph.Device.A.Scalar)

          Module A.Scalar

          val add : elt -> elt -> elt
          val sub : elt -> elt -> elt
          val mul : elt -> elt -> elt
          val div : elt -> elt -> elt
          val pow : elt -> elt -> elt
          val atan2 : elt -> elt -> elt
          val abs : elt -> elt
          val neg : elt -> elt
          val sqr : elt -> elt
          val sqrt : elt -> elt
          val exp : elt -> elt
          val log : elt -> elt
          val log2 : elt -> elt
          val log10 : elt -> elt
          val signum : elt -> elt
          val floor : elt -> elt
          val ceil : elt -> elt
          val round : elt -> elt
          val sin : elt -> elt
          val cos : elt -> elt
          val tan : elt -> elt
          val sinh : elt -> elt
          val cosh : elt -> elt
          val tanh : elt -> elt
          val asin : elt -> elt
          val acos : elt -> elt
          val atan : elt -> elt
          val asinh : elt -> elt
          val acosh : elt -> elt
          val atanh : elt -> elt
          val relu : elt -> elt
          val dawsn : elt -> elt
          val sigmoid : elt -> elt
          diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/index.html index 5f2cf0f1a..bb991605e 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine.Make_Graph.Device.A)

          Module Device.A

          include Owl_types_ndarray_algodiff.Sig
          include Owl_types_ndarray_eltcmp.Sig
          include Owl_types_ndarray_basic.Sig
          type arr
          type elt
          val empty : int array -> arr
          val zeros : int array -> arr
          val ones : int array -> arr
          val create : int array -> elt -> arr
          val sequential : ?a:elt -> ?step:elt -> int array -> arr
          val uniform : ?a:elt -> ?b:elt -> int array -> arr
          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
          val bernoulli : ?p:elt -> int array -> arr
          val init : int array -> (int -> elt) -> arr
          val init_nd : int array -> (int array -> elt) -> arr
          val shape : arr -> int array
          val numel : arr -> int
          val get : arr -> int array -> elt
          val set : arr -> int array -> elt -> unit
          val get_slice : int list list -> arr -> arr
          val set_slice : int list list -> arr -> arr -> unit
          val get_fancy : Owl_types_common.index list -> arr -> arr
          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
          val copy : arr -> arr
          val copy_ : out:arr -> arr -> unit
          val reset : arr -> unit
          val reshape : arr -> int array -> arr
          val reverse : arr -> arr
          val tile : arr -> int array -> arr
          val repeat : arr -> int array -> arr
          val concatenate : ?axis:int -> arr array -> arr
          val stack : ?axis:int -> arr array -> arr
          val split : ?axis:int -> int array -> arr -> arr array
          val expand : ?hi:bool -> arr -> int -> arr
          val squeeze : ?axis:int array -> arr -> arr
          val draw : ?axis:int -> arr -> int -> arr * int array
          val map : (elt -> elt) -> arr -> arr
          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
          val one_hot : int -> arr -> arr
          val pad : ?v:elt -> int list list -> arr -> arr
          val print : +A (owl-base.Owl_computation_engine.Make_Graph.Device.A)

          Module Device.A

          include Owl_types_ndarray_algodiff.Sig
          include Owl_types_ndarray_eltcmp.Sig
          include Owl_types_ndarray_basic.Sig
          type arr
          type elt
          val empty : int array -> arr
          val zeros : int array -> arr
          val ones : int array -> arr
          val create : int array -> elt -> arr
          val sequential : ?a:elt -> ?step:elt -> int array -> arr
          val uniform : ?a:elt -> ?b:elt -> int array -> arr
          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
          val bernoulli : ?p:elt -> int array -> arr
          val init : int array -> (int -> elt) -> arr
          val init_nd : int array -> (int array -> elt) -> arr
          val shape : arr -> int array
          val numel : arr -> int
          val get : arr -> int array -> elt
          val set : arr -> int array -> elt -> unit
          val get_slice : int list list -> arr -> arr
          val set_slice : int list list -> arr -> arr -> unit
          val get_fancy : Owl_types_common.index list -> arr -> arr
          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
          val copy : arr -> arr
          val copy_ : out:arr -> arr -> unit
          val reset : arr -> unit
          val reshape : arr -> int array -> arr
          val reverse : arr -> arr
          val tile : arr -> int array -> arr
          val repeat : arr -> int array -> arr
          val concatenate : ?axis:int -> arr array -> arr
          val stack : ?axis:int -> arr array -> arr
          val split : ?axis:int -> int array -> arr -> arr array
          val expand : ?hi:bool -> arr -> int -> arr
          val squeeze : ?axis:int array -> arr -> arr
          val draw : ?axis:int -> arr -> int -> arr * int array
          val map : (elt -> elt) -> arr -> arr
          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
          val one_hot : int -> arr -> arr
          val pad : ?v:elt -> int list list -> arr -> arr
          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/index.html index d28e6315e..b97fb1fcd 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/argument-1-Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine.Make_Graph.Device)

          Parameter Make_Graph.Device

          Type definition
          type device

          TODO

          type value

          TODO

          Core functions
          val make_device : unit -> device

          TODO

          val arr_to_value : A.arr -> value

          TODO

          val value_to_arr : value -> A.arr

          TODO

          val elt_to_value : A.elt -> value

          TODO

          val value_to_elt : value -> A.elt

          TODO

          val value_to_float : value -> float

          TODO

          val is_arr : value -> bool

          TODO

          val is_elt : value -> bool

          TODO

          +Device (owl-base.Owl_computation_engine.Make_Graph.Device)

          Parameter Make_Graph.Device

          Type definition
          type device

          TODO

          type value

          TODO

          Core functions
          val make_device : unit -> device

          TODO

          val arr_to_value : A.arr -> value

          TODO

          val value_to_arr : value -> A.arr

          TODO

          val elt_to_value : A.elt -> value

          TODO

          val value_to_elt : value -> A.elt

          TODO

          val value_to_float : value -> float

          TODO

          val is_arr : value -> bool

          TODO

          val is_elt : value -> bool

          TODO

          diff --git a/docs/owl-base/Owl_computation_engine/Make_Graph/index.html b/docs/owl-base/Owl_computation_engine/Make_Graph/index.html index 38d7edd3a..579035c04 100644 --- a/docs/owl-base/Owl_computation_engine/Make_Graph/index.html +++ b/docs/owl-base/Owl_computation_engine/Make_Graph/index.html @@ -1,5 +1,5 @@ -Make_Graph (owl-base.Owl_computation_engine.Make_Graph)

          Module Owl_computation_engine.Make_Graph

          Parameters

          Signature

          include sig ... end
          module Optimiser : sig ... end
          type graph = +Make_Graph (owl-base.Owl_computation_engine.Make_Graph)

          Module Owl_computation_engine.Make_Graph

          Parameters

          Signature

          include sig ... end
          module Optimiser : sig ... end
          type graph = Owl_computation_graph.Make(Owl_computation_optimiser.Make(Owl_computation_operator.Make(Owl_computation_symbol.Make(Owl_computation_shape.Make(Owl_computation_type.Make(Device)))))).graph = {
          1. mutable name : string;
          2. mutable input : Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array;
          3. mutable output : Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array;
          4. mutable iopair : (Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node * Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node) diff --git a/docs/owl-base/Owl_computation_engine/index.html b/docs/owl-base/Owl_computation_engine/index.html index a033e0b89..a33de1b14 100644 --- a/docs/owl-base/Owl_computation_engine/index.html +++ b/docs/owl-base/Owl_computation_engine/index.html @@ -1,2 +1,2 @@ -Owl_computation_engine (owl-base.Owl_computation_engine)

            Module Owl_computation_engine

            This functor takes a device as its input, then it generates the computation graph module without flattening the module hierarchy.

            This functor takes an engine as its input, flattens all its embedded modules into the same level. Therefore the generated module has all the functions sit at the top level, then can be passed to other functors like Algodiff.

            +Owl_computation_engine (owl-base.Owl_computation_engine)

            Module Owl_computation_engine

            This functor takes a device as its input, then it generates the computation graph module without flattening the module hierarchy.

            This functor takes an engine as its input, flattens all its embedded modules into the same level. Therefore the generated module has all the functions sit at the top level, then can be passed to other functors like Algodiff.

            diff --git a/docs/owl-base/Owl_computation_engine_sig/index.html b/docs/owl-base/Owl_computation_engine_sig/index.html index 88ef671c7..9add7f8e9 100644 --- a/docs/owl-base/Owl_computation_engine_sig/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/index.html @@ -1,2 +1,2 @@ -Owl_computation_engine_sig (owl-base.Owl_computation_engine_sig)

            Module Owl_computation_engine_sig

            module type Make_Graph_Sig = sig ... end
            module type Flatten_Sig = sig ... end
            +Owl_computation_engine_sig (owl-base.Owl_computation_engine_sig)

            Module Owl_computation_engine_sig

            module type Make_Graph_Sig = sig ... end
            module type Flatten_Sig = sig ... end
            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Linalg/index.html index 7d1a10a1f..f2ace234b 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.A.Linalg)

            Module A.Linalg

            val inv : arr -> arr
            val logdet : arr -> elt
            val chol : ?upper:bool -> arr -> arr
            val svd : ?thin:bool -> arr -> arr * arr * arr
            val qr : arr -> arr * arr
            val lq : arr -> arr * arr
            val sylvester : arr -> arr -> arr -> arr
            val lyapunov : arr -> arr -> arr
            val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.A.Linalg)

            Module A.Linalg

            val inv : arr -> arr
            val logdet : arr -> elt
            val chol : ?upper:bool -> arr -> arr
            val svd : ?thin:bool -> arr -> arr * arr * arr
            val qr : arr -> arr * arr
            val lq : arr -> arr * arr
            val sylvester : arr -> arr -> arr -> arr
            val lyapunov : arr -> arr -> arr
            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Mat/index.html index bc8cd5292..971043379 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.A.Mat)

            Module A.Mat

            val diagm : ?k:int -> arr -> arr
            val triu : ?k:int -> arr -> arr
            val tril : ?k:int -> arr -> arr
            val eye : int -> arr
            +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.A.Mat)

            Module A.Mat

            val diagm : ?k:int -> arr -> arr
            val triu : ?k:int -> arr -> arr
            val tril : ?k:int -> arr -> arr
            val eye : int -> arr
            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Scalar/index.html index 7d2ab278f..1c7464f6c 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.A.Scalar)

            Module A.Scalar

            val add : elt -> elt -> elt
            val sub : elt -> elt -> elt
            val mul : elt -> elt -> elt
            val div : elt -> elt -> elt
            val pow : elt -> elt -> elt
            val atan2 : elt -> elt -> elt
            val abs : elt -> elt
            val neg : elt -> elt
            val sqr : elt -> elt
            val sqrt : elt -> elt
            val exp : elt -> elt
            val log : elt -> elt
            val log2 : elt -> elt
            val log10 : elt -> elt
            val signum : elt -> elt
            val floor : elt -> elt
            val ceil : elt -> elt
            val round : elt -> elt
            val sin : elt -> elt
            val cos : elt -> elt
            val tan : elt -> elt
            val sinh : elt -> elt
            val cosh : elt -> elt
            val tanh : elt -> elt
            val asin : elt -> elt
            val acos : elt -> elt
            val atan : elt -> elt
            val asinh : elt -> elt
            val acosh : elt -> elt
            val atanh : elt -> elt
            val relu : elt -> elt
            val dawsn : elt -> elt
            val sigmoid : elt -> elt
            +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.A.Scalar)

            Module A.Scalar

            val add : elt -> elt -> elt
            val sub : elt -> elt -> elt
            val mul : elt -> elt -> elt
            val div : elt -> elt -> elt
            val pow : elt -> elt -> elt
            val atan2 : elt -> elt -> elt
            val abs : elt -> elt
            val neg : elt -> elt
            val sqr : elt -> elt
            val sqrt : elt -> elt
            val exp : elt -> elt
            val log : elt -> elt
            val log2 : elt -> elt
            val log10 : elt -> elt
            val signum : elt -> elt
            val floor : elt -> elt
            val ceil : elt -> elt
            val round : elt -> elt
            val sin : elt -> elt
            val cos : elt -> elt
            val tan : elt -> elt
            val sinh : elt -> elt
            val cosh : elt -> elt
            val tanh : elt -> elt
            val asin : elt -> elt
            val acos : elt -> elt
            val atan : elt -> elt
            val asinh : elt -> elt
            val acosh : elt -> elt
            val atanh : elt -> elt
            val relu : elt -> elt
            val dawsn : elt -> elt
            val sigmoid : elt -> elt
            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/index.html index 20d56589e..55db962ab 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine_sig.Flatten_Sig.A)

            Module Flatten_Sig.A

            include Owl_types_ndarray_algodiff.Sig
            include Owl_types_ndarray_eltcmp.Sig
            include Owl_types_ndarray_basic.Sig
            type arr
            type elt
            val empty : int array -> arr
            val zeros : int array -> arr
            val ones : int array -> arr
            val create : int array -> elt -> arr
            val sequential : ?a:elt -> ?step:elt -> int array -> arr
            val uniform : ?a:elt -> ?b:elt -> int array -> arr
            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
            val bernoulli : ?p:elt -> int array -> arr
            val init : int array -> (int -> elt) -> arr
            val init_nd : int array -> (int array -> elt) -> arr
            val shape : arr -> int array
            val numel : arr -> int
            val get : arr -> int array -> elt
            val set : arr -> int array -> elt -> unit
            val get_slice : int list list -> arr -> arr
            val set_slice : int list list -> arr -> arr -> unit
            val get_fancy : Owl_types_common.index list -> arr -> arr
            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
            val copy : arr -> arr
            val copy_ : out:arr -> arr -> unit
            val reset : arr -> unit
            val reshape : arr -> int array -> arr
            val reverse : arr -> arr
            val tile : arr -> int array -> arr
            val repeat : arr -> int array -> arr
            val concatenate : ?axis:int -> arr array -> arr
            val stack : ?axis:int -> arr array -> arr
            val split : ?axis:int -> int array -> arr -> arr array
            val expand : ?hi:bool -> arr -> int -> arr
            val squeeze : ?axis:int array -> arr -> arr
            val draw : ?axis:int -> arr -> int -> arr * int array
            val map : (elt -> elt) -> arr -> arr
            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
            val one_hot : int -> arr -> arr
            val pad : ?v:elt -> int list list -> arr -> arr
            val print : +A (owl-base.Owl_computation_engine_sig.Flatten_Sig.A)

            Module Flatten_Sig.A

            include Owl_types_ndarray_algodiff.Sig
            include Owl_types_ndarray_eltcmp.Sig
            include Owl_types_ndarray_basic.Sig
            type arr
            type elt
            val empty : int array -> arr
            val zeros : int array -> arr
            val ones : int array -> arr
            val create : int array -> elt -> arr
            val sequential : ?a:elt -> ?step:elt -> int array -> arr
            val uniform : ?a:elt -> ?b:elt -> int array -> arr
            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
            val bernoulli : ?p:elt -> int array -> arr
            val init : int array -> (int -> elt) -> arr
            val init_nd : int array -> (int array -> elt) -> arr
            val shape : arr -> int array
            val numel : arr -> int
            val get : arr -> int array -> elt
            val set : arr -> int array -> elt -> unit
            val get_slice : int list list -> arr -> arr
            val set_slice : int list list -> arr -> arr -> unit
            val get_fancy : Owl_types_common.index list -> arr -> arr
            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
            val copy : arr -> arr
            val copy_ : out:arr -> arr -> unit
            val reset : arr -> unit
            val reshape : arr -> int array -> arr
            val reverse : arr -> arr
            val tile : arr -> int array -> arr
            val repeat : arr -> int array -> arr
            val concatenate : ?axis:int -> arr array -> arr
            val stack : ?axis:int -> arr array -> arr
            val split : ?axis:int -> int array -> arr -> arr array
            val expand : ?hi:bool -> arr -> int -> arr
            val squeeze : ?axis:int array -> arr -> arr
            val draw : ?axis:int -> arr -> int -> arr * int array
            val map : (elt -> elt) -> arr -> arr
            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
            val one_hot : int -> arr -> arr
            val pad : ?v:elt -> int list list -> arr -> arr
            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Linalg/index.html index e6c23bd62..9c378a668 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device.A.Linalg)

            Module A.Linalg

            val inv : arr -> arr
            val logdet : arr -> elt
            val chol : ?upper:bool -> arr -> arr
            val svd : ?thin:bool -> arr -> arr * arr * arr
            val qr : arr -> arr * arr
            val lq : arr -> arr * arr
            val sylvester : arr -> arr -> arr -> arr
            val lyapunov : arr -> arr -> arr
            val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device.A.Linalg)

            Module A.Linalg

            val inv : arr -> arr
            val logdet : arr -> elt
            val chol : ?upper:bool -> arr -> arr
            val svd : ?thin:bool -> arr -> arr * arr * arr
            val qr : arr -> arr * arr
            val lq : arr -> arr * arr
            val sylvester : arr -> arr -> arr -> arr
            val lyapunov : arr -> arr -> arr
            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Mat/index.html index 4e05b6679..704b90228 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device.A.Mat)

            Module A.Mat

            val diagm : ?k:int -> arr -> arr
            val triu : ?k:int -> arr -> arr
            val tril : ?k:int -> arr -> arr
            val eye : int -> arr
            +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device.A.Mat)

            Module A.Mat

            val diagm : ?k:int -> arr -> arr
            val triu : ?k:int -> arr -> arr
            val tril : ?k:int -> arr -> arr
            val eye : int -> arr
            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Scalar/index.html index 1a416bbbd..848ff59c5 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device.A.Scalar)

            Module A.Scalar

            val add : elt -> elt -> elt
            val sub : elt -> elt -> elt
            val mul : elt -> elt -> elt
            val div : elt -> elt -> elt
            val pow : elt -> elt -> elt
            val atan2 : elt -> elt -> elt
            val abs : elt -> elt
            val neg : elt -> elt
            val sqr : elt -> elt
            val sqrt : elt -> elt
            val exp : elt -> elt
            val log : elt -> elt
            val log2 : elt -> elt
            val log10 : elt -> elt
            val signum : elt -> elt
            val floor : elt -> elt
            val ceil : elt -> elt
            val round : elt -> elt
            val sin : elt -> elt
            val cos : elt -> elt
            val tan : elt -> elt
            val sinh : elt -> elt
            val cosh : elt -> elt
            val tanh : elt -> elt
            val asin : elt -> elt
            val acos : elt -> elt
            val atan : elt -> elt
            val asinh : elt -> elt
            val acosh : elt -> elt
            val atanh : elt -> elt
            val relu : elt -> elt
            val dawsn : elt -> elt
            val sigmoid : elt -> elt
            +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device.A.Scalar)

            Module A.Scalar

            val add : elt -> elt -> elt
            val sub : elt -> elt -> elt
            val mul : elt -> elt -> elt
            val div : elt -> elt -> elt
            val pow : elt -> elt -> elt
            val atan2 : elt -> elt -> elt
            val abs : elt -> elt
            val neg : elt -> elt
            val sqr : elt -> elt
            val sqrt : elt -> elt
            val exp : elt -> elt
            val log : elt -> elt
            val log2 : elt -> elt
            val log10 : elt -> elt
            val signum : elt -> elt
            val floor : elt -> elt
            val ceil : elt -> elt
            val round : elt -> elt
            val sin : elt -> elt
            val cos : elt -> elt
            val tan : elt -> elt
            val sinh : elt -> elt
            val cosh : elt -> elt
            val tanh : elt -> elt
            val asin : elt -> elt
            val acos : elt -> elt
            val atan : elt -> elt
            val asinh : elt -> elt
            val acosh : elt -> elt
            val atanh : elt -> elt
            val relu : elt -> elt
            val dawsn : elt -> elt
            val sigmoid : elt -> elt
            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/index.html index f05e71d0f..f1b628fbb 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device.A)

            Module Device.A

            include Owl_types_ndarray_algodiff.Sig
            include Owl_types_ndarray_eltcmp.Sig
            include Owl_types_ndarray_basic.Sig
            type arr
            type elt
            val empty : int array -> arr
            val zeros : int array -> arr
            val ones : int array -> arr
            val create : int array -> elt -> arr
            val sequential : ?a:elt -> ?step:elt -> int array -> arr
            val uniform : ?a:elt -> ?b:elt -> int array -> arr
            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
            val bernoulli : ?p:elt -> int array -> arr
            val init : int array -> (int -> elt) -> arr
            val init_nd : int array -> (int array -> elt) -> arr
            val shape : arr -> int array
            val numel : arr -> int
            val get : arr -> int array -> elt
            val set : arr -> int array -> elt -> unit
            val get_slice : int list list -> arr -> arr
            val set_slice : int list list -> arr -> arr -> unit
            val get_fancy : Owl_types_common.index list -> arr -> arr
            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
            val copy : arr -> arr
            val copy_ : out:arr -> arr -> unit
            val reset : arr -> unit
            val reshape : arr -> int array -> arr
            val reverse : arr -> arr
            val tile : arr -> int array -> arr
            val repeat : arr -> int array -> arr
            val concatenate : ?axis:int -> arr array -> arr
            val stack : ?axis:int -> arr array -> arr
            val split : ?axis:int -> int array -> arr -> arr array
            val expand : ?hi:bool -> arr -> int -> arr
            val squeeze : ?axis:int array -> arr -> arr
            val draw : ?axis:int -> arr -> int -> arr * int array
            val map : (elt -> elt) -> arr -> arr
            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
            val one_hot : int -> arr -> arr
            val pad : ?v:elt -> int list list -> arr -> arr
            val print : +A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device.A)

            Module Device.A

            include Owl_types_ndarray_algodiff.Sig
            include Owl_types_ndarray_eltcmp.Sig
            include Owl_types_ndarray_basic.Sig
            type arr
            type elt
            val empty : int array -> arr
            val zeros : int array -> arr
            val ones : int array -> arr
            val create : int array -> elt -> arr
            val sequential : ?a:elt -> ?step:elt -> int array -> arr
            val uniform : ?a:elt -> ?b:elt -> int array -> arr
            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
            val bernoulli : ?p:elt -> int array -> arr
            val init : int array -> (int -> elt) -> arr
            val init_nd : int array -> (int array -> elt) -> arr
            val shape : arr -> int array
            val numel : arr -> int
            val get : arr -> int array -> elt
            val set : arr -> int array -> elt -> unit
            val get_slice : int list list -> arr -> arr
            val set_slice : int list list -> arr -> arr -> unit
            val get_fancy : Owl_types_common.index list -> arr -> arr
            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
            val copy : arr -> arr
            val copy_ : out:arr -> arr -> unit
            val reset : arr -> unit
            val reshape : arr -> int array -> arr
            val reverse : arr -> arr
            val tile : arr -> int array -> arr
            val repeat : arr -> int array -> arr
            val concatenate : ?axis:int -> arr array -> arr
            val stack : ?axis:int -> arr array -> arr
            val split : ?axis:int -> int array -> arr -> arr array
            val expand : ?hi:bool -> arr -> int -> arr
            val squeeze : ?axis:int array -> arr -> arr
            val draw : ?axis:int -> arr -> int -> arr * int array
            val map : (elt -> elt) -> arr -> arr
            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
            val one_hot : int -> arr -> arr
            val pad : ?v:elt -> int list list -> arr -> arr
            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/index.html index 0d5ab8247..5efb3e620 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device)

            Module Flatten_Sig.Device

            Type definition
            type device

            TODO

            type value

            TODO

            Core functions
            val make_device : unit -> device

            TODO

            val arr_to_value : A.arr -> value

            TODO

            val value_to_arr : value -> A.arr

            TODO

            val elt_to_value : A.elt -> value

            TODO

            val value_to_elt : value -> A.elt

            TODO

            val value_to_float : value -> float

            TODO

            val is_arr : value -> bool

            TODO

            val is_elt : value -> bool

            TODO

            +Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Device)

            Module Flatten_Sig.Device

            Type definition
            type device

            TODO

            type value

            TODO

            Core functions
            val make_device : unit -> device

            TODO

            val arr_to_value : A.arr -> value

            TODO

            val value_to_arr : value -> A.arr

            TODO

            val elt_to_value : A.elt -> value

            TODO

            val value_to_elt : value -> A.elt

            TODO

            val value_to_float : value -> float

            TODO

            val is_arr : value -> bool

            TODO

            val is_elt : value -> bool

            TODO

            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Linalg/index.html index 87012916c..301b683a2 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Linalg)

            Module Operator.Linalg

            val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

            TODO

            val svd : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Linalg)

            Module Operator.Linalg

            inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

            logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

            val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

            chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

            • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

            qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

            lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

            svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

            • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
            val lyapunov : + Symbol.Shape.Type.arr

            sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

            val discrete_lyapunov : + Symbol.Shape.Type.arr

            lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

            TODO

            val linsolve : + Symbol.Shape.Type.arr

            discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

            • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
            val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

            TODO

            linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

            • trans specifies whether to transpose the matrix A.
            • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

            care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

            • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
            + Symbol.Shape.Type.arr

            dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

            • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Mat/index.html index 4ac1133f5..a47d50863 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Mat)

            Module Operator.Mat

            val eye : int -> Symbol.Shape.Type.arr

            TODO

            TODO

            TODO

            TODO

            +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Mat)

            Module Operator.Mat

            val eye : int -> Symbol.Shape.Type.arr

            eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

            diagm ?k v creates a diagonal matrix from the array v.

            • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

            triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

            tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Scalar/index.html index c4ffb57b4..045c2c57d 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Scalar)

            Module Operator.Scalar

            val add : +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Scalar)

            Module Operator.Scalar

            add a b returns the sum of the scalars a and b.

            sub a b returns the difference of the scalars a and b.

            mul a b returns the product of the scalars a and b.

            div a b returns the quotient of the scalars a and b.

            val atan2 : + Symbol.Shape.Type.elt

            pow a b returns the scalar a raised to the power of b.

            + Symbol.Shape.Type.elt

            atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

            abs a returns the absolute value of the scalar a.

            neg a returns the negation of the scalar a.

            sqr a returns the square of the scalar a.

            sqrt a returns the square root of the scalar a.

            exp a returns the exponential of the scalar a.

            log a returns the natural logarithm of the scalar a.

            log2 a returns the base-2 logarithm of the scalar a.

            log10 a returns the base-10 logarithm of the scalar a.

            signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

            floor a returns the greatest integer less than or equal to the scalar a.

            ceil a returns the smallest integer greater than or equal to the scalar a.

            round a returns the nearest integer to the scalar a.

            sin a returns the sine of the scalar a.

            cos a returns the cosine of the scalar a.

            tan a returns the tangent of the scalar a.

            sinh a returns the hyperbolic sine of the scalar a.

            cosh a returns the hyperbolic cosine of the scalar a.

            tanh a returns the hyperbolic tangent of the scalar a.

            asin a returns the arcsine of the scalar a.

            acos a returns the arccosine of the scalar a.

            atan a returns the arctangent of the scalar a.

            asinh a returns the inverse hyperbolic sine of the scalar a.

            acosh a returns the inverse hyperbolic cosine of the scalar a.

            atanh a returns the inverse hyperbolic tangent of the scalar a.

            relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

            dawsn a returns Dawson's function of the scalar a.

            sigmoid a returns the sigmoid function of the scalar a.

            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 598f2aa66..0c222191c 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

            Module A.Linalg

            val inv : arr -> arr
            val logdet : arr -> elt
            val chol : ?upper:bool -> arr -> arr
            val svd : ?thin:bool -> arr -> arr * arr * arr
            val qr : arr -> arr * arr
            val lq : arr -> arr * arr
            val sylvester : arr -> arr -> arr -> arr
            val lyapunov : arr -> arr -> arr
            val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

            Module A.Linalg

            val inv : arr -> arr
            val logdet : arr -> elt
            val chol : ?upper:bool -> arr -> arr
            val svd : ?thin:bool -> arr -> arr * arr * arr
            val qr : arr -> arr * arr
            val lq : arr -> arr * arr
            val sylvester : arr -> arr -> arr -> arr
            val lyapunov : arr -> arr -> arr
            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index fbae423fc..5d5a429e9 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

            Module A.Mat

            val diagm : ?k:int -> arr -> arr
            val triu : ?k:int -> arr -> arr
            val tril : ?k:int -> arr -> arr
            val eye : int -> arr
            +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

            Module A.Mat

            val diagm : ?k:int -> arr -> arr
            val triu : ?k:int -> arr -> arr
            val tril : ?k:int -> arr -> arr
            val eye : int -> arr
            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index b13b48bc5..8520ac5fc 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

            Module A.Scalar

            val add : elt -> elt -> elt
            val sub : elt -> elt -> elt
            val mul : elt -> elt -> elt
            val div : elt -> elt -> elt
            val pow : elt -> elt -> elt
            val atan2 : elt -> elt -> elt
            val abs : elt -> elt
            val neg : elt -> elt
            val sqr : elt -> elt
            val sqrt : elt -> elt
            val exp : elt -> elt
            val log : elt -> elt
            val log2 : elt -> elt
            val log10 : elt -> elt
            val signum : elt -> elt
            val floor : elt -> elt
            val ceil : elt -> elt
            val round : elt -> elt
            val sin : elt -> elt
            val cos : elt -> elt
            val tan : elt -> elt
            val sinh : elt -> elt
            val cosh : elt -> elt
            val tanh : elt -> elt
            val asin : elt -> elt
            val acos : elt -> elt
            val atan : elt -> elt
            val asinh : elt -> elt
            val acosh : elt -> elt
            val atanh : elt -> elt
            val relu : elt -> elt
            val dawsn : elt -> elt
            val sigmoid : elt -> elt
            +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

            Module A.Scalar

            val add : elt -> elt -> elt
            val sub : elt -> elt -> elt
            val mul : elt -> elt -> elt
            val div : elt -> elt -> elt
            val pow : elt -> elt -> elt
            val atan2 : elt -> elt -> elt
            val abs : elt -> elt
            val neg : elt -> elt
            val sqr : elt -> elt
            val sqrt : elt -> elt
            val exp : elt -> elt
            val log : elt -> elt
            val log2 : elt -> elt
            val log10 : elt -> elt
            val signum : elt -> elt
            val floor : elt -> elt
            val ceil : elt -> elt
            val round : elt -> elt
            val sin : elt -> elt
            val cos : elt -> elt
            val tan : elt -> elt
            val sinh : elt -> elt
            val cosh : elt -> elt
            val tanh : elt -> elt
            val asin : elt -> elt
            val acos : elt -> elt
            val atan : elt -> elt
            val asinh : elt -> elt
            val acosh : elt -> elt
            val atanh : elt -> elt
            val relu : elt -> elt
            val dawsn : elt -> elt
            val sigmoid : elt -> elt
            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index f00fe0272..29300267e 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

            Module Device.A

            include Owl_types_ndarray_algodiff.Sig
            include Owl_types_ndarray_eltcmp.Sig
            include Owl_types_ndarray_basic.Sig
            type arr
            type elt
            val empty : int array -> arr
            val zeros : int array -> arr
            val ones : int array -> arr
            val create : int array -> elt -> arr
            val sequential : ?a:elt -> ?step:elt -> int array -> arr
            val uniform : ?a:elt -> ?b:elt -> int array -> arr
            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
            val bernoulli : ?p:elt -> int array -> arr
            val init : int array -> (int -> elt) -> arr
            val init_nd : int array -> (int array -> elt) -> arr
            val shape : arr -> int array
            val numel : arr -> int
            val get : arr -> int array -> elt
            val set : arr -> int array -> elt -> unit
            val get_slice : int list list -> arr -> arr
            val set_slice : int list list -> arr -> arr -> unit
            val get_fancy : Owl_types_common.index list -> arr -> arr
            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
            val copy : arr -> arr
            val copy_ : out:arr -> arr -> unit
            val reset : arr -> unit
            val reshape : arr -> int array -> arr
            val reverse : arr -> arr
            val tile : arr -> int array -> arr
            val repeat : arr -> int array -> arr
            val concatenate : ?axis:int -> arr array -> arr
            val stack : ?axis:int -> arr array -> arr
            val split : ?axis:int -> int array -> arr -> arr array
            val expand : ?hi:bool -> arr -> int -> arr
            val squeeze : ?axis:int array -> arr -> arr
            val draw : ?axis:int -> arr -> int -> arr * int array
            val map : (elt -> elt) -> arr -> arr
            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
            val one_hot : int -> arr -> arr
            val pad : ?v:elt -> int list list -> arr -> arr
            val print : +A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

            Module Device.A

            include Owl_types_ndarray_algodiff.Sig
            include Owl_types_ndarray_eltcmp.Sig
            include Owl_types_ndarray_basic.Sig
            type arr
            type elt
            val empty : int array -> arr
            val zeros : int array -> arr
            val ones : int array -> arr
            val create : int array -> elt -> arr
            val sequential : ?a:elt -> ?step:elt -> int array -> arr
            val uniform : ?a:elt -> ?b:elt -> int array -> arr
            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
            val bernoulli : ?p:elt -> int array -> arr
            val init : int array -> (int -> elt) -> arr
            val init_nd : int array -> (int array -> elt) -> arr
            val shape : arr -> int array
            val numel : arr -> int
            val get : arr -> int array -> elt
            val set : arr -> int array -> elt -> unit
            val get_slice : int list list -> arr -> arr
            val set_slice : int list list -> arr -> arr -> unit
            val get_fancy : Owl_types_common.index list -> arr -> arr
            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
            val copy : arr -> arr
            val copy_ : out:arr -> arr -> unit
            val reset : arr -> unit
            val reshape : arr -> int array -> arr
            val reverse : arr -> arr
            val tile : arr -> int array -> arr
            val repeat : arr -> int array -> arr
            val concatenate : ?axis:int -> arr array -> arr
            val stack : ?axis:int -> arr array -> arr
            val split : ?axis:int -> int array -> arr -> arr array
            val expand : ?hi:bool -> arr -> int -> arr
            val squeeze : ?axis:int array -> arr -> arr
            val draw : ?axis:int -> arr -> int -> arr * int array
            val map : (elt -> elt) -> arr -> arr
            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
            val one_hot : int -> arr -> arr
            val pad : ?v:elt -> int list list -> arr -> arr
            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index e96995b8c..2edbdf24f 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

            Module Type.Device

            Type definition
            type device

            TODO

            type value

            TODO

            Core functions
            val make_device : unit -> device

            TODO

            val arr_to_value : A.arr -> value

            TODO

            val value_to_arr : value -> A.arr

            TODO

            val elt_to_value : A.elt -> value

            TODO

            val value_to_elt : value -> A.elt

            TODO

            val value_to_float : value -> float

            TODO

            val is_arr : value -> bool

            TODO

            val is_elt : value -> bool

            TODO

            +Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

            Module Type.Device

            Type definition
            type device

            TODO

            type value

            TODO

            Core functions
            val make_device : unit -> device

            TODO

            val arr_to_value : A.arr -> value

            TODO

            val value_to_arr : value -> A.arr

            TODO

            val elt_to_value : A.elt -> value

            TODO

            val value_to_elt : value -> A.elt

            TODO

            val value_to_float : value -> float

            TODO

            val is_arr : value -> bool

            TODO

            val is_elt : value -> bool

            TODO

            diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index 79f26d2b4..f828fdfdc 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type)

            Module Shape.Type

            Type definition
            type state =
            1. | Valid
            2. | Invalid
              (*

              TODO

              *)

            TODO

            and block = {
            1. size : int;
            2. block_id : int;
            3. mutable active : t option;
            4. mutable memory : Device.value;
            5. mutable nodes : t list;
            }

            block type keeps a reference to a block of memory and to the nodes sharing that block.

            and attr = {
            1. mutable op : op;
            2. mutable freeze : bool;
            3. mutable reuse : bool;
            4. mutable state : state;
            5. mutable shape : int array option array;
            6. mutable value : Device.value array;
            7. mutable block : block array option;
            }

            TODO

            and arr =
            1. | Arr of t
            and elt =
            1. | Elt of t
            and op =
            1. | Noop
            2. | Var
            3. | Const
            4. | Empty of int array
            5. | Zeros of int array
            6. | Ones of int array
            7. | Create of int array
            8. | Sequential of int array
            9. | Uniform of int array
            10. | Gaussian of int array
            11. | Bernoulli of int array
            12. | Init of int array * int -> elt
            13. | Get of int array
            14. | Set of int array
            15. | GetSlice of int list list
            16. | SetSlice of int list list
            17. | GetFancy of Owl_types_common.index list
            18. | SetFancy of Owl_types_common.index list
            19. | Copy
            20. | Reset
            21. | Reshape of int array
            22. | Reverse
            23. | Tile of int array
            24. | Repeat of int array
            25. | Pad of elt * int list list
            26. | Concatenate of int
            27. | Stack of int
            28. | Split of int * int array
            29. | Draw of int * int
            30. | Map of elt -> elt
            31. | Fold of int * elt -> elt -> elt
            32. | Scan of int * elt -> elt -> elt
            33. | OneHot of int
            34. | OfArray of int array
            35. | Delay of Device.A.arr -> Device.A.arr
            36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
            37. | LazyPrint of int option +Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape.Type)

              Module Shape.Type

              Type definition
              type state =
              1. | Valid
              2. | Invalid
                (*

                TODO

                *)

              TODO

              and block = {
              1. size : int;
              2. block_id : int;
              3. mutable active : t option;
              4. mutable memory : Device.value;
              5. mutable nodes : t list;
              }

              block type keeps a reference to a block of memory and to the nodes sharing that block.

              and attr = {
              1. mutable op : op;
              2. mutable freeze : bool;
              3. mutable reuse : bool;
              4. mutable state : state;
              5. mutable shape : int array option array;
              6. mutable value : Device.value array;
              7. mutable block : block array option;
              }

              TODO

              and arr =
              1. | Arr of t
              and elt =
              1. | Elt of t
              and op =
              1. | Noop
              2. | Var
              3. | Const
              4. | Empty of int array
              5. | Zeros of int array
              6. | Ones of int array
              7. | Create of int array
              8. | Sequential of int array
              9. | Uniform of int array
              10. | Gaussian of int array
              11. | Bernoulli of int array
              12. | Init of int array * int -> elt
              13. | Get of int array
              14. | Set of int array
              15. | GetSlice of int list list
              16. | SetSlice of int list list
              17. | GetFancy of Owl_types_common.index list
              18. | SetFancy of Owl_types_common.index list
              19. | Copy
              20. | Reset
              21. | Reshape of int array
              22. | Reverse
              23. | Tile of int array
              24. | Repeat of int array
              25. | Pad of elt * int list list
              26. | Concatenate of int
              27. | Stack of int
              28. | Split of int * int array
              29. | Draw of int * int
              30. | Map of elt -> elt
              31. | Fold of int * elt -> elt -> elt
              32. | Scan of int * elt -> elt -> elt
              33. | OneHot of int
              34. | OfArray of int array
              35. | Delay of Device.A.arr -> Device.A.arr
              36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
              37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
              38. | Abs
              39. | Neg
              40. | Floor
              41. | Ceil
              42. | Round
              43. | Sqr
              44. | Sqrt
              45. | Log
              46. | Log2
              47. | Log10
              48. | Exp
              49. | Sin
              50. | Cos
              51. | Tan
              52. | Sinh
              53. | Cosh
              54. | Tanh
              55. | Asin
              56. | Acos
              57. | Atan
              58. | Asinh
              59. | Acosh
              60. | Atanh
              61. | Min of bool * int
              62. | Max of bool * int
              63. | Sum of bool * int
              64. | SumReduce of int array
              65. | Signum
              66. | Sigmoid
              67. | Relu
              68. | Dawsn
              69. | Min'
              70. | Max'
              71. | Sum'
              72. | LogSumExp'
              73. | LogSumExp of bool * int
              74. | L1norm'
              75. | L2norm'
              76. | L2NormSqr'
              77. | ClipByValue
              78. | ClipByL2norm
              79. | Pow
              80. | ScalarPow
              81. | PowScalar
              82. | Atan2
              83. | ScalarAtan2
              84. | Atan2Scalar
              85. | Hypot
              86. | Min2
              87. | Max2
              88. | Add
              89. | Sub
              90. | Mul
              91. | Div
              92. | AddScalar
              93. | SubScalar
              94. | MulScalar
              95. | DivScalar
              96. | ScalarAdd
              97. | ScalarSub
              98. | ScalarMul
              99. | ScalarDiv
              100. | FMA
              101. | EltEqual
              102. | EltNotEqual
              103. | EltLess
              104. | EltGreater
              105. | EltLessEqual
              106. | EltGreaterEqual
              107. | EltEqualScalar
              108. | EltNotEqualScalar
              109. | EltLessScalar
              110. | EltGreaterScalar
              111. | EltLessEqualScalar
              112. | EltGreaterEqualScalar
              113. | Conv1d of Owl_types_common.padding * int array
              114. | Conv2d of Owl_types_common.padding * int array
              115. | Conv3d of Owl_types_common.padding * int array
              116. | TransposeConv1d of Owl_types_common.padding * int array
              117. | TransposeConv2d of Owl_types_common.padding * int array
              118. | TransposeConv3d of Owl_types_common.padding * int array
              119. | DilatedConv1d of Owl_types_common.padding * int array * int array
              120. | DilatedConv2d of Owl_types_common.padding * int array * int array
              121. | DilatedConv3d of Owl_types_common.padding * int array * int array
              122. | MaxPool1d of Owl_types_common.padding * int array * int array
              123. | MaxPool2d of Owl_types_common.padding * int array * int array
              124. | MaxPool3d of Owl_types_common.padding * int array * int array
              125. | AvgPool1d of Owl_types_common.padding * int array * int array
              126. | AvgPool2d of Owl_types_common.padding * int array * int array
              127. | AvgPool3d of Owl_types_common.padding * int array * int array
              128. | UpSampling2d of int array
              129. | Conv1dBackwardInput of int array
              130. | Conv1dBackwardKernel of int array
              131. | Conv2dBackwardInput of int array
              132. | Conv2dBackwardKernel of int array
              133. | Conv3dBackwardInput of int array
              134. | Conv3dBackwardKernel of int array
              135. | TransposeConv1dBackwardInput of int array
              136. | TransposeConv1dBackwardKernel of int array
              137. | TransposeConv2dBackwardInput of int array
              138. | TransposeConv2dBackwardKernel of int array
              139. | TransposeConv3dBackwardInput of int array
              140. | TransposeConv3dBackwardKernel of int array
              141. | DilatedConv1dBackwardInput of int array * int array
              142. | DilatedConv1dBackwardKernel of int array * int array
              143. | DilatedConv2dBackwardInput of int array * int array
              144. | DilatedConv2dBackwardKernel of int array * int array
              145. | DilatedConv3dBackwardInput of int array * int array
              146. | DilatedConv3dBackwardKernel of int array * int array
              147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
              148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
              149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
              150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
              151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
              152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
              153. | UpSampling2dBackward of int array
              154. | RowNum
              155. | ColNum
              156. | Row
              157. | Rows of int array
              158. | CopyRowTo
              159. | CopyColTo
              160. | Dot of bool * bool * elt * elt
              161. | Inv
              162. | Trace
              163. | Transpose of int array
              164. | ToRows
              165. | OfRows
              166. | Scalar_Add
              167. | Scalar_Sub
              168. | Scalar_Mul
              169. | Scalar_Div
              170. | Scalar_Pow
              171. | Scalar_Atan2
              172. | Scalar_Abs
              173. | Scalar_Neg
              174. | Scalar_Sqr
              175. | Scalar_Sqrt
              176. | Scalar_Exp
              177. | Scalar_Log
              178. | Scalar_Log2
              179. | Scalar_Log10
              180. | Scalar_Signum
              181. | Scalar_Floor
              182. | Scalar_Ceil
              183. | Scalar_Round
              184. | Scalar_Sin
              185. | Scalar_Cos
              186. | Scalar_Tan
              187. | Scalar_Sinh
              188. | Scalar_Cosh
              189. | Scalar_Tanh
              190. | Scalar_Asin
              191. | Scalar_Acos
              192. | Scalar_Atan
              193. | Scalar_Asinh
              194. | Scalar_Acosh
              195. | Scalar_Atanh
              196. | Scalar_Relu
              197. | Scalar_Dawsn
              198. | Scalar_Sigmoid
              199. | Fused_Adagrad of float * float
                (*

                TODO

                *)
              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/index.html index 792c454c9..33cf64534 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape)

              Module Symbol.Shape

              Core functions
              val infer_shape : +Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol.Shape)

              Module Symbol.Shape

              Core functions
              val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

              TODO

              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/index.html index f9957d633..6b912ef63 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol)

              Module Operator.Symbol

              Core functions
              val op_to_str : Shape.Type.op -> string

              TODO

              val is_random_variable : Shape.Type.op -> bool

              TODO

              val refnum : 'a Owl_graph.node -> int

              TODO

              val node_shape : Shape.Type.attr Owl_graph.node -> int array

              TODO

              val node_numel : Shape.Type.attr Owl_graph.node -> int

              TODO

              val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

              TODO

              val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

              TODO

              val shape_to_str : int array option array -> string

              TODO

              val node_to_str : Shape.Type.attr Owl_graph.node -> string

              TODO

              val node_to_arr : Shape.Type.t -> Shape.Type.arr

              TODO

              val arr_to_node : Shape.Type.arr -> Shape.Type.t

              TODO

              val node_to_elt : Shape.Type.t -> Shape.Type.elt

              TODO

              val elt_to_node : Shape.Type.elt -> Shape.Type.t

              TODO

              val make_node : +Symbol (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator.Symbol)

              Module Operator.Symbol

              Core functions
              val op_to_str : Shape.Type.op -> string

              TODO

              val is_random_variable : Shape.Type.op -> bool

              TODO

              val refnum : 'a Owl_graph.node -> int

              TODO

              val node_shape : Shape.Type.attr Owl_graph.node -> int array

              TODO

              val node_numel : Shape.Type.attr Owl_graph.node -> int

              TODO

              val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

              TODO

              val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

              TODO

              val shape_to_str : int array option array -> string

              TODO

              val node_to_str : Shape.Type.attr Owl_graph.node -> string

              TODO

              val node_to_arr : Shape.Type.t -> Shape.Type.arr

              TODO

              val arr_to_node : Shape.Type.arr -> Shape.Type.t

              TODO

              val node_to_elt : Shape.Type.t -> Shape.Type.elt

              TODO

              val elt_to_node : Shape.Type.elt -> Shape.Type.t

              TODO

              val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/index.html index 7aa936255..763316a4a 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator)

              Module Optimiser.Operator

              Vectorised functions
              val empty : int array -> Symbol.Shape.Type.arr

              TODO

              val zeros : int array -> Symbol.Shape.Type.arr

              TODO

              val ones : int array -> Symbol.Shape.Type.arr

              TODO

              val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

              TODO

              val sequential : +Operator (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser.Operator)

              Module Optimiser.Operator

              Vectorised functions

              noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

              val empty : int array -> Symbol.Shape.Type.arr

              empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

              val zeros : int array -> Symbol.Shape.Type.arr

              zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

              val ones : int array -> Symbol.Shape.Type.arr

              ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

              val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

              create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

              val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

              TODO

              val uniform : + Symbol.Shape.Type.arr

              sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

              val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

              TODO

              val gaussian : + Symbol.Shape.Type.arr

              uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

              val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

              TODO

              val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

              TODO

              val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

              TODO

              val init_nd : + Symbol.Shape.Type.arr

              gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

              val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

              bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

              val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

              init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

              val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

              TODO

              val shape : Symbol.Shape.Type.arr -> int array

              TODO

              val numel : Symbol.Shape.Type.arr -> int

              TODO

              TODO

              val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

              TODO

              val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

              TODO

              val set_slice : + Symbol.Shape.Type.arr

              init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

              val shape : Symbol.Shape.Type.arr -> int array

              shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

              val numel : Symbol.Shape.Type.arr -> int

              numel arr returns the total number of elements in the array arr.

              get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

              val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

              set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

              val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

              get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

              val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

              TODO

              val get_fancy : + unit

              set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

              val set_fancy : + Symbol.Shape.Type.arr

              get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

              val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

              TODO

              val copy_ : out:'a -> 'b -> 'c

              TODO

              val reset : Symbol.Shape.Type.arr -> unit

              TODO

              val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              TODO

              val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              TODO

              val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              TODO

              val pad : + unit

              set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

              copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

              val copy_ : out:'a -> 'b -> 'c

              copy_ ~out src copies the contents of the array src into the pre-allocated array out.

              val reset : Symbol.Shape.Type.arr -> unit

              reset arr sets all elements of the array arr to zero.

              val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

              reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

              val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

              val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

              TODO

              val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

              TODO

              val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

              TODO

              val concatenate : + Symbol.Shape.Type.arr

              pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

              val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

              expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

              val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

              squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

              val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

              TODO

              val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

              TODO

              val concat : + Symbol.Shape.Type.arr

              concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

              val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

              stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

              val split : ?axis:int -> 'a -> 'b -> 'c

              TODO

              concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

              val split : ?axis:int -> 'a -> 'b -> 'c

              split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

              • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
              val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

              TODO

              val map : + Symbol.Shape.Type.arr * 'a array

              draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

              map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

              fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

              TODO

              val delay : + Symbol.Shape.Type.arr

              scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

              one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

              delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

              val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

              val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

              TODO

              lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

              val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

              print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

              • max_row is an optional parameter specifying the maximum number of rows to print.
              • max_col is an optional parameter specifying the maximum number of columns to print.
              • header is an optional parameter to include a header in the output.
              • fmt is an optional parameter to specify the format of the output.

              abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

              neg arr negates each element in the array arr. Returns a new array with each element negated.

              floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

              ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

              round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

              sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

              sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

              log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

              log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

              log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

              exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

              sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

              cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

              tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

              sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

              cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

              tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

              asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

              acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

              atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

              asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

              acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

              atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

              val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

              • axis specifies the axis along which to compute the minimum.
              • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
              val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

              • axis specifies the axis along which to compute the maximum.
              • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
              val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val sum_reduce : + Symbol.Shape.Type.arr

              sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

              • axis specifies the axis along which to compute the sum.
              • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
              val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val log_sum_exp : + Symbol.Shape.Type.arr

              sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

              • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

              signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

              sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

              relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

              dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

              min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

              max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

              sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

              log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

              val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val clip_by_value : + Symbol.Shape.Type.arr

              log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

              • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
              • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

              l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

              l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

              l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

              val clip_by_l2norm : + Symbol.Shape.Type.arr

              clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

              • amin specifies the minimum value to clip to.
              • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

              clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

              val scalar_pow : + Symbol.Shape.Type.arr

              pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

              val pow_scalar : + Symbol.Shape.Type.arr

              scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

              val atan2 : + Symbol.Shape.Type.arr

              pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

              val scalar_atan2 : + Symbol.Shape.Type.arr

              atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

              val atan2_scalar : + Symbol.Shape.Type.arr

              scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

              val hypot : + Symbol.Shape.Type.arr

              atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

              hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

              min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

              max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

              add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

              sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

              mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

              val add_scalar : + Symbol.Shape.Type.arr

              div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

              val sub_scalar : + Symbol.Shape.Type.arr

              add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

              val mul_scalar : + Symbol.Shape.Type.arr

              sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

              val div_scalar : + Symbol.Shape.Type.arr

              mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

              val scalar_add : + Symbol.Shape.Type.arr

              div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

              val scalar_sub : + Symbol.Shape.Type.arr

              scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

              val scalar_mul : + Symbol.Shape.Type.arr

              scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

              val scalar_div : + Symbol.Shape.Type.arr

              scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

              scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

              val elt_equal : + Symbol.Shape.Type.arr

              fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

              val elt_not_equal : + Symbol.Shape.Type.arr

              elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

              val elt_less : + Symbol.Shape.Type.arr

              elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

              val elt_greater : + Symbol.Shape.Type.arr

              elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

              val elt_less_equal : + Symbol.Shape.Type.arr

              elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

              val elt_greater_equal : + Symbol.Shape.Type.arr

              elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

              val elt_equal_scalar : + Symbol.Shape.Type.arr

              elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

              val elt_not_equal_scalar : + Symbol.Shape.Type.arr

              elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

              val elt_less_scalar : + Symbol.Shape.Type.arr

              elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

              val elt_greater_scalar : + Symbol.Shape.Type.arr

              elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

              val elt_less_equal_scalar : + Symbol.Shape.Type.arr

              elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

              TODO

              val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

              elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

              TODO

              val conv1d : + Symbol.Shape.Type.arr

              elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

              val conv2d : + Symbol.Shape.Type.arr

              conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

              • padding specifies the padding strategy (default is "valid").
              • strides specifies the stride length. Returns a new array with the result of the convolution.
              val conv3d : + Symbol.Shape.Type.arr

              conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

              • padding specifies the padding strategy (default is "valid").
              • strides specifies the stride length. Returns a new array with the result of the convolution.
              val transpose_conv1d : + Symbol.Shape.Type.arr

              conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

              • padding specifies the padding strategy (default is "valid").
              • strides specifies the stride length. Returns a new array with the result of the convolution.
              val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

              TODO

              val transpose_conv2d : + Symbol.Shape.Type.arr

              transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

              • padding specifies the padding strategy (default is "valid").
              • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
              val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

              TODO

              val transpose_conv3d : + Symbol.Shape.Type.arr

              transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

              • padding specifies the padding strategy (default is "valid").
              • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
              val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

              TODO

              val dilated_conv1d : + Symbol.Shape.Type.arr

              transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

              • padding specifies the padding strategy (default is "valid").
              • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
              val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val dilated_conv2d : + Symbol.Shape.Type.arr

              dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

              • padding specifies the padding strategy (default is "valid").
              • strides specifies the stride length.
              • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
              val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val dilated_conv3d : + Symbol.Shape.Type.arr

              dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

              • padding specifies the padding strategy (default is "valid").
              • strides specifies the stride length.
              • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
              val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val max_pool1d : + Symbol.Shape.Type.arr

              dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

              • padding specifies the padding strategy (default is "valid").
              • strides specifies the stride length.
              • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
              val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val max_pool2d : + Symbol.Shape.Type.arr

              max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

              • padding specifies the padding strategy (default is "valid").
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length. Returns a new array with the result of the max pooling.
              val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val max_pool3d : + Symbol.Shape.Type.arr

              max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

              • padding specifies the padding strategy (default is "valid").
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length. Returns a new array with the result of the max pooling.
              val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val avg_pool1d : + Symbol.Shape.Type.arr

              max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

              • padding specifies the padding strategy (default is "valid").
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length. Returns a new array with the result of the max pooling.
              val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val avg_pool2d : + Symbol.Shape.Type.arr

              avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

              • padding specifies the padding strategy (default is "valid").
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length. Returns a new array with the result of the average pooling.
              val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val avg_pool3d : + Symbol.Shape.Type.arr

              avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

              • padding specifies the padding strategy (default is "valid").
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length. Returns a new array with the result of the average pooling.
              val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              TODO

              val conv1d_backward_input : + Symbol.Shape.Type.arr

              avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

              • padding specifies the padding strategy (default is "valid").
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length. Returns a new array with the result of the average pooling.
              val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              upsampling2d input size performs a 2-dimensional upsampling on the input array.

              • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

              TODO

              val conv1d_backward_kernel : + Symbol.Shape.Type.arr

              conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

              • input is the original input array.
              • kernel is the convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
              val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val conv2d_backward_input : + Symbol.Shape.Type.arr

              conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

              • input is the original input array.
              • kernel is the convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

              TODO

              val conv2d_backward_kernel : + Symbol.Shape.Type.arr

              conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

              • input is the original input array.
              • kernel is the convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
              val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val conv3d_backward_input : + Symbol.Shape.Type.arr

              conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

              • input is the original input array.
              • kernel is the convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

              TODO

              val conv3d_backward_kernel : + Symbol.Shape.Type.arr

              conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

              • input is the original input array.
              • kernel is the convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
              val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

              conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

              • input is the original input array.
              • kernel is the convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
              val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

              transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

              • input is the original input array.
              • kernel is the transposed convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
              val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

              transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

              • input is the original input array.
              • kernel is the transposed convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
              val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

              transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

              • input is the original input array.
              • kernel is the transposed convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
              val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

              transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

              • input is the original input array.
              • kernel is the transposed convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
              val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

              transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

              • input is the original input array.
              • kernel is the transposed convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
              val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

              transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

              • input is the original input array.
              • kernel is the transposed convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
              val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

              dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

              • input is the original input array.
              • kernel is the dilated convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • dilations specifies the dilation rate.
              • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
              val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

              dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

              • input is the original input array.
              • kernel is the dilated convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • dilations specifies the dilation rate.
              • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
              val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

              dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

              • input is the original input array.
              • kernel is the dilated convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • dilations specifies the dilation rate.
              • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
              val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

              dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

              • input is the original input array.
              • kernel is the dilated convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • dilations specifies the dilation rate.
              • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
              val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

              dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

              • input is the original input array.
              • kernel is the dilated convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • dilations specifies the dilation rate.
              • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
              val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val max_pool1d_backward : + Symbol.Shape.Type.arr

              dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

              • input is the original input array.
              • kernel is the dilated convolutional kernel used during the forward pass.
              • strides specifies the stride length.
              • dilations specifies the dilation rate.
              • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
              val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val max_pool2d_backward : + Symbol.Shape.Type.arr

              max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

              • padding specifies the padding strategy used during the forward pass.
              • input is the original input array.
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
              val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val max_pool3d_backward : + Symbol.Shape.Type.arr

              max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

              • padding specifies the padding strategy used during the forward pass.
              • input is the original input array.
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
              val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val avg_pool1d_backward : + Symbol.Shape.Type.arr

              max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

              • padding specifies the padding strategy used during the forward pass.
              • input is the original input array.
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
              val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val avg_pool2d_backward : + Symbol.Shape.Type.arr

              avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

              • padding specifies the padding strategy used during the forward pass.
              • input is the original input array.
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
              val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val avg_pool3d_backward : + Symbol.Shape.Type.arr

              avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

              • padding specifies the padding strategy used during the forward pass.
              • input is the original input array.
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
              val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val upsampling2d_backward : + Symbol.Shape.Type.arr

              avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

              • padding specifies the padding strategy used during the forward pass.
              • input is the original input array.
              • pool_size specifies the size of the pooling window.
              • strides specifies the stride length.
              • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
              val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val row_num : Symbol.Shape.Type.arr -> int

              TODO

              val col_num : Symbol.Shape.Type.arr -> int

              TODO

              val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              TODO

              val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

              TODO

              val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

              TODO

              TODO

              upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

              • input is the original input array.
              • size specifies the upsampling factors for each dimension.
              • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
              val row_num : Symbol.Shape.Type.arr -> int

              row_num arr returns the number of rows in the array arr.

              val col_num : Symbol.Shape.Type.arr -> int

              col_num arr returns the number of columns in the array arr.

              row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

              val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

              rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

              val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

              copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

              val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

              copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

              diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

              trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

              val transpose : + Symbol.Shape.Type.arr

              dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

              val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val to_rows : Symbol.Shape.Type.arr -> 'a array

              TODO

              TODO

              val to_cols : Symbol.Shape.Type.arr -> 'a array

              TODO

              TODO

              val of_array : + Symbol.Shape.Type.arr

              transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

              val to_rows : Symbol.Shape.Type.arr -> 'a array

              to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

              of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

              val to_cols : Symbol.Shape.Type.arr -> 'a array

              to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

              of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

              val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

              TODO

              val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

              TODO

              val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

              TODO

              Scalar functions
              module Scalar : sig ... end
              module Mat : sig ... end
              module Linalg : sig ... end
              + Symbol.Shape.Type.arr

              of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

              val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

              of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

              val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

              to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

              Scalar functions
              module Scalar : sig ... end
              module Mat : sig ... end
              module Linalg : sig ... end
              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/index.html index af0c8b87b..34a9f5df8 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser)

              Module Graph.Optimiser

              Core functions
              val estimate_complexity : 'a Owl_graph.node array -> int * int

              TODO

              val optimise_nodes : +Optimiser (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph.Optimiser)

              Module Graph.Optimiser

              Core functions
              val estimate_complexity : 'a Owl_graph.node array -> int * int

              TODO

              val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

              TODO

              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/index.html index 886526035..821791cc0 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph)

              Module Flatten_Sig.Graph

              Type definition
              type graph

              TODO

              Core functions
              val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

              TODO

              val graph_to_dot : graph -> string

              TODO

              val graph_to_trace : graph -> string

              TODO

              val save_graph : 'a -> string -> unit

              TODO

              val load_graph : string -> 'a * 'b

              TODO

              val collect_rvs : +Graph (owl-base.Owl_computation_engine_sig.Flatten_Sig.Graph)

              Module Flatten_Sig.Graph

              Type definition
              type graph

              TODO

              Core functions
              val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

              TODO

              val graph_to_dot : graph -> string

              TODO

              val graph_to_trace : graph -> string

              TODO

              val save_graph : 'a -> string -> unit

              TODO

              val load_graph : string -> 'a * 'b

              TODO

              val invalidate_rvs : graph -> unit

              TODO

              val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Linalg/index.html index c8edf988a..36455189d 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Linalg)

              Module Flatten_Sig.Linalg

              val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

              TODO

              val svd : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Linalg)

              Module Flatten_Sig.Linalg

              inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

              logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

              val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

              chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

              • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

              qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

              lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

              svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

              • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
              val lyapunov : + Symbol.Shape.Type.arr

              sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

              val discrete_lyapunov : + Symbol.Shape.Type.arr

              lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

              val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val linsolve : + Symbol.Shape.Type.arr

              discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

              • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
              val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

              • trans specifies whether to transpose the matrix A.
              • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

              care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

              • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
              + Symbol.Shape.Type.arr

              dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

              • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Mat/index.html index eaf0a0115..ad8d96e09 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Mat)

              Module Flatten_Sig.Mat

              val eye : int -> Symbol.Shape.Type.arr

              TODO

              TODO

              TODO

              TODO

              +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Mat)

              Module Flatten_Sig.Mat

              val eye : int -> Symbol.Shape.Type.arr

              eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

              diagm ?k v creates a diagonal matrix from the array v.

              • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

              triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

              tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Linalg/index.html index 0c3eedd0d..b8bc86e1e 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Linalg)

              Module Operator.Linalg

              val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

              TODO

              val svd : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Linalg)

              Module Operator.Linalg

              inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

              logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

              val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

              chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

              • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

              qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

              lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

              svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

              • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
              val lyapunov : + Symbol.Shape.Type.arr

              sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

              val discrete_lyapunov : + Symbol.Shape.Type.arr

              lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

              val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              val linsolve : + Symbol.Shape.Type.arr

              discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

              • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
              val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

              TODO

              linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

              • trans specifies whether to transpose the matrix A.
              • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

              care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

              • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
              + Symbol.Shape.Type.arr

              dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

              • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Mat/index.html index 03ddc54a8..4b69511dc 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Mat)

              Module Operator.Mat

              val eye : int -> Symbol.Shape.Type.arr

              TODO

              TODO

              TODO

              TODO

              +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Mat)

              Module Operator.Mat

              val eye : int -> Symbol.Shape.Type.arr

              eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

              diagm ?k v creates a diagonal matrix from the array v.

              • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

              triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

              tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Scalar/index.html index 0574a45a2..8b08936ef 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Scalar)

              Module Operator.Scalar

              val add : +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Scalar)

              Module Operator.Scalar

              add a b returns the sum of the scalars a and b.

              sub a b returns the difference of the scalars a and b.

              mul a b returns the product of the scalars a and b.

              div a b returns the quotient of the scalars a and b.

              val atan2 : + Symbol.Shape.Type.elt

              pow a b returns the scalar a raised to the power of b.

              + Symbol.Shape.Type.elt

              atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

              abs a returns the absolute value of the scalar a.

              neg a returns the negation of the scalar a.

              sqr a returns the square of the scalar a.

              sqrt a returns the square root of the scalar a.

              exp a returns the exponential of the scalar a.

              log a returns the natural logarithm of the scalar a.

              log2 a returns the base-2 logarithm of the scalar a.

              log10 a returns the base-10 logarithm of the scalar a.

              signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

              floor a returns the greatest integer less than or equal to the scalar a.

              ceil a returns the smallest integer greater than or equal to the scalar a.

              round a returns the nearest integer to the scalar a.

              sin a returns the sine of the scalar a.

              cos a returns the cosine of the scalar a.

              tan a returns the tangent of the scalar a.

              sinh a returns the hyperbolic sine of the scalar a.

              cosh a returns the hyperbolic cosine of the scalar a.

              tanh a returns the hyperbolic tangent of the scalar a.

              asin a returns the arcsine of the scalar a.

              acos a returns the arccosine of the scalar a.

              atan a returns the arctangent of the scalar a.

              asinh a returns the inverse hyperbolic sine of the scalar a.

              acosh a returns the inverse hyperbolic cosine of the scalar a.

              atanh a returns the inverse hyperbolic tangent of the scalar a.

              relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

              dawsn a returns Dawson's function of the scalar a.

              sigmoid a returns the sigmoid function of the scalar a.

              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 98235f133..868ac7e8d 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device.A.Linalg)

              Module A.Linalg

              val inv : arr -> arr
              val logdet : arr -> elt
              val chol : ?upper:bool -> arr -> arr
              val svd : ?thin:bool -> arr -> arr * arr * arr
              val qr : arr -> arr * arr
              val lq : arr -> arr * arr
              val sylvester : arr -> arr -> arr -> arr
              val lyapunov : arr -> arr -> arr
              val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device.A.Linalg)

              Module A.Linalg

              val inv : arr -> arr
              val logdet : arr -> elt
              val chol : ?upper:bool -> arr -> arr
              val svd : ?thin:bool -> arr -> arr * arr * arr
              val qr : arr -> arr * arr
              val lq : arr -> arr * arr
              val sylvester : arr -> arr -> arr -> arr
              val lyapunov : arr -> arr -> arr
              val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 1c77b4a70..1d545c199 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device.A.Mat)

              Module A.Mat

              val diagm : ?k:int -> arr -> arr
              val triu : ?k:int -> arr -> arr
              val tril : ?k:int -> arr -> arr
              val eye : int -> arr
              +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device.A.Mat)

              Module A.Mat

              val diagm : ?k:int -> arr -> arr
              val triu : ?k:int -> arr -> arr
              val tril : ?k:int -> arr -> arr
              val eye : int -> arr
              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index a3cf88421..fe6257ac2 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device.A.Scalar)

              Module A.Scalar

              val add : elt -> elt -> elt
              val sub : elt -> elt -> elt
              val mul : elt -> elt -> elt
              val div : elt -> elt -> elt
              val pow : elt -> elt -> elt
              val atan2 : elt -> elt -> elt
              val abs : elt -> elt
              val neg : elt -> elt
              val sqr : elt -> elt
              val sqrt : elt -> elt
              val exp : elt -> elt
              val log : elt -> elt
              val log2 : elt -> elt
              val log10 : elt -> elt
              val signum : elt -> elt
              val floor : elt -> elt
              val ceil : elt -> elt
              val round : elt -> elt
              val sin : elt -> elt
              val cos : elt -> elt
              val tan : elt -> elt
              val sinh : elt -> elt
              val cosh : elt -> elt
              val tanh : elt -> elt
              val asin : elt -> elt
              val acos : elt -> elt
              val atan : elt -> elt
              val asinh : elt -> elt
              val acosh : elt -> elt
              val atanh : elt -> elt
              val relu : elt -> elt
              val dawsn : elt -> elt
              val sigmoid : elt -> elt
              +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device.A.Scalar)

              Module A.Scalar

              val add : elt -> elt -> elt
              val sub : elt -> elt -> elt
              val mul : elt -> elt -> elt
              val div : elt -> elt -> elt
              val pow : elt -> elt -> elt
              val atan2 : elt -> elt -> elt
              val abs : elt -> elt
              val neg : elt -> elt
              val sqr : elt -> elt
              val sqrt : elt -> elt
              val exp : elt -> elt
              val log : elt -> elt
              val log2 : elt -> elt
              val log10 : elt -> elt
              val signum : elt -> elt
              val floor : elt -> elt
              val ceil : elt -> elt
              val round : elt -> elt
              val sin : elt -> elt
              val cos : elt -> elt
              val tan : elt -> elt
              val sinh : elt -> elt
              val cosh : elt -> elt
              val tanh : elt -> elt
              val asin : elt -> elt
              val acos : elt -> elt
              val atan : elt -> elt
              val asinh : elt -> elt
              val acosh : elt -> elt
              val atanh : elt -> elt
              val relu : elt -> elt
              val dawsn : elt -> elt
              val sigmoid : elt -> elt
              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/index.html index 2231784bb..b45039423 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device.A)

              Module Device.A

              include Owl_types_ndarray_algodiff.Sig
              include Owl_types_ndarray_eltcmp.Sig
              include Owl_types_ndarray_basic.Sig
              type arr
              type elt
              val empty : int array -> arr
              val zeros : int array -> arr
              val ones : int array -> arr
              val create : int array -> elt -> arr
              val sequential : ?a:elt -> ?step:elt -> int array -> arr
              val uniform : ?a:elt -> ?b:elt -> int array -> arr
              val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
              val bernoulli : ?p:elt -> int array -> arr
              val init : int array -> (int -> elt) -> arr
              val init_nd : int array -> (int array -> elt) -> arr
              val shape : arr -> int array
              val numel : arr -> int
              val get : arr -> int array -> elt
              val set : arr -> int array -> elt -> unit
              val get_slice : int list list -> arr -> arr
              val set_slice : int list list -> arr -> arr -> unit
              val get_fancy : Owl_types_common.index list -> arr -> arr
              val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
              val copy : arr -> arr
              val copy_ : out:arr -> arr -> unit
              val reset : arr -> unit
              val reshape : arr -> int array -> arr
              val reverse : arr -> arr
              val tile : arr -> int array -> arr
              val repeat : arr -> int array -> arr
              val concatenate : ?axis:int -> arr array -> arr
              val stack : ?axis:int -> arr array -> arr
              val split : ?axis:int -> int array -> arr -> arr array
              val expand : ?hi:bool -> arr -> int -> arr
              val squeeze : ?axis:int array -> arr -> arr
              val draw : ?axis:int -> arr -> int -> arr * int array
              val map : (elt -> elt) -> arr -> arr
              val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
              val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
              val one_hot : int -> arr -> arr
              val pad : ?v:elt -> int list list -> arr -> arr
              val print : +A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device.A)

              Module Device.A

              include Owl_types_ndarray_algodiff.Sig
              include Owl_types_ndarray_eltcmp.Sig
              include Owl_types_ndarray_basic.Sig
              type arr
              type elt
              val empty : int array -> arr
              val zeros : int array -> arr
              val ones : int array -> arr
              val create : int array -> elt -> arr
              val sequential : ?a:elt -> ?step:elt -> int array -> arr
              val uniform : ?a:elt -> ?b:elt -> int array -> arr
              val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
              val bernoulli : ?p:elt -> int array -> arr
              val init : int array -> (int -> elt) -> arr
              val init_nd : int array -> (int array -> elt) -> arr
              val shape : arr -> int array
              val numel : arr -> int
              val get : arr -> int array -> elt
              val set : arr -> int array -> elt -> unit
              val get_slice : int list list -> arr -> arr
              val set_slice : int list list -> arr -> arr -> unit
              val get_fancy : Owl_types_common.index list -> arr -> arr
              val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
              val copy : arr -> arr
              val copy_ : out:arr -> arr -> unit
              val reset : arr -> unit
              val reshape : arr -> int array -> arr
              val reverse : arr -> arr
              val tile : arr -> int array -> arr
              val repeat : arr -> int array -> arr
              val concatenate : ?axis:int -> arr array -> arr
              val stack : ?axis:int -> arr array -> arr
              val split : ?axis:int -> int array -> arr -> arr array
              val expand : ?hi:bool -> arr -> int -> arr
              val squeeze : ?axis:int array -> arr -> arr
              val draw : ?axis:int -> arr -> int -> arr * int array
              val map : (elt -> elt) -> arr -> arr
              val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
              val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
              val one_hot : int -> arr -> arr
              val pad : ?v:elt -> int list list -> arr -> arr
              val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/index.html index 44c454ae5..68b478432 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device)

              Module Type.Device

              Type definition
              type device

              TODO

              type value

              TODO

              Core functions
              val make_device : unit -> device

              TODO

              val arr_to_value : A.arr -> value

              TODO

              val value_to_arr : value -> A.arr

              TODO

              val elt_to_value : A.elt -> value

              TODO

              val value_to_elt : value -> A.elt

              TODO

              val value_to_float : value -> float

              TODO

              val is_arr : value -> bool

              TODO

              val is_elt : value -> bool

              TODO

              +Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type.Device)

              Module Type.Device

              Type definition
              type device

              TODO

              type value

              TODO

              Core functions
              val make_device : unit -> device

              TODO

              val arr_to_value : A.arr -> value

              TODO

              val value_to_arr : value -> A.arr

              TODO

              val elt_to_value : A.elt -> value

              TODO

              val value_to_elt : value -> A.elt

              TODO

              val value_to_float : value -> float

              TODO

              val is_arr : value -> bool

              TODO

              val is_elt : value -> bool

              TODO

              diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/index.html index fff5d0f4f..9c145f5af 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type)

              Module Shape.Type

              Type definition
              type state =
              1. | Valid
              2. | Invalid
                (*

                TODO

                *)

              TODO

              and block = {
              1. size : int;
              2. block_id : int;
              3. mutable active : t option;
              4. mutable memory : Device.value;
              5. mutable nodes : t list;
              }

              block type keeps a reference to a block of memory and to the nodes sharing that block.

              and attr = {
              1. mutable op : op;
              2. mutable freeze : bool;
              3. mutable reuse : bool;
              4. mutable state : state;
              5. mutable shape : int array option array;
              6. mutable value : Device.value array;
              7. mutable block : block array option;
              }

              TODO

              and arr =
              1. | Arr of t
              and elt =
              1. | Elt of t
              and op =
              1. | Noop
              2. | Var
              3. | Const
              4. | Empty of int array
              5. | Zeros of int array
              6. | Ones of int array
              7. | Create of int array
              8. | Sequential of int array
              9. | Uniform of int array
              10. | Gaussian of int array
              11. | Bernoulli of int array
              12. | Init of int array * int -> elt
              13. | Get of int array
              14. | Set of int array
              15. | GetSlice of int list list
              16. | SetSlice of int list list
              17. | GetFancy of Owl_types_common.index list
              18. | SetFancy of Owl_types_common.index list
              19. | Copy
              20. | Reset
              21. | Reshape of int array
              22. | Reverse
              23. | Tile of int array
              24. | Repeat of int array
              25. | Pad of elt * int list list
              26. | Concatenate of int
              27. | Stack of int
              28. | Split of int * int array
              29. | Draw of int * int
              30. | Map of elt -> elt
              31. | Fold of int * elt -> elt -> elt
              32. | Scan of int * elt -> elt -> elt
              33. | OneHot of int
              34. | OfArray of int array
              35. | Delay of Device.A.arr -> Device.A.arr
              36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
              37. | LazyPrint of int option +Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape.Type)

                Module Shape.Type

                Type definition
                type state =
                1. | Valid
                2. | Invalid
                  (*

                  TODO

                  *)

                TODO

                and block = {
                1. size : int;
                2. block_id : int;
                3. mutable active : t option;
                4. mutable memory : Device.value;
                5. mutable nodes : t list;
                }

                block type keeps a reference to a block of memory and to the nodes sharing that block.

                and attr = {
                1. mutable op : op;
                2. mutable freeze : bool;
                3. mutable reuse : bool;
                4. mutable state : state;
                5. mutable shape : int array option array;
                6. mutable value : Device.value array;
                7. mutable block : block array option;
                }

                TODO

                and arr =
                1. | Arr of t
                and elt =
                1. | Elt of t
                and op =
                1. | Noop
                2. | Var
                3. | Const
                4. | Empty of int array
                5. | Zeros of int array
                6. | Ones of int array
                7. | Create of int array
                8. | Sequential of int array
                9. | Uniform of int array
                10. | Gaussian of int array
                11. | Bernoulli of int array
                12. | Init of int array * int -> elt
                13. | Get of int array
                14. | Set of int array
                15. | GetSlice of int list list
                16. | SetSlice of int list list
                17. | GetFancy of Owl_types_common.index list
                18. | SetFancy of Owl_types_common.index list
                19. | Copy
                20. | Reset
                21. | Reshape of int array
                22. | Reverse
                23. | Tile of int array
                24. | Repeat of int array
                25. | Pad of elt * int list list
                26. | Concatenate of int
                27. | Stack of int
                28. | Split of int * int array
                29. | Draw of int * int
                30. | Map of elt -> elt
                31. | Fold of int * elt -> elt -> elt
                32. | Scan of int * elt -> elt -> elt
                33. | OneHot of int
                34. | OfArray of int array
                35. | Delay of Device.A.arr -> Device.A.arr
                36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                38. | Abs
                39. | Neg
                40. | Floor
                41. | Ceil
                42. | Round
                43. | Sqr
                44. | Sqrt
                45. | Log
                46. | Log2
                47. | Log10
                48. | Exp
                49. | Sin
                50. | Cos
                51. | Tan
                52. | Sinh
                53. | Cosh
                54. | Tanh
                55. | Asin
                56. | Acos
                57. | Atan
                58. | Asinh
                59. | Acosh
                60. | Atanh
                61. | Min of bool * int
                62. | Max of bool * int
                63. | Sum of bool * int
                64. | SumReduce of int array
                65. | Signum
                66. | Sigmoid
                67. | Relu
                68. | Dawsn
                69. | Min'
                70. | Max'
                71. | Sum'
                72. | LogSumExp'
                73. | LogSumExp of bool * int
                74. | L1norm'
                75. | L2norm'
                76. | L2NormSqr'
                77. | ClipByValue
                78. | ClipByL2norm
                79. | Pow
                80. | ScalarPow
                81. | PowScalar
                82. | Atan2
                83. | ScalarAtan2
                84. | Atan2Scalar
                85. | Hypot
                86. | Min2
                87. | Max2
                88. | Add
                89. | Sub
                90. | Mul
                91. | Div
                92. | AddScalar
                93. | SubScalar
                94. | MulScalar
                95. | DivScalar
                96. | ScalarAdd
                97. | ScalarSub
                98. | ScalarMul
                99. | ScalarDiv
                100. | FMA
                101. | EltEqual
                102. | EltNotEqual
                103. | EltLess
                104. | EltGreater
                105. | EltLessEqual
                106. | EltGreaterEqual
                107. | EltEqualScalar
                108. | EltNotEqualScalar
                109. | EltLessScalar
                110. | EltGreaterScalar
                111. | EltLessEqualScalar
                112. | EltGreaterEqualScalar
                113. | Conv1d of Owl_types_common.padding * int array
                114. | Conv2d of Owl_types_common.padding * int array
                115. | Conv3d of Owl_types_common.padding * int array
                116. | TransposeConv1d of Owl_types_common.padding * int array
                117. | TransposeConv2d of Owl_types_common.padding * int array
                118. | TransposeConv3d of Owl_types_common.padding * int array
                119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                122. | MaxPool1d of Owl_types_common.padding * int array * int array
                123. | MaxPool2d of Owl_types_common.padding * int array * int array
                124. | MaxPool3d of Owl_types_common.padding * int array * int array
                125. | AvgPool1d of Owl_types_common.padding * int array * int array
                126. | AvgPool2d of Owl_types_common.padding * int array * int array
                127. | AvgPool3d of Owl_types_common.padding * int array * int array
                128. | UpSampling2d of int array
                129. | Conv1dBackwardInput of int array
                130. | Conv1dBackwardKernel of int array
                131. | Conv2dBackwardInput of int array
                132. | Conv2dBackwardKernel of int array
                133. | Conv3dBackwardInput of int array
                134. | Conv3dBackwardKernel of int array
                135. | TransposeConv1dBackwardInput of int array
                136. | TransposeConv1dBackwardKernel of int array
                137. | TransposeConv2dBackwardInput of int array
                138. | TransposeConv2dBackwardKernel of int array
                139. | TransposeConv3dBackwardInput of int array
                140. | TransposeConv3dBackwardKernel of int array
                141. | DilatedConv1dBackwardInput of int array * int array
                142. | DilatedConv1dBackwardKernel of int array * int array
                143. | DilatedConv2dBackwardInput of int array * int array
                144. | DilatedConv2dBackwardKernel of int array * int array
                145. | DilatedConv3dBackwardInput of int array * int array
                146. | DilatedConv3dBackwardKernel of int array * int array
                147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                153. | UpSampling2dBackward of int array
                154. | RowNum
                155. | ColNum
                156. | Row
                157. | Rows of int array
                158. | CopyRowTo
                159. | CopyColTo
                160. | Dot of bool * bool * elt * elt
                161. | Inv
                162. | Trace
                163. | Transpose of int array
                164. | ToRows
                165. | OfRows
                166. | Scalar_Add
                167. | Scalar_Sub
                168. | Scalar_Mul
                169. | Scalar_Div
                170. | Scalar_Pow
                171. | Scalar_Atan2
                172. | Scalar_Abs
                173. | Scalar_Neg
                174. | Scalar_Sqr
                175. | Scalar_Sqrt
                176. | Scalar_Exp
                177. | Scalar_Log
                178. | Scalar_Log2
                179. | Scalar_Log10
                180. | Scalar_Signum
                181. | Scalar_Floor
                182. | Scalar_Ceil
                183. | Scalar_Round
                184. | Scalar_Sin
                185. | Scalar_Cos
                186. | Scalar_Tan
                187. | Scalar_Sinh
                188. | Scalar_Cosh
                189. | Scalar_Tanh
                190. | Scalar_Asin
                191. | Scalar_Acos
                192. | Scalar_Atan
                193. | Scalar_Asinh
                194. | Scalar_Acosh
                195. | Scalar_Atanh
                196. | Scalar_Relu
                197. | Scalar_Dawsn
                198. | Scalar_Sigmoid
                199. | Fused_Adagrad of float * float
                  (*

                  TODO

                  *)
                diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/index.html index d5eceb235..9e0b0cd33 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape)

                Module Symbol.Shape

                Core functions
                val infer_shape : +Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol.Shape)

                Module Symbol.Shape

                Core functions
                val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                TODO

                diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/index.html index 3b6968065..c1aa76362 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol)

                Module Operator.Symbol

                Core functions
                val op_to_str : Shape.Type.op -> string

                TODO

                val is_random_variable : Shape.Type.op -> bool

                TODO

                val refnum : 'a Owl_graph.node -> int

                TODO

                val node_shape : Shape.Type.attr Owl_graph.node -> int array

                TODO

                val node_numel : Shape.Type.attr Owl_graph.node -> int

                TODO

                val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                TODO

                val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                TODO

                val shape_to_str : int array option array -> string

                TODO

                val node_to_str : Shape.Type.attr Owl_graph.node -> string

                TODO

                val node_to_arr : Shape.Type.t -> Shape.Type.arr

                TODO

                val arr_to_node : Shape.Type.arr -> Shape.Type.t

                TODO

                val node_to_elt : Shape.Type.t -> Shape.Type.elt

                TODO

                val elt_to_node : Shape.Type.elt -> Shape.Type.t

                TODO

                val make_node : +Symbol (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator.Symbol)

                Module Operator.Symbol

                Core functions
                val op_to_str : Shape.Type.op -> string

                TODO

                val is_random_variable : Shape.Type.op -> bool

                TODO

                val refnum : 'a Owl_graph.node -> int

                TODO

                val node_shape : Shape.Type.attr Owl_graph.node -> int array

                TODO

                val node_numel : Shape.Type.attr Owl_graph.node -> int

                TODO

                val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                TODO

                val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                TODO

                val shape_to_str : int array option array -> string

                TODO

                val node_to_str : Shape.Type.attr Owl_graph.node -> string

                TODO

                val node_to_arr : Shape.Type.t -> Shape.Type.arr

                TODO

                val arr_to_node : Shape.Type.arr -> Shape.Type.t

                TODO

                val node_to_elt : Shape.Type.t -> Shape.Type.elt

                TODO

                val elt_to_node : Shape.Type.elt -> Shape.Type.t

                TODO

                val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/index.html index 6701b11a3..42ecd8301 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator)

                Module Flatten_Sig.Operator

                Vectorised functions
                val empty : int array -> Symbol.Shape.Type.arr

                TODO

                val zeros : int array -> Symbol.Shape.Type.arr

                TODO

                val ones : int array -> Symbol.Shape.Type.arr

                TODO

                val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                TODO

                val sequential : +Operator (owl-base.Owl_computation_engine_sig.Flatten_Sig.Operator)

                Module Flatten_Sig.Operator

                Vectorised functions

                noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                val empty : int array -> Symbol.Shape.Type.arr

                empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                val zeros : int array -> Symbol.Shape.Type.arr

                zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                val ones : int array -> Symbol.Shape.Type.arr

                ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                TODO

                val uniform : + Symbol.Shape.Type.arr

                sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                TODO

                val gaussian : + Symbol.Shape.Type.arr

                uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                TODO

                val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                TODO

                val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                TODO

                val init_nd : + Symbol.Shape.Type.arr

                gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                TODO

                val shape : Symbol.Shape.Type.arr -> int array

                TODO

                val numel : Symbol.Shape.Type.arr -> int

                TODO

                TODO

                val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                TODO

                val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                TODO

                val set_slice : + Symbol.Shape.Type.arr

                init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                val shape : Symbol.Shape.Type.arr -> int array

                shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                val numel : Symbol.Shape.Type.arr -> int

                numel arr returns the total number of elements in the array arr.

                get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                TODO

                val get_fancy : + unit

                set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                val set_fancy : + Symbol.Shape.Type.arr

                get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                TODO

                val copy_ : out:'a -> 'b -> 'c

                TODO

                val reset : Symbol.Shape.Type.arr -> unit

                TODO

                val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                TODO

                val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                TODO

                val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                TODO

                val pad : + unit

                set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                val copy_ : out:'a -> 'b -> 'c

                copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                val reset : Symbol.Shape.Type.arr -> unit

                reset arr sets all elements of the array arr to zero.

                val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                TODO

                val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                TODO

                val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                TODO

                val concatenate : + Symbol.Shape.Type.arr

                pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                TODO

                val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                TODO

                val concat : + Symbol.Shape.Type.arr

                concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                val split : ?axis:int -> 'a -> 'b -> 'c

                TODO

                concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                val split : ?axis:int -> 'a -> 'b -> 'c

                split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                TODO

                val map : + Symbol.Shape.Type.arr * 'a array

                draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                TODO

                val delay : + Symbol.Shape.Type.arr

                scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                TODO

                lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                • max_row is an optional parameter specifying the maximum number of rows to print.
                • max_col is an optional parameter specifying the maximum number of columns to print.
                • header is an optional parameter to include a header in the output.
                • fmt is an optional parameter to specify the format of the output.

                abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                neg arr negates each element in the array arr. Returns a new array with each element negated.

                floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                • axis specifies the axis along which to compute the minimum.
                • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                • axis specifies the axis along which to compute the maximum.
                • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val sum_reduce : + Symbol.Shape.Type.arr

                sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                • axis specifies the axis along which to compute the sum.
                • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val log_sum_exp : + Symbol.Shape.Type.arr

                sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val clip_by_value : + Symbol.Shape.Type.arr

                log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                val clip_by_l2norm : + Symbol.Shape.Type.arr

                clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                • amin specifies the minimum value to clip to.
                • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                val scalar_pow : + Symbol.Shape.Type.arr

                pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                val pow_scalar : + Symbol.Shape.Type.arr

                scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                val atan2 : + Symbol.Shape.Type.arr

                pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                val scalar_atan2 : + Symbol.Shape.Type.arr

                atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                val atan2_scalar : + Symbol.Shape.Type.arr

                scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                val hypot : + Symbol.Shape.Type.arr

                atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                val add_scalar : + Symbol.Shape.Type.arr

                div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                val sub_scalar : + Symbol.Shape.Type.arr

                add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                val mul_scalar : + Symbol.Shape.Type.arr

                sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                val div_scalar : + Symbol.Shape.Type.arr

                mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                val scalar_add : + Symbol.Shape.Type.arr

                div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                val scalar_sub : + Symbol.Shape.Type.arr

                scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                val scalar_mul : + Symbol.Shape.Type.arr

                scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                val scalar_div : + Symbol.Shape.Type.arr

                scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                val elt_equal : + Symbol.Shape.Type.arr

                fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                val elt_not_equal : + Symbol.Shape.Type.arr

                elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                val elt_less : + Symbol.Shape.Type.arr

                elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                val elt_greater : + Symbol.Shape.Type.arr

                elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                val elt_less_equal : + Symbol.Shape.Type.arr

                elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                val elt_greater_equal : + Symbol.Shape.Type.arr

                elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                val elt_equal_scalar : + Symbol.Shape.Type.arr

                elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                val elt_less_scalar : + Symbol.Shape.Type.arr

                elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                val elt_greater_scalar : + Symbol.Shape.Type.arr

                elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                TODO

                val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                TODO

                val conv1d : + Symbol.Shape.Type.arr

                elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                val conv2d : + Symbol.Shape.Type.arr

                conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                • padding specifies the padding strategy (default is "valid").
                • strides specifies the stride length. Returns a new array with the result of the convolution.
                val conv3d : + Symbol.Shape.Type.arr

                conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                • padding specifies the padding strategy (default is "valid").
                • strides specifies the stride length. Returns a new array with the result of the convolution.
                val transpose_conv1d : + Symbol.Shape.Type.arr

                conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                • padding specifies the padding strategy (default is "valid").
                • strides specifies the stride length. Returns a new array with the result of the convolution.
                val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                TODO

                val transpose_conv2d : + Symbol.Shape.Type.arr

                transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                • padding specifies the padding strategy (default is "valid").
                • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                TODO

                val transpose_conv3d : + Symbol.Shape.Type.arr

                transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                • padding specifies the padding strategy (default is "valid").
                • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                TODO

                val dilated_conv1d : + Symbol.Shape.Type.arr

                transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                • padding specifies the padding strategy (default is "valid").
                • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val dilated_conv2d : + Symbol.Shape.Type.arr

                dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                • padding specifies the padding strategy (default is "valid").
                • strides specifies the stride length.
                • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val dilated_conv3d : + Symbol.Shape.Type.arr

                dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                • padding specifies the padding strategy (default is "valid").
                • strides specifies the stride length.
                • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val max_pool1d : + Symbol.Shape.Type.arr

                dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                • padding specifies the padding strategy (default is "valid").
                • strides specifies the stride length.
                • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val max_pool2d : + Symbol.Shape.Type.arr

                max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                • padding specifies the padding strategy (default is "valid").
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length. Returns a new array with the result of the max pooling.
                val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val max_pool3d : + Symbol.Shape.Type.arr

                max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                • padding specifies the padding strategy (default is "valid").
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length. Returns a new array with the result of the max pooling.
                val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val avg_pool1d : + Symbol.Shape.Type.arr

                max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                • padding specifies the padding strategy (default is "valid").
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length. Returns a new array with the result of the max pooling.
                val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val avg_pool2d : + Symbol.Shape.Type.arr

                avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                • padding specifies the padding strategy (default is "valid").
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length. Returns a new array with the result of the average pooling.
                val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val avg_pool3d : + Symbol.Shape.Type.arr

                avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                • padding specifies the padding strategy (default is "valid").
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length. Returns a new array with the result of the average pooling.
                val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                TODO

                val conv1d_backward_input : + Symbol.Shape.Type.arr

                avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                • padding specifies the padding strategy (default is "valid").
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length. Returns a new array with the result of the average pooling.
                val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                upsampling2d input size performs a 2-dimensional upsampling on the input array.

                • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                TODO

                val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                • input is the original input array.
                • kernel is the convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val conv2d_backward_input : + Symbol.Shape.Type.arr

                conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                • input is the original input array.
                • kernel is the convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                TODO

                val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                • input is the original input array.
                • kernel is the convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val conv3d_backward_input : + Symbol.Shape.Type.arr

                conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                • input is the original input array.
                • kernel is the convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                TODO

                val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                • input is the original input array.
                • kernel is the convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                • input is the original input array.
                • kernel is the convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                • input is the original input array.
                • kernel is the transposed convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                • input is the original input array.
                • kernel is the transposed convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                • input is the original input array.
                • kernel is the transposed convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                • input is the original input array.
                • kernel is the transposed convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                • input is the original input array.
                • kernel is the transposed convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                • input is the original input array.
                • kernel is the transposed convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                • input is the original input array.
                • kernel is the dilated convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • dilations specifies the dilation rate.
                • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                • input is the original input array.
                • kernel is the dilated convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • dilations specifies the dilation rate.
                • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                • input is the original input array.
                • kernel is the dilated convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • dilations specifies the dilation rate.
                • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                • input is the original input array.
                • kernel is the dilated convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • dilations specifies the dilation rate.
                • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                • input is the original input array.
                • kernel is the dilated convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • dilations specifies the dilation rate.
                • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val max_pool1d_backward : + Symbol.Shape.Type.arr

                dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                • input is the original input array.
                • kernel is the dilated convolutional kernel used during the forward pass.
                • strides specifies the stride length.
                • dilations specifies the dilation rate.
                • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val max_pool2d_backward : + Symbol.Shape.Type.arr

                max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                • padding specifies the padding strategy used during the forward pass.
                • input is the original input array.
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val max_pool3d_backward : + Symbol.Shape.Type.arr

                max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                • padding specifies the padding strategy used during the forward pass.
                • input is the original input array.
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val avg_pool1d_backward : + Symbol.Shape.Type.arr

                max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                • padding specifies the padding strategy used during the forward pass.
                • input is the original input array.
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val avg_pool2d_backward : + Symbol.Shape.Type.arr

                avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                • padding specifies the padding strategy used during the forward pass.
                • input is the original input array.
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val avg_pool3d_backward : + Symbol.Shape.Type.arr

                avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                • padding specifies the padding strategy used during the forward pass.
                • input is the original input array.
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val upsampling2d_backward : + Symbol.Shape.Type.arr

                avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                • padding specifies the padding strategy used during the forward pass.
                • input is the original input array.
                • pool_size specifies the size of the pooling window.
                • strides specifies the stride length.
                • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val row_num : Symbol.Shape.Type.arr -> int

                TODO

                val col_num : Symbol.Shape.Type.arr -> int

                TODO

                val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                TODO

                val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                TODO

                val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                TODO

                TODO

                upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                • input is the original input array.
                • size specifies the upsampling factors for each dimension.
                • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                val row_num : Symbol.Shape.Type.arr -> int

                row_num arr returns the number of rows in the array arr.

                val col_num : Symbol.Shape.Type.arr -> int

                col_num arr returns the number of columns in the array arr.

                row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                val transpose : + Symbol.Shape.Type.arr

                dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val to_rows : Symbol.Shape.Type.arr -> 'a array

                TODO

                TODO

                val to_cols : Symbol.Shape.Type.arr -> 'a array

                TODO

                TODO

                val of_array : + Symbol.Shape.Type.arr

                transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                val to_rows : Symbol.Shape.Type.arr -> 'a array

                to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                val to_cols : Symbol.Shape.Type.arr -> 'a array

                to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                TODO

                val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                TODO

                val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                TODO

                Scalar functions
                module Scalar : sig ... end
                module Mat : sig ... end
                module Linalg : sig ... end
                + Symbol.Shape.Type.arr

                of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                Scalar functions
                module Scalar : sig ... end
                module Mat : sig ... end
                module Linalg : sig ... end
                diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Linalg/index.html index d8632c007..d1873780f 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Linalg)

                Module Operator.Linalg

                val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                TODO

                val svd : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Linalg)

                Module Operator.Linalg

                inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

                logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

                val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

                • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

                qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

                lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

                svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

                • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
                val lyapunov : + Symbol.Shape.Type.arr

                sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

                val discrete_lyapunov : + Symbol.Shape.Type.arr

                lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

                val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                val linsolve : + Symbol.Shape.Type.arr

                discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

                • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
                val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                TODO

                linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

                • trans specifies whether to transpose the matrix A.
                • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

                care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

                • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                + Symbol.Shape.Type.arr

                dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

                • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Mat/index.html index 06d2b1e21..a8e366006 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Mat)

                Module Operator.Mat

                val eye : int -> Symbol.Shape.Type.arr

                TODO

                TODO

                TODO

                TODO

                +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Mat)

                Module Operator.Mat

                val eye : int -> Symbol.Shape.Type.arr

                eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

                diagm ?k v creates a diagonal matrix from the array v.

                • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

                triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

                tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

                diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Scalar/index.html index 315528d96..d697242de 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Scalar)

                Module Operator.Scalar

                val add : +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Scalar)

                Module Operator.Scalar

                add a b returns the sum of the scalars a and b.

                sub a b returns the difference of the scalars a and b.

                mul a b returns the product of the scalars a and b.

                div a b returns the quotient of the scalars a and b.

                val atan2 : + Symbol.Shape.Type.elt

                pow a b returns the scalar a raised to the power of b.

                + Symbol.Shape.Type.elt

                atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                abs a returns the absolute value of the scalar a.

                neg a returns the negation of the scalar a.

                sqr a returns the square of the scalar a.

                sqrt a returns the square root of the scalar a.

                exp a returns the exponential of the scalar a.

                log a returns the natural logarithm of the scalar a.

                log2 a returns the base-2 logarithm of the scalar a.

                log10 a returns the base-10 logarithm of the scalar a.

                signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                floor a returns the greatest integer less than or equal to the scalar a.

                ceil a returns the smallest integer greater than or equal to the scalar a.

                round a returns the nearest integer to the scalar a.

                sin a returns the sine of the scalar a.

                cos a returns the cosine of the scalar a.

                tan a returns the tangent of the scalar a.

                sinh a returns the hyperbolic sine of the scalar a.

                cosh a returns the hyperbolic cosine of the scalar a.

                tanh a returns the hyperbolic tangent of the scalar a.

                asin a returns the arcsine of the scalar a.

                acos a returns the arccosine of the scalar a.

                atan a returns the arctangent of the scalar a.

                asinh a returns the inverse hyperbolic sine of the scalar a.

                acosh a returns the inverse hyperbolic cosine of the scalar a.

                atanh a returns the inverse hyperbolic tangent of the scalar a.

                relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                dawsn a returns Dawson's function of the scalar a.

                sigmoid a returns the sigmoid function of the scalar a.

                diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 48922e91f..53f462144 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                Module A.Linalg

                val inv : arr -> arr
                val logdet : arr -> elt
                val chol : ?upper:bool -> arr -> arr
                val svd : ?thin:bool -> arr -> arr * arr * arr
                val qr : arr -> arr * arr
                val lq : arr -> arr * arr
                val sylvester : arr -> arr -> arr -> arr
                val lyapunov : arr -> arr -> arr
                val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                Module A.Linalg

                val inv : arr -> arr
                val logdet : arr -> elt
                val chol : ?upper:bool -> arr -> arr
                val svd : ?thin:bool -> arr -> arr * arr * arr
                val qr : arr -> arr * arr
                val lq : arr -> arr * arr
                val sylvester : arr -> arr -> arr -> arr
                val lyapunov : arr -> arr -> arr
                val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index d3d3ad9dc..320c5d21e 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                Module A.Mat

                val diagm : ?k:int -> arr -> arr
                val triu : ?k:int -> arr -> arr
                val tril : ?k:int -> arr -> arr
                val eye : int -> arr
                +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                Module A.Mat

                val diagm : ?k:int -> arr -> arr
                val triu : ?k:int -> arr -> arr
                val tril : ?k:int -> arr -> arr
                val eye : int -> arr
                diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index ef4ff16ac..ab331a68d 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                Module A.Scalar

                val add : elt -> elt -> elt
                val sub : elt -> elt -> elt
                val mul : elt -> elt -> elt
                val div : elt -> elt -> elt
                val pow : elt -> elt -> elt
                val atan2 : elt -> elt -> elt
                val abs : elt -> elt
                val neg : elt -> elt
                val sqr : elt -> elt
                val sqrt : elt -> elt
                val exp : elt -> elt
                val log : elt -> elt
                val log2 : elt -> elt
                val log10 : elt -> elt
                val signum : elt -> elt
                val floor : elt -> elt
                val ceil : elt -> elt
                val round : elt -> elt
                val sin : elt -> elt
                val cos : elt -> elt
                val tan : elt -> elt
                val sinh : elt -> elt
                val cosh : elt -> elt
                val tanh : elt -> elt
                val asin : elt -> elt
                val acos : elt -> elt
                val atan : elt -> elt
                val asinh : elt -> elt
                val acosh : elt -> elt
                val atanh : elt -> elt
                val relu : elt -> elt
                val dawsn : elt -> elt
                val sigmoid : elt -> elt
                +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                Module A.Scalar

                val add : elt -> elt -> elt
                val sub : elt -> elt -> elt
                val mul : elt -> elt -> elt
                val div : elt -> elt -> elt
                val pow : elt -> elt -> elt
                val atan2 : elt -> elt -> elt
                val abs : elt -> elt
                val neg : elt -> elt
                val sqr : elt -> elt
                val sqrt : elt -> elt
                val exp : elt -> elt
                val log : elt -> elt
                val log2 : elt -> elt
                val log10 : elt -> elt
                val signum : elt -> elt
                val floor : elt -> elt
                val ceil : elt -> elt
                val round : elt -> elt
                val sin : elt -> elt
                val cos : elt -> elt
                val tan : elt -> elt
                val sinh : elt -> elt
                val cosh : elt -> elt
                val tanh : elt -> elt
                val asin : elt -> elt
                val acos : elt -> elt
                val atan : elt -> elt
                val asinh : elt -> elt
                val acosh : elt -> elt
                val atanh : elt -> elt
                val relu : elt -> elt
                val dawsn : elt -> elt
                val sigmoid : elt -> elt
                diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index 555fbf84b..fb1fadb9f 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                Module Device.A

                include Owl_types_ndarray_algodiff.Sig
                include Owl_types_ndarray_eltcmp.Sig
                include Owl_types_ndarray_basic.Sig
                type arr
                type elt
                val empty : int array -> arr
                val zeros : int array -> arr
                val ones : int array -> arr
                val create : int array -> elt -> arr
                val sequential : ?a:elt -> ?step:elt -> int array -> arr
                val uniform : ?a:elt -> ?b:elt -> int array -> arr
                val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                val bernoulli : ?p:elt -> int array -> arr
                val init : int array -> (int -> elt) -> arr
                val init_nd : int array -> (int array -> elt) -> arr
                val shape : arr -> int array
                val numel : arr -> int
                val get : arr -> int array -> elt
                val set : arr -> int array -> elt -> unit
                val get_slice : int list list -> arr -> arr
                val set_slice : int list list -> arr -> arr -> unit
                val get_fancy : Owl_types_common.index list -> arr -> arr
                val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                val copy : arr -> arr
                val copy_ : out:arr -> arr -> unit
                val reset : arr -> unit
                val reshape : arr -> int array -> arr
                val reverse : arr -> arr
                val tile : arr -> int array -> arr
                val repeat : arr -> int array -> arr
                val concatenate : ?axis:int -> arr array -> arr
                val stack : ?axis:int -> arr array -> arr
                val split : ?axis:int -> int array -> arr -> arr array
                val expand : ?hi:bool -> arr -> int -> arr
                val squeeze : ?axis:int array -> arr -> arr
                val draw : ?axis:int -> arr -> int -> arr * int array
                val map : (elt -> elt) -> arr -> arr
                val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                val one_hot : int -> arr -> arr
                val pad : ?v:elt -> int list list -> arr -> arr
                val print : +A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                Module Device.A

                include Owl_types_ndarray_algodiff.Sig
                include Owl_types_ndarray_eltcmp.Sig
                include Owl_types_ndarray_basic.Sig
                type arr
                type elt
                val empty : int array -> arr
                val zeros : int array -> arr
                val ones : int array -> arr
                val create : int array -> elt -> arr
                val sequential : ?a:elt -> ?step:elt -> int array -> arr
                val uniform : ?a:elt -> ?b:elt -> int array -> arr
                val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                val bernoulli : ?p:elt -> int array -> arr
                val init : int array -> (int -> elt) -> arr
                val init_nd : int array -> (int array -> elt) -> arr
                val shape : arr -> int array
                val numel : arr -> int
                val get : arr -> int array -> elt
                val set : arr -> int array -> elt -> unit
                val get_slice : int list list -> arr -> arr
                val set_slice : int list list -> arr -> arr -> unit
                val get_fancy : Owl_types_common.index list -> arr -> arr
                val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                val copy : arr -> arr
                val copy_ : out:arr -> arr -> unit
                val reset : arr -> unit
                val reshape : arr -> int array -> arr
                val reverse : arr -> arr
                val tile : arr -> int array -> arr
                val repeat : arr -> int array -> arr
                val concatenate : ?axis:int -> arr array -> arr
                val stack : ?axis:int -> arr array -> arr
                val split : ?axis:int -> int array -> arr -> arr array
                val expand : ?hi:bool -> arr -> int -> arr
                val squeeze : ?axis:int array -> arr -> arr
                val draw : ?axis:int -> arr -> int -> arr * int array
                val map : (elt -> elt) -> arr -> arr
                val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                val one_hot : int -> arr -> arr
                val pad : ?v:elt -> int list list -> arr -> arr
                val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index 45a2329f1..fed50578a 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device)

                Module Type.Device

                Type definition
                type device

                TODO

                type value

                TODO

                Core functions
                val make_device : unit -> device

                TODO

                val arr_to_value : A.arr -> value

                TODO

                val value_to_arr : value -> A.arr

                TODO

                val elt_to_value : A.elt -> value

                TODO

                val value_to_elt : value -> A.elt

                TODO

                val value_to_float : value -> float

                TODO

                val is_arr : value -> bool

                TODO

                val is_elt : value -> bool

                TODO

                +Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type.Device)

                Module Type.Device

                Type definition
                type device

                TODO

                type value

                TODO

                Core functions
                val make_device : unit -> device

                TODO

                val arr_to_value : A.arr -> value

                TODO

                val value_to_arr : value -> A.arr

                TODO

                val elt_to_value : A.elt -> value

                TODO

                val value_to_elt : value -> A.elt

                TODO

                val value_to_float : value -> float

                TODO

                val is_arr : value -> bool

                TODO

                val is_elt : value -> bool

                TODO

                diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/index.html index ae6be6f9a..870905652 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type)

                Module Shape.Type

                Type definition
                type state =
                1. | Valid
                2. | Invalid
                  (*

                  TODO

                  *)

                TODO

                and block = {
                1. size : int;
                2. block_id : int;
                3. mutable active : t option;
                4. mutable memory : Device.value;
                5. mutable nodes : t list;
                }

                block type keeps a reference to a block of memory and to the nodes sharing that block.

                and attr = {
                1. mutable op : op;
                2. mutable freeze : bool;
                3. mutable reuse : bool;
                4. mutable state : state;
                5. mutable shape : int array option array;
                6. mutable value : Device.value array;
                7. mutable block : block array option;
                }

                TODO

                and arr =
                1. | Arr of t
                and elt =
                1. | Elt of t
                and op =
                1. | Noop
                2. | Var
                3. | Const
                4. | Empty of int array
                5. | Zeros of int array
                6. | Ones of int array
                7. | Create of int array
                8. | Sequential of int array
                9. | Uniform of int array
                10. | Gaussian of int array
                11. | Bernoulli of int array
                12. | Init of int array * int -> elt
                13. | Get of int array
                14. | Set of int array
                15. | GetSlice of int list list
                16. | SetSlice of int list list
                17. | GetFancy of Owl_types_common.index list
                18. | SetFancy of Owl_types_common.index list
                19. | Copy
                20. | Reset
                21. | Reshape of int array
                22. | Reverse
                23. | Tile of int array
                24. | Repeat of int array
                25. | Pad of elt * int list list
                26. | Concatenate of int
                27. | Stack of int
                28. | Split of int * int array
                29. | Draw of int * int
                30. | Map of elt -> elt
                31. | Fold of int * elt -> elt -> elt
                32. | Scan of int * elt -> elt -> elt
                33. | OneHot of int
                34. | OfArray of int array
                35. | Delay of Device.A.arr -> Device.A.arr
                36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                37. | LazyPrint of int option +Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape.Type)

                  Module Shape.Type

                  Type definition
                  type state =
                  1. | Valid
                  2. | Invalid
                    (*

                    TODO

                    *)

                  TODO

                  and block = {
                  1. size : int;
                  2. block_id : int;
                  3. mutable active : t option;
                  4. mutable memory : Device.value;
                  5. mutable nodes : t list;
                  }

                  block type keeps a reference to a block of memory and to the nodes sharing that block.

                  and attr = {
                  1. mutable op : op;
                  2. mutable freeze : bool;
                  3. mutable reuse : bool;
                  4. mutable state : state;
                  5. mutable shape : int array option array;
                  6. mutable value : Device.value array;
                  7. mutable block : block array option;
                  }

                  TODO

                  and arr =
                  1. | Arr of t
                  and elt =
                  1. | Elt of t
                  and op =
                  1. | Noop
                  2. | Var
                  3. | Const
                  4. | Empty of int array
                  5. | Zeros of int array
                  6. | Ones of int array
                  7. | Create of int array
                  8. | Sequential of int array
                  9. | Uniform of int array
                  10. | Gaussian of int array
                  11. | Bernoulli of int array
                  12. | Init of int array * int -> elt
                  13. | Get of int array
                  14. | Set of int array
                  15. | GetSlice of int list list
                  16. | SetSlice of int list list
                  17. | GetFancy of Owl_types_common.index list
                  18. | SetFancy of Owl_types_common.index list
                  19. | Copy
                  20. | Reset
                  21. | Reshape of int array
                  22. | Reverse
                  23. | Tile of int array
                  24. | Repeat of int array
                  25. | Pad of elt * int list list
                  26. | Concatenate of int
                  27. | Stack of int
                  28. | Split of int * int array
                  29. | Draw of int * int
                  30. | Map of elt -> elt
                  31. | Fold of int * elt -> elt -> elt
                  32. | Scan of int * elt -> elt -> elt
                  33. | OneHot of int
                  34. | OfArray of int array
                  35. | Delay of Device.A.arr -> Device.A.arr
                  36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                  37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                  38. | Abs
                  39. | Neg
                  40. | Floor
                  41. | Ceil
                  42. | Round
                  43. | Sqr
                  44. | Sqrt
                  45. | Log
                  46. | Log2
                  47. | Log10
                  48. | Exp
                  49. | Sin
                  50. | Cos
                  51. | Tan
                  52. | Sinh
                  53. | Cosh
                  54. | Tanh
                  55. | Asin
                  56. | Acos
                  57. | Atan
                  58. | Asinh
                  59. | Acosh
                  60. | Atanh
                  61. | Min of bool * int
                  62. | Max of bool * int
                  63. | Sum of bool * int
                  64. | SumReduce of int array
                  65. | Signum
                  66. | Sigmoid
                  67. | Relu
                  68. | Dawsn
                  69. | Min'
                  70. | Max'
                  71. | Sum'
                  72. | LogSumExp'
                  73. | LogSumExp of bool * int
                  74. | L1norm'
                  75. | L2norm'
                  76. | L2NormSqr'
                  77. | ClipByValue
                  78. | ClipByL2norm
                  79. | Pow
                  80. | ScalarPow
                  81. | PowScalar
                  82. | Atan2
                  83. | ScalarAtan2
                  84. | Atan2Scalar
                  85. | Hypot
                  86. | Min2
                  87. | Max2
                  88. | Add
                  89. | Sub
                  90. | Mul
                  91. | Div
                  92. | AddScalar
                  93. | SubScalar
                  94. | MulScalar
                  95. | DivScalar
                  96. | ScalarAdd
                  97. | ScalarSub
                  98. | ScalarMul
                  99. | ScalarDiv
                  100. | FMA
                  101. | EltEqual
                  102. | EltNotEqual
                  103. | EltLess
                  104. | EltGreater
                  105. | EltLessEqual
                  106. | EltGreaterEqual
                  107. | EltEqualScalar
                  108. | EltNotEqualScalar
                  109. | EltLessScalar
                  110. | EltGreaterScalar
                  111. | EltLessEqualScalar
                  112. | EltGreaterEqualScalar
                  113. | Conv1d of Owl_types_common.padding * int array
                  114. | Conv2d of Owl_types_common.padding * int array
                  115. | Conv3d of Owl_types_common.padding * int array
                  116. | TransposeConv1d of Owl_types_common.padding * int array
                  117. | TransposeConv2d of Owl_types_common.padding * int array
                  118. | TransposeConv3d of Owl_types_common.padding * int array
                  119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                  120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                  121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                  122. | MaxPool1d of Owl_types_common.padding * int array * int array
                  123. | MaxPool2d of Owl_types_common.padding * int array * int array
                  124. | MaxPool3d of Owl_types_common.padding * int array * int array
                  125. | AvgPool1d of Owl_types_common.padding * int array * int array
                  126. | AvgPool2d of Owl_types_common.padding * int array * int array
                  127. | AvgPool3d of Owl_types_common.padding * int array * int array
                  128. | UpSampling2d of int array
                  129. | Conv1dBackwardInput of int array
                  130. | Conv1dBackwardKernel of int array
                  131. | Conv2dBackwardInput of int array
                  132. | Conv2dBackwardKernel of int array
                  133. | Conv3dBackwardInput of int array
                  134. | Conv3dBackwardKernel of int array
                  135. | TransposeConv1dBackwardInput of int array
                  136. | TransposeConv1dBackwardKernel of int array
                  137. | TransposeConv2dBackwardInput of int array
                  138. | TransposeConv2dBackwardKernel of int array
                  139. | TransposeConv3dBackwardInput of int array
                  140. | TransposeConv3dBackwardKernel of int array
                  141. | DilatedConv1dBackwardInput of int array * int array
                  142. | DilatedConv1dBackwardKernel of int array * int array
                  143. | DilatedConv2dBackwardInput of int array * int array
                  144. | DilatedConv2dBackwardKernel of int array * int array
                  145. | DilatedConv3dBackwardInput of int array * int array
                  146. | DilatedConv3dBackwardKernel of int array * int array
                  147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                  148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                  149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                  150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                  151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                  152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                  153. | UpSampling2dBackward of int array
                  154. | RowNum
                  155. | ColNum
                  156. | Row
                  157. | Rows of int array
                  158. | CopyRowTo
                  159. | CopyColTo
                  160. | Dot of bool * bool * elt * elt
                  161. | Inv
                  162. | Trace
                  163. | Transpose of int array
                  164. | ToRows
                  165. | OfRows
                  166. | Scalar_Add
                  167. | Scalar_Sub
                  168. | Scalar_Mul
                  169. | Scalar_Div
                  170. | Scalar_Pow
                  171. | Scalar_Atan2
                  172. | Scalar_Abs
                  173. | Scalar_Neg
                  174. | Scalar_Sqr
                  175. | Scalar_Sqrt
                  176. | Scalar_Exp
                  177. | Scalar_Log
                  178. | Scalar_Log2
                  179. | Scalar_Log10
                  180. | Scalar_Signum
                  181. | Scalar_Floor
                  182. | Scalar_Ceil
                  183. | Scalar_Round
                  184. | Scalar_Sin
                  185. | Scalar_Cos
                  186. | Scalar_Tan
                  187. | Scalar_Sinh
                  188. | Scalar_Cosh
                  189. | Scalar_Tanh
                  190. | Scalar_Asin
                  191. | Scalar_Acos
                  192. | Scalar_Atan
                  193. | Scalar_Asinh
                  194. | Scalar_Acosh
                  195. | Scalar_Atanh
                  196. | Scalar_Relu
                  197. | Scalar_Dawsn
                  198. | Scalar_Sigmoid
                  199. | Fused_Adagrad of float * float
                    (*

                    TODO

                    *)
                  diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/index.html index 47e343fc4..119419d2f 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape)

                  Module Symbol.Shape

                  Core functions
                  val infer_shape : +Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol.Shape)

                  Module Symbol.Shape

                  Core functions
                  val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                  TODO

                  diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/index.html index 2302d4ff5..e75127b8c 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol)

                  Module Operator.Symbol

                  Core functions
                  val op_to_str : Shape.Type.op -> string

                  TODO

                  val is_random_variable : Shape.Type.op -> bool

                  TODO

                  val refnum : 'a Owl_graph.node -> int

                  TODO

                  val node_shape : Shape.Type.attr Owl_graph.node -> int array

                  TODO

                  val node_numel : Shape.Type.attr Owl_graph.node -> int

                  TODO

                  val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                  TODO

                  val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                  TODO

                  val shape_to_str : int array option array -> string

                  TODO

                  val node_to_str : Shape.Type.attr Owl_graph.node -> string

                  TODO

                  val node_to_arr : Shape.Type.t -> Shape.Type.arr

                  TODO

                  val arr_to_node : Shape.Type.arr -> Shape.Type.t

                  TODO

                  val node_to_elt : Shape.Type.t -> Shape.Type.elt

                  TODO

                  val elt_to_node : Shape.Type.elt -> Shape.Type.t

                  TODO

                  val make_node : +Symbol (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator.Symbol)

                  Module Operator.Symbol

                  Core functions
                  val op_to_str : Shape.Type.op -> string

                  TODO

                  val is_random_variable : Shape.Type.op -> bool

                  TODO

                  val refnum : 'a Owl_graph.node -> int

                  TODO

                  val node_shape : Shape.Type.attr Owl_graph.node -> int array

                  TODO

                  val node_numel : Shape.Type.attr Owl_graph.node -> int

                  TODO

                  val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                  TODO

                  val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                  TODO

                  val shape_to_str : int array option array -> string

                  TODO

                  val node_to_str : Shape.Type.attr Owl_graph.node -> string

                  TODO

                  val node_to_arr : Shape.Type.t -> Shape.Type.arr

                  TODO

                  val arr_to_node : Shape.Type.arr -> Shape.Type.t

                  TODO

                  val node_to_elt : Shape.Type.t -> Shape.Type.elt

                  TODO

                  val elt_to_node : Shape.Type.elt -> Shape.Type.t

                  TODO

                  val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/index.html index 9796695e8..c296009b4 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator)

                  Module Optimiser.Operator

                  Vectorised functions
                  val empty : int array -> Symbol.Shape.Type.arr

                  TODO

                  val zeros : int array -> Symbol.Shape.Type.arr

                  TODO

                  val ones : int array -> Symbol.Shape.Type.arr

                  TODO

                  val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                  TODO

                  val sequential : +Operator (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser.Operator)

                  Module Optimiser.Operator

                  Vectorised functions

                  noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                  val empty : int array -> Symbol.Shape.Type.arr

                  empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                  val zeros : int array -> Symbol.Shape.Type.arr

                  zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                  val ones : int array -> Symbol.Shape.Type.arr

                  ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                  val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                  create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                  val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val uniform : + Symbol.Shape.Type.arr

                  sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                  val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val gaussian : + Symbol.Shape.Type.arr

                  uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                  val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                  TODO

                  val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                  TODO

                  val init_nd : + Symbol.Shape.Type.arr

                  gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                  val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                  bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                  val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                  init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                  val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                  TODO

                  val shape : Symbol.Shape.Type.arr -> int array

                  TODO

                  val numel : Symbol.Shape.Type.arr -> int

                  TODO

                  TODO

                  val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                  TODO

                  val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                  TODO

                  val set_slice : + Symbol.Shape.Type.arr

                  init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                  val shape : Symbol.Shape.Type.arr -> int array

                  shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                  val numel : Symbol.Shape.Type.arr -> int

                  numel arr returns the total number of elements in the array arr.

                  get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                  val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                  set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                  val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                  get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                  val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                  TODO

                  val get_fancy : + unit

                  set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                  val set_fancy : + Symbol.Shape.Type.arr

                  get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                  val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                  TODO

                  val copy_ : out:'a -> 'b -> 'c

                  TODO

                  val reset : Symbol.Shape.Type.arr -> unit

                  TODO

                  val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  TODO

                  val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  TODO

                  val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  TODO

                  val pad : + unit

                  set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                  copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                  val copy_ : out:'a -> 'b -> 'c

                  copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                  val reset : Symbol.Shape.Type.arr -> unit

                  reset arr sets all elements of the array arr to zero.

                  val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                  reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                  val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                  val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                  TODO

                  val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                  TODO

                  val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                  TODO

                  val concatenate : + Symbol.Shape.Type.arr

                  pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                  val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                  expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                  val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                  squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                  val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                  TODO

                  val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                  TODO

                  val concat : + Symbol.Shape.Type.arr

                  concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                  val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                  stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                  val split : ?axis:int -> 'a -> 'b -> 'c

                  TODO

                  concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                  val split : ?axis:int -> 'a -> 'b -> 'c

                  split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                  • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                  val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                  TODO

                  val map : + Symbol.Shape.Type.arr * 'a array

                  draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                  map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                  fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                  TODO

                  val delay : + Symbol.Shape.Type.arr

                  scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                  one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                  delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                  val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                  val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                  TODO

                  lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                  val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                  print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                  • max_row is an optional parameter specifying the maximum number of rows to print.
                  • max_col is an optional parameter specifying the maximum number of columns to print.
                  • header is an optional parameter to include a header in the output.
                  • fmt is an optional parameter to specify the format of the output.

                  abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                  neg arr negates each element in the array arr. Returns a new array with each element negated.

                  floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                  ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                  round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                  sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                  sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                  log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                  log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                  log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                  exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                  sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                  cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                  tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                  sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                  cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                  tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                  asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                  acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                  atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                  asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                  acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                  atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                  val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                  • axis specifies the axis along which to compute the minimum.
                  • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                  val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                  • axis specifies the axis along which to compute the maximum.
                  • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                  val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val sum_reduce : + Symbol.Shape.Type.arr

                  sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                  • axis specifies the axis along which to compute the sum.
                  • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                  val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val log_sum_exp : + Symbol.Shape.Type.arr

                  sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                  • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                  signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                  sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                  relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                  dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                  min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                  max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                  sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                  log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                  val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val clip_by_value : + Symbol.Shape.Type.arr

                  log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                  • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                  • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                  l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                  l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                  l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                  val clip_by_l2norm : + Symbol.Shape.Type.arr

                  clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                  • amin specifies the minimum value to clip to.
                  • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                  clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                  val scalar_pow : + Symbol.Shape.Type.arr

                  pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                  val pow_scalar : + Symbol.Shape.Type.arr

                  scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                  val atan2 : + Symbol.Shape.Type.arr

                  pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                  val scalar_atan2 : + Symbol.Shape.Type.arr

                  atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                  val atan2_scalar : + Symbol.Shape.Type.arr

                  scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                  val hypot : + Symbol.Shape.Type.arr

                  atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                  hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                  min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                  max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                  add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                  sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                  mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                  val add_scalar : + Symbol.Shape.Type.arr

                  div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                  val sub_scalar : + Symbol.Shape.Type.arr

                  add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                  val mul_scalar : + Symbol.Shape.Type.arr

                  sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                  val div_scalar : + Symbol.Shape.Type.arr

                  mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                  val scalar_add : + Symbol.Shape.Type.arr

                  div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                  val scalar_sub : + Symbol.Shape.Type.arr

                  scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                  val scalar_mul : + Symbol.Shape.Type.arr

                  scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                  val scalar_div : + Symbol.Shape.Type.arr

                  scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                  scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                  val elt_equal : + Symbol.Shape.Type.arr

                  fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                  val elt_not_equal : + Symbol.Shape.Type.arr

                  elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                  val elt_less : + Symbol.Shape.Type.arr

                  elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                  val elt_greater : + Symbol.Shape.Type.arr

                  elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                  val elt_less_equal : + Symbol.Shape.Type.arr

                  elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                  val elt_greater_equal : + Symbol.Shape.Type.arr

                  elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                  val elt_equal_scalar : + Symbol.Shape.Type.arr

                  elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                  val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                  elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                  val elt_less_scalar : + Symbol.Shape.Type.arr

                  elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                  val elt_greater_scalar : + Symbol.Shape.Type.arr

                  elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                  val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                  elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                  TODO

                  val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                  elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                  TODO

                  val conv1d : + Symbol.Shape.Type.arr

                  elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                  val conv2d : + Symbol.Shape.Type.arr

                  conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                  • padding specifies the padding strategy (default is "valid").
                  • strides specifies the stride length. Returns a new array with the result of the convolution.
                  val conv3d : + Symbol.Shape.Type.arr

                  conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                  • padding specifies the padding strategy (default is "valid").
                  • strides specifies the stride length. Returns a new array with the result of the convolution.
                  val transpose_conv1d : + Symbol.Shape.Type.arr

                  conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                  • padding specifies the padding strategy (default is "valid").
                  • strides specifies the stride length. Returns a new array with the result of the convolution.
                  val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val transpose_conv2d : + Symbol.Shape.Type.arr

                  transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                  • padding specifies the padding strategy (default is "valid").
                  • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                  val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val transpose_conv3d : + Symbol.Shape.Type.arr

                  transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                  • padding specifies the padding strategy (default is "valid").
                  • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                  val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val dilated_conv1d : + Symbol.Shape.Type.arr

                  transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                  • padding specifies the padding strategy (default is "valid").
                  • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                  val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val dilated_conv2d : + Symbol.Shape.Type.arr

                  dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                  • padding specifies the padding strategy (default is "valid").
                  • strides specifies the stride length.
                  • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                  val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val dilated_conv3d : + Symbol.Shape.Type.arr

                  dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                  • padding specifies the padding strategy (default is "valid").
                  • strides specifies the stride length.
                  • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                  val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val max_pool1d : + Symbol.Shape.Type.arr

                  dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                  • padding specifies the padding strategy (default is "valid").
                  • strides specifies the stride length.
                  • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                  val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val max_pool2d : + Symbol.Shape.Type.arr

                  max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                  • padding specifies the padding strategy (default is "valid").
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length. Returns a new array with the result of the max pooling.
                  val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val max_pool3d : + Symbol.Shape.Type.arr

                  max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                  • padding specifies the padding strategy (default is "valid").
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length. Returns a new array with the result of the max pooling.
                  val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val avg_pool1d : + Symbol.Shape.Type.arr

                  max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                  • padding specifies the padding strategy (default is "valid").
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length. Returns a new array with the result of the max pooling.
                  val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val avg_pool2d : + Symbol.Shape.Type.arr

                  avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                  • padding specifies the padding strategy (default is "valid").
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length. Returns a new array with the result of the average pooling.
                  val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val avg_pool3d : + Symbol.Shape.Type.arr

                  avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                  • padding specifies the padding strategy (default is "valid").
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length. Returns a new array with the result of the average pooling.
                  val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  TODO

                  val conv1d_backward_input : + Symbol.Shape.Type.arr

                  avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                  • padding specifies the padding strategy (default is "valid").
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length. Returns a new array with the result of the average pooling.
                  val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  upsampling2d input size performs a 2-dimensional upsampling on the input array.

                  • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                  TODO

                  val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                  conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                  • input is the original input array.
                  • kernel is the convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                  val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val conv2d_backward_input : + Symbol.Shape.Type.arr

                  conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                  • input is the original input array.
                  • kernel is the convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                  TODO

                  val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                  conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                  • input is the original input array.
                  • kernel is the convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                  val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val conv3d_backward_input : + Symbol.Shape.Type.arr

                  conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                  • input is the original input array.
                  • kernel is the convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                  TODO

                  val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                  conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                  • input is the original input array.
                  • kernel is the convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                  val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                  conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                  • input is the original input array.
                  • kernel is the convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                  val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                  transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                  • input is the original input array.
                  • kernel is the transposed convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                  val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                  transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                  • input is the original input array.
                  • kernel is the transposed convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                  val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                  transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                  • input is the original input array.
                  • kernel is the transposed convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                  val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                  transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                  • input is the original input array.
                  • kernel is the transposed convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                  val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                  transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                  • input is the original input array.
                  • kernel is the transposed convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                  val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                  transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                  • input is the original input array.
                  • kernel is the transposed convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                  val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                  dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                  • input is the original input array.
                  • kernel is the dilated convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • dilations specifies the dilation rate.
                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                  val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                  dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                  • input is the original input array.
                  • kernel is the dilated convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • dilations specifies the dilation rate.
                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                  val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                  dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                  • input is the original input array.
                  • kernel is the dilated convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • dilations specifies the dilation rate.
                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                  val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                  dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                  • input is the original input array.
                  • kernel is the dilated convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • dilations specifies the dilation rate.
                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                  val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                  dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                  • input is the original input array.
                  • kernel is the dilated convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • dilations specifies the dilation rate.
                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                  val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val max_pool1d_backward : + Symbol.Shape.Type.arr

                  dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                  • input is the original input array.
                  • kernel is the dilated convolutional kernel used during the forward pass.
                  • strides specifies the stride length.
                  • dilations specifies the dilation rate.
                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                  val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val max_pool2d_backward : + Symbol.Shape.Type.arr

                  max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                  • padding specifies the padding strategy used during the forward pass.
                  • input is the original input array.
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                  val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val max_pool3d_backward : + Symbol.Shape.Type.arr

                  max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                  • padding specifies the padding strategy used during the forward pass.
                  • input is the original input array.
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                  val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val avg_pool1d_backward : + Symbol.Shape.Type.arr

                  max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                  • padding specifies the padding strategy used during the forward pass.
                  • input is the original input array.
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                  val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val avg_pool2d_backward : + Symbol.Shape.Type.arr

                  avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                  • padding specifies the padding strategy used during the forward pass.
                  • input is the original input array.
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                  val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val avg_pool3d_backward : + Symbol.Shape.Type.arr

                  avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                  • padding specifies the padding strategy used during the forward pass.
                  • input is the original input array.
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                  val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val upsampling2d_backward : + Symbol.Shape.Type.arr

                  avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                  • padding specifies the padding strategy used during the forward pass.
                  • input is the original input array.
                  • pool_size specifies the size of the pooling window.
                  • strides specifies the stride length.
                  • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                  val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val row_num : Symbol.Shape.Type.arr -> int

                  TODO

                  val col_num : Symbol.Shape.Type.arr -> int

                  TODO

                  val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  TODO

                  val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                  TODO

                  val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                  TODO

                  TODO

                  upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                  • input is the original input array.
                  • size specifies the upsampling factors for each dimension.
                  • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                  val row_num : Symbol.Shape.Type.arr -> int

                  row_num arr returns the number of rows in the array arr.

                  val col_num : Symbol.Shape.Type.arr -> int

                  col_num arr returns the number of columns in the array arr.

                  row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                  val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                  rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                  val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                  copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                  val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                  copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                  diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                  trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                  val transpose : + Symbol.Shape.Type.arr

                  dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                  val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                  TODO

                  val to_rows : Symbol.Shape.Type.arr -> 'a array

                  TODO

                  TODO

                  val to_cols : Symbol.Shape.Type.arr -> 'a array

                  TODO

                  TODO

                  val of_array : + Symbol.Shape.Type.arr

                  transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                  val to_rows : Symbol.Shape.Type.arr -> 'a array

                  to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                  of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                  val to_cols : Symbol.Shape.Type.arr -> 'a array

                  to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                  of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                  val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                  TODO

                  val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                  TODO

                  val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                  TODO

                  Scalar functions
                  module Scalar : sig ... end
                  module Mat : sig ... end
                  module Linalg : sig ... end
                  + Symbol.Shape.Type.arr

                  of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                  val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                  of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                  val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                  to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                  Scalar functions
                  module Scalar : sig ... end
                  module Mat : sig ... end
                  module Linalg : sig ... end
                  diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/index.html index 98b46fed7..01cc1a309 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser)

                  Module Flatten_Sig.Optimiser

                  Core functions
                  val estimate_complexity : 'a Owl_graph.node array -> int * int

                  TODO

                  val optimise_nodes : +Optimiser (owl-base.Owl_computation_engine_sig.Flatten_Sig.Optimiser)

                  Module Flatten_Sig.Optimiser

                  Core functions
                  val estimate_complexity : 'a Owl_graph.node array -> int * int

                  TODO

                  val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

                  TODO

                  diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Scalar/index.html index 423958b27..102c23c69 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Scalar)

                  Module Flatten_Sig.Scalar

                  val add : +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Scalar)

                  Module Flatten_Sig.Scalar

                  add a b returns the sum of the scalars a and b.

                  sub a b returns the difference of the scalars a and b.

                  mul a b returns the product of the scalars a and b.

                  div a b returns the quotient of the scalars a and b.

                  val atan2 : + Symbol.Shape.Type.elt

                  pow a b returns the scalar a raised to the power of b.

                  + Symbol.Shape.Type.elt

                  atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                  abs a returns the absolute value of the scalar a.

                  neg a returns the negation of the scalar a.

                  sqr a returns the square of the scalar a.

                  sqrt a returns the square root of the scalar a.

                  exp a returns the exponential of the scalar a.

                  log a returns the natural logarithm of the scalar a.

                  log2 a returns the base-2 logarithm of the scalar a.

                  log10 a returns the base-10 logarithm of the scalar a.

                  signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                  floor a returns the greatest integer less than or equal to the scalar a.

                  ceil a returns the smallest integer greater than or equal to the scalar a.

                  round a returns the nearest integer to the scalar a.

                  sin a returns the sine of the scalar a.

                  cos a returns the cosine of the scalar a.

                  tan a returns the tangent of the scalar a.

                  sinh a returns the hyperbolic sine of the scalar a.

                  cosh a returns the hyperbolic cosine of the scalar a.

                  tanh a returns the hyperbolic tangent of the scalar a.

                  asin a returns the arcsine of the scalar a.

                  acos a returns the arccosine of the scalar a.

                  atan a returns the arctangent of the scalar a.

                  asinh a returns the inverse hyperbolic sine of the scalar a.

                  acosh a returns the inverse hyperbolic cosine of the scalar a.

                  atanh a returns the inverse hyperbolic tangent of the scalar a.

                  relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                  dawsn a returns Dawson's function of the scalar a.

                  sigmoid a returns the sigmoid function of the scalar a.

                  diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Linalg/index.html index 9a6e60dfb..a21127747 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device.A.Linalg)

                  Module A.Linalg

                  val inv : arr -> arr
                  val logdet : arr -> elt
                  val chol : ?upper:bool -> arr -> arr
                  val svd : ?thin:bool -> arr -> arr * arr * arr
                  val qr : arr -> arr * arr
                  val lq : arr -> arr * arr
                  val sylvester : arr -> arr -> arr -> arr
                  val lyapunov : arr -> arr -> arr
                  val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device.A.Linalg)

                  Module A.Linalg

                  val inv : arr -> arr
                  val logdet : arr -> elt
                  val chol : ?upper:bool -> arr -> arr
                  val svd : ?thin:bool -> arr -> arr * arr * arr
                  val qr : arr -> arr * arr
                  val lq : arr -> arr * arr
                  val sylvester : arr -> arr -> arr -> arr
                  val lyapunov : arr -> arr -> arr
                  val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Mat/index.html index 65741b05e..a35253666 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device.A.Mat)

                  Module A.Mat

                  val diagm : ?k:int -> arr -> arr
                  val triu : ?k:int -> arr -> arr
                  val tril : ?k:int -> arr -> arr
                  val eye : int -> arr
                  +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device.A.Mat)

                  Module A.Mat

                  val diagm : ?k:int -> arr -> arr
                  val triu : ?k:int -> arr -> arr
                  val tril : ?k:int -> arr -> arr
                  val eye : int -> arr
                  diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Scalar/index.html index 8c3c18f6c..4bfa4d85d 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device.A.Scalar)

                  Module A.Scalar

                  val add : elt -> elt -> elt
                  val sub : elt -> elt -> elt
                  val mul : elt -> elt -> elt
                  val div : elt -> elt -> elt
                  val pow : elt -> elt -> elt
                  val atan2 : elt -> elt -> elt
                  val abs : elt -> elt
                  val neg : elt -> elt
                  val sqr : elt -> elt
                  val sqrt : elt -> elt
                  val exp : elt -> elt
                  val log : elt -> elt
                  val log2 : elt -> elt
                  val log10 : elt -> elt
                  val signum : elt -> elt
                  val floor : elt -> elt
                  val ceil : elt -> elt
                  val round : elt -> elt
                  val sin : elt -> elt
                  val cos : elt -> elt
                  val tan : elt -> elt
                  val sinh : elt -> elt
                  val cosh : elt -> elt
                  val tanh : elt -> elt
                  val asin : elt -> elt
                  val acos : elt -> elt
                  val atan : elt -> elt
                  val asinh : elt -> elt
                  val acosh : elt -> elt
                  val atanh : elt -> elt
                  val relu : elt -> elt
                  val dawsn : elt -> elt
                  val sigmoid : elt -> elt
                  +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device.A.Scalar)

                  Module A.Scalar

                  val add : elt -> elt -> elt
                  val sub : elt -> elt -> elt
                  val mul : elt -> elt -> elt
                  val div : elt -> elt -> elt
                  val pow : elt -> elt -> elt
                  val atan2 : elt -> elt -> elt
                  val abs : elt -> elt
                  val neg : elt -> elt
                  val sqr : elt -> elt
                  val sqrt : elt -> elt
                  val exp : elt -> elt
                  val log : elt -> elt
                  val log2 : elt -> elt
                  val log10 : elt -> elt
                  val signum : elt -> elt
                  val floor : elt -> elt
                  val ceil : elt -> elt
                  val round : elt -> elt
                  val sin : elt -> elt
                  val cos : elt -> elt
                  val tan : elt -> elt
                  val sinh : elt -> elt
                  val cosh : elt -> elt
                  val tanh : elt -> elt
                  val asin : elt -> elt
                  val acos : elt -> elt
                  val atan : elt -> elt
                  val asinh : elt -> elt
                  val acosh : elt -> elt
                  val atanh : elt -> elt
                  val relu : elt -> elt
                  val dawsn : elt -> elt
                  val sigmoid : elt -> elt
                  diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/index.html index ed007930a..38a465e5c 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device.A)

                  Module Device.A

                  include Owl_types_ndarray_algodiff.Sig
                  include Owl_types_ndarray_eltcmp.Sig
                  include Owl_types_ndarray_basic.Sig
                  type arr
                  type elt
                  val empty : int array -> arr
                  val zeros : int array -> arr
                  val ones : int array -> arr
                  val create : int array -> elt -> arr
                  val sequential : ?a:elt -> ?step:elt -> int array -> arr
                  val uniform : ?a:elt -> ?b:elt -> int array -> arr
                  val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                  val bernoulli : ?p:elt -> int array -> arr
                  val init : int array -> (int -> elt) -> arr
                  val init_nd : int array -> (int array -> elt) -> arr
                  val shape : arr -> int array
                  val numel : arr -> int
                  val get : arr -> int array -> elt
                  val set : arr -> int array -> elt -> unit
                  val get_slice : int list list -> arr -> arr
                  val set_slice : int list list -> arr -> arr -> unit
                  val get_fancy : Owl_types_common.index list -> arr -> arr
                  val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                  val copy : arr -> arr
                  val copy_ : out:arr -> arr -> unit
                  val reset : arr -> unit
                  val reshape : arr -> int array -> arr
                  val reverse : arr -> arr
                  val tile : arr -> int array -> arr
                  val repeat : arr -> int array -> arr
                  val concatenate : ?axis:int -> arr array -> arr
                  val stack : ?axis:int -> arr array -> arr
                  val split : ?axis:int -> int array -> arr -> arr array
                  val expand : ?hi:bool -> arr -> int -> arr
                  val squeeze : ?axis:int array -> arr -> arr
                  val draw : ?axis:int -> arr -> int -> arr * int array
                  val map : (elt -> elt) -> arr -> arr
                  val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                  val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                  val one_hot : int -> arr -> arr
                  val pad : ?v:elt -> int list list -> arr -> arr
                  val print : +A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device.A)

                  Module Device.A

                  include Owl_types_ndarray_algodiff.Sig
                  include Owl_types_ndarray_eltcmp.Sig
                  include Owl_types_ndarray_basic.Sig
                  type arr
                  type elt
                  val empty : int array -> arr
                  val zeros : int array -> arr
                  val ones : int array -> arr
                  val create : int array -> elt -> arr
                  val sequential : ?a:elt -> ?step:elt -> int array -> arr
                  val uniform : ?a:elt -> ?b:elt -> int array -> arr
                  val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                  val bernoulli : ?p:elt -> int array -> arr
                  val init : int array -> (int -> elt) -> arr
                  val init_nd : int array -> (int array -> elt) -> arr
                  val shape : arr -> int array
                  val numel : arr -> int
                  val get : arr -> int array -> elt
                  val set : arr -> int array -> elt -> unit
                  val get_slice : int list list -> arr -> arr
                  val set_slice : int list list -> arr -> arr -> unit
                  val get_fancy : Owl_types_common.index list -> arr -> arr
                  val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                  val copy : arr -> arr
                  val copy_ : out:arr -> arr -> unit
                  val reset : arr -> unit
                  val reshape : arr -> int array -> arr
                  val reverse : arr -> arr
                  val tile : arr -> int array -> arr
                  val repeat : arr -> int array -> arr
                  val concatenate : ?axis:int -> arr array -> arr
                  val stack : ?axis:int -> arr array -> arr
                  val split : ?axis:int -> int array -> arr -> arr array
                  val expand : ?hi:bool -> arr -> int -> arr
                  val squeeze : ?axis:int array -> arr -> arr
                  val draw : ?axis:int -> arr -> int -> arr * int array
                  val map : (elt -> elt) -> arr -> arr
                  val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                  val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                  val one_hot : int -> arr -> arr
                  val pad : ?v:elt -> int list list -> arr -> arr
                  val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/index.html index bed006948..27ca4d181 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device)

                  Module Type.Device

                  Type definition
                  type device

                  TODO

                  type value

                  TODO

                  Core functions
                  val make_device : unit -> device

                  TODO

                  val arr_to_value : A.arr -> value

                  TODO

                  val value_to_arr : value -> A.arr

                  TODO

                  val elt_to_value : A.elt -> value

                  TODO

                  val value_to_elt : value -> A.elt

                  TODO

                  val value_to_float : value -> float

                  TODO

                  val is_arr : value -> bool

                  TODO

                  val is_elt : value -> bool

                  TODO

                  +Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type.Device)

                  Module Type.Device

                  Type definition
                  type device

                  TODO

                  type value

                  TODO

                  Core functions
                  val make_device : unit -> device

                  TODO

                  val arr_to_value : A.arr -> value

                  TODO

                  val value_to_arr : value -> A.arr

                  TODO

                  val elt_to_value : A.elt -> value

                  TODO

                  val value_to_elt : value -> A.elt

                  TODO

                  val value_to_float : value -> float

                  TODO

                  val is_arr : value -> bool

                  TODO

                  val is_elt : value -> bool

                  TODO

                  diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/index.html index 19f8fc0b8..24d428c33 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type)

                  Module Shape.Type

                  Type definition
                  type state =
                  1. | Valid
                  2. | Invalid
                    (*

                    TODO

                    *)

                  TODO

                  and block = {
                  1. size : int;
                  2. block_id : int;
                  3. mutable active : t option;
                  4. mutable memory : Device.value;
                  5. mutable nodes : t list;
                  }

                  block type keeps a reference to a block of memory and to the nodes sharing that block.

                  and attr = {
                  1. mutable op : op;
                  2. mutable freeze : bool;
                  3. mutable reuse : bool;
                  4. mutable state : state;
                  5. mutable shape : int array option array;
                  6. mutable value : Device.value array;
                  7. mutable block : block array option;
                  }

                  TODO

                  and arr =
                  1. | Arr of t
                  and elt =
                  1. | Elt of t
                  and op =
                  1. | Noop
                  2. | Var
                  3. | Const
                  4. | Empty of int array
                  5. | Zeros of int array
                  6. | Ones of int array
                  7. | Create of int array
                  8. | Sequential of int array
                  9. | Uniform of int array
                  10. | Gaussian of int array
                  11. | Bernoulli of int array
                  12. | Init of int array * int -> elt
                  13. | Get of int array
                  14. | Set of int array
                  15. | GetSlice of int list list
                  16. | SetSlice of int list list
                  17. | GetFancy of Owl_types_common.index list
                  18. | SetFancy of Owl_types_common.index list
                  19. | Copy
                  20. | Reset
                  21. | Reshape of int array
                  22. | Reverse
                  23. | Tile of int array
                  24. | Repeat of int array
                  25. | Pad of elt * int list list
                  26. | Concatenate of int
                  27. | Stack of int
                  28. | Split of int * int array
                  29. | Draw of int * int
                  30. | Map of elt -> elt
                  31. | Fold of int * elt -> elt -> elt
                  32. | Scan of int * elt -> elt -> elt
                  33. | OneHot of int
                  34. | OfArray of int array
                  35. | Delay of Device.A.arr -> Device.A.arr
                  36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                  37. | LazyPrint of int option +Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape.Type)

                    Module Shape.Type

                    Type definition
                    type state =
                    1. | Valid
                    2. | Invalid
                      (*

                      TODO

                      *)

                    TODO

                    and block = {
                    1. size : int;
                    2. block_id : int;
                    3. mutable active : t option;
                    4. mutable memory : Device.value;
                    5. mutable nodes : t list;
                    }

                    block type keeps a reference to a block of memory and to the nodes sharing that block.

                    and attr = {
                    1. mutable op : op;
                    2. mutable freeze : bool;
                    3. mutable reuse : bool;
                    4. mutable state : state;
                    5. mutable shape : int array option array;
                    6. mutable value : Device.value array;
                    7. mutable block : block array option;
                    }

                    TODO

                    and arr =
                    1. | Arr of t
                    and elt =
                    1. | Elt of t
                    and op =
                    1. | Noop
                    2. | Var
                    3. | Const
                    4. | Empty of int array
                    5. | Zeros of int array
                    6. | Ones of int array
                    7. | Create of int array
                    8. | Sequential of int array
                    9. | Uniform of int array
                    10. | Gaussian of int array
                    11. | Bernoulli of int array
                    12. | Init of int array * int -> elt
                    13. | Get of int array
                    14. | Set of int array
                    15. | GetSlice of int list list
                    16. | SetSlice of int list list
                    17. | GetFancy of Owl_types_common.index list
                    18. | SetFancy of Owl_types_common.index list
                    19. | Copy
                    20. | Reset
                    21. | Reshape of int array
                    22. | Reverse
                    23. | Tile of int array
                    24. | Repeat of int array
                    25. | Pad of elt * int list list
                    26. | Concatenate of int
                    27. | Stack of int
                    28. | Split of int * int array
                    29. | Draw of int * int
                    30. | Map of elt -> elt
                    31. | Fold of int * elt -> elt -> elt
                    32. | Scan of int * elt -> elt -> elt
                    33. | OneHot of int
                    34. | OfArray of int array
                    35. | Delay of Device.A.arr -> Device.A.arr
                    36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                    37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                    38. | Abs
                    39. | Neg
                    40. | Floor
                    41. | Ceil
                    42. | Round
                    43. | Sqr
                    44. | Sqrt
                    45. | Log
                    46. | Log2
                    47. | Log10
                    48. | Exp
                    49. | Sin
                    50. | Cos
                    51. | Tan
                    52. | Sinh
                    53. | Cosh
                    54. | Tanh
                    55. | Asin
                    56. | Acos
                    57. | Atan
                    58. | Asinh
                    59. | Acosh
                    60. | Atanh
                    61. | Min of bool * int
                    62. | Max of bool * int
                    63. | Sum of bool * int
                    64. | SumReduce of int array
                    65. | Signum
                    66. | Sigmoid
                    67. | Relu
                    68. | Dawsn
                    69. | Min'
                    70. | Max'
                    71. | Sum'
                    72. | LogSumExp'
                    73. | LogSumExp of bool * int
                    74. | L1norm'
                    75. | L2norm'
                    76. | L2NormSqr'
                    77. | ClipByValue
                    78. | ClipByL2norm
                    79. | Pow
                    80. | ScalarPow
                    81. | PowScalar
                    82. | Atan2
                    83. | ScalarAtan2
                    84. | Atan2Scalar
                    85. | Hypot
                    86. | Min2
                    87. | Max2
                    88. | Add
                    89. | Sub
                    90. | Mul
                    91. | Div
                    92. | AddScalar
                    93. | SubScalar
                    94. | MulScalar
                    95. | DivScalar
                    96. | ScalarAdd
                    97. | ScalarSub
                    98. | ScalarMul
                    99. | ScalarDiv
                    100. | FMA
                    101. | EltEqual
                    102. | EltNotEqual
                    103. | EltLess
                    104. | EltGreater
                    105. | EltLessEqual
                    106. | EltGreaterEqual
                    107. | EltEqualScalar
                    108. | EltNotEqualScalar
                    109. | EltLessScalar
                    110. | EltGreaterScalar
                    111. | EltLessEqualScalar
                    112. | EltGreaterEqualScalar
                    113. | Conv1d of Owl_types_common.padding * int array
                    114. | Conv2d of Owl_types_common.padding * int array
                    115. | Conv3d of Owl_types_common.padding * int array
                    116. | TransposeConv1d of Owl_types_common.padding * int array
                    117. | TransposeConv2d of Owl_types_common.padding * int array
                    118. | TransposeConv3d of Owl_types_common.padding * int array
                    119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                    120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                    121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                    122. | MaxPool1d of Owl_types_common.padding * int array * int array
                    123. | MaxPool2d of Owl_types_common.padding * int array * int array
                    124. | MaxPool3d of Owl_types_common.padding * int array * int array
                    125. | AvgPool1d of Owl_types_common.padding * int array * int array
                    126. | AvgPool2d of Owl_types_common.padding * int array * int array
                    127. | AvgPool3d of Owl_types_common.padding * int array * int array
                    128. | UpSampling2d of int array
                    129. | Conv1dBackwardInput of int array
                    130. | Conv1dBackwardKernel of int array
                    131. | Conv2dBackwardInput of int array
                    132. | Conv2dBackwardKernel of int array
                    133. | Conv3dBackwardInput of int array
                    134. | Conv3dBackwardKernel of int array
                    135. | TransposeConv1dBackwardInput of int array
                    136. | TransposeConv1dBackwardKernel of int array
                    137. | TransposeConv2dBackwardInput of int array
                    138. | TransposeConv2dBackwardKernel of int array
                    139. | TransposeConv3dBackwardInput of int array
                    140. | TransposeConv3dBackwardKernel of int array
                    141. | DilatedConv1dBackwardInput of int array * int array
                    142. | DilatedConv1dBackwardKernel of int array * int array
                    143. | DilatedConv2dBackwardInput of int array * int array
                    144. | DilatedConv2dBackwardKernel of int array * int array
                    145. | DilatedConv3dBackwardInput of int array * int array
                    146. | DilatedConv3dBackwardKernel of int array * int array
                    147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                    148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                    149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                    150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                    151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                    152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                    153. | UpSampling2dBackward of int array
                    154. | RowNum
                    155. | ColNum
                    156. | Row
                    157. | Rows of int array
                    158. | CopyRowTo
                    159. | CopyColTo
                    160. | Dot of bool * bool * elt * elt
                    161. | Inv
                    162. | Trace
                    163. | Transpose of int array
                    164. | ToRows
                    165. | OfRows
                    166. | Scalar_Add
                    167. | Scalar_Sub
                    168. | Scalar_Mul
                    169. | Scalar_Div
                    170. | Scalar_Pow
                    171. | Scalar_Atan2
                    172. | Scalar_Abs
                    173. | Scalar_Neg
                    174. | Scalar_Sqr
                    175. | Scalar_Sqrt
                    176. | Scalar_Exp
                    177. | Scalar_Log
                    178. | Scalar_Log2
                    179. | Scalar_Log10
                    180. | Scalar_Signum
                    181. | Scalar_Floor
                    182. | Scalar_Ceil
                    183. | Scalar_Round
                    184. | Scalar_Sin
                    185. | Scalar_Cos
                    186. | Scalar_Tan
                    187. | Scalar_Sinh
                    188. | Scalar_Cosh
                    189. | Scalar_Tanh
                    190. | Scalar_Asin
                    191. | Scalar_Acos
                    192. | Scalar_Atan
                    193. | Scalar_Asinh
                    194. | Scalar_Acosh
                    195. | Scalar_Atanh
                    196. | Scalar_Relu
                    197. | Scalar_Dawsn
                    198. | Scalar_Sigmoid
                    199. | Fused_Adagrad of float * float
                      (*

                      TODO

                      *)
                    diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/index.html index a1a39ba4e..54fdce412 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape)

                    Module Flatten_Sig.Shape

                    Core functions
                    val infer_shape : +Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Shape)

                    Module Flatten_Sig.Shape

                    Core functions
                    val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                    TODO

                    diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Linalg/index.html index 4e5541152..1b9bc820c 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device.A.Linalg)

                    Module A.Linalg

                    val inv : arr -> arr
                    val logdet : arr -> elt
                    val chol : ?upper:bool -> arr -> arr
                    val svd : ?thin:bool -> arr -> arr * arr * arr
                    val qr : arr -> arr * arr
                    val lq : arr -> arr * arr
                    val sylvester : arr -> arr -> arr -> arr
                    val lyapunov : arr -> arr -> arr
                    val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device.A.Linalg)

                    Module A.Linalg

                    val inv : arr -> arr
                    val logdet : arr -> elt
                    val chol : ?upper:bool -> arr -> arr
                    val svd : ?thin:bool -> arr -> arr * arr * arr
                    val qr : arr -> arr * arr
                    val lq : arr -> arr * arr
                    val sylvester : arr -> arr -> arr -> arr
                    val lyapunov : arr -> arr -> arr
                    val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Mat/index.html index 60a5b79e6..2e2f3084f 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device.A.Mat)

                    Module A.Mat

                    val diagm : ?k:int -> arr -> arr
                    val triu : ?k:int -> arr -> arr
                    val tril : ?k:int -> arr -> arr
                    val eye : int -> arr
                    +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device.A.Mat)

                    Module A.Mat

                    val diagm : ?k:int -> arr -> arr
                    val triu : ?k:int -> arr -> arr
                    val tril : ?k:int -> arr -> arr
                    val eye : int -> arr
                    diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Scalar/index.html index aaa010276..18fbcfd82 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device.A.Scalar)

                    Module A.Scalar

                    val add : elt -> elt -> elt
                    val sub : elt -> elt -> elt
                    val mul : elt -> elt -> elt
                    val div : elt -> elt -> elt
                    val pow : elt -> elt -> elt
                    val atan2 : elt -> elt -> elt
                    val abs : elt -> elt
                    val neg : elt -> elt
                    val sqr : elt -> elt
                    val sqrt : elt -> elt
                    val exp : elt -> elt
                    val log : elt -> elt
                    val log2 : elt -> elt
                    val log10 : elt -> elt
                    val signum : elt -> elt
                    val floor : elt -> elt
                    val ceil : elt -> elt
                    val round : elt -> elt
                    val sin : elt -> elt
                    val cos : elt -> elt
                    val tan : elt -> elt
                    val sinh : elt -> elt
                    val cosh : elt -> elt
                    val tanh : elt -> elt
                    val asin : elt -> elt
                    val acos : elt -> elt
                    val atan : elt -> elt
                    val asinh : elt -> elt
                    val acosh : elt -> elt
                    val atanh : elt -> elt
                    val relu : elt -> elt
                    val dawsn : elt -> elt
                    val sigmoid : elt -> elt
                    +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device.A.Scalar)

                    Module A.Scalar

                    val add : elt -> elt -> elt
                    val sub : elt -> elt -> elt
                    val mul : elt -> elt -> elt
                    val div : elt -> elt -> elt
                    val pow : elt -> elt -> elt
                    val atan2 : elt -> elt -> elt
                    val abs : elt -> elt
                    val neg : elt -> elt
                    val sqr : elt -> elt
                    val sqrt : elt -> elt
                    val exp : elt -> elt
                    val log : elt -> elt
                    val log2 : elt -> elt
                    val log10 : elt -> elt
                    val signum : elt -> elt
                    val floor : elt -> elt
                    val ceil : elt -> elt
                    val round : elt -> elt
                    val sin : elt -> elt
                    val cos : elt -> elt
                    val tan : elt -> elt
                    val sinh : elt -> elt
                    val cosh : elt -> elt
                    val tanh : elt -> elt
                    val asin : elt -> elt
                    val acos : elt -> elt
                    val atan : elt -> elt
                    val asinh : elt -> elt
                    val acosh : elt -> elt
                    val atanh : elt -> elt
                    val relu : elt -> elt
                    val dawsn : elt -> elt
                    val sigmoid : elt -> elt
                    diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/index.html index 6ca1593c6..0f762004d 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device.A)

                    Module Device.A

                    include Owl_types_ndarray_algodiff.Sig
                    include Owl_types_ndarray_eltcmp.Sig
                    include Owl_types_ndarray_basic.Sig
                    type arr
                    type elt
                    val empty : int array -> arr
                    val zeros : int array -> arr
                    val ones : int array -> arr
                    val create : int array -> elt -> arr
                    val sequential : ?a:elt -> ?step:elt -> int array -> arr
                    val uniform : ?a:elt -> ?b:elt -> int array -> arr
                    val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                    val bernoulli : ?p:elt -> int array -> arr
                    val init : int array -> (int -> elt) -> arr
                    val init_nd : int array -> (int array -> elt) -> arr
                    val shape : arr -> int array
                    val numel : arr -> int
                    val get : arr -> int array -> elt
                    val set : arr -> int array -> elt -> unit
                    val get_slice : int list list -> arr -> arr
                    val set_slice : int list list -> arr -> arr -> unit
                    val get_fancy : Owl_types_common.index list -> arr -> arr
                    val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                    val copy : arr -> arr
                    val copy_ : out:arr -> arr -> unit
                    val reset : arr -> unit
                    val reshape : arr -> int array -> arr
                    val reverse : arr -> arr
                    val tile : arr -> int array -> arr
                    val repeat : arr -> int array -> arr
                    val concatenate : ?axis:int -> arr array -> arr
                    val stack : ?axis:int -> arr array -> arr
                    val split : ?axis:int -> int array -> arr -> arr array
                    val expand : ?hi:bool -> arr -> int -> arr
                    val squeeze : ?axis:int array -> arr -> arr
                    val draw : ?axis:int -> arr -> int -> arr * int array
                    val map : (elt -> elt) -> arr -> arr
                    val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                    val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                    val one_hot : int -> arr -> arr
                    val pad : ?v:elt -> int list list -> arr -> arr
                    val print : +A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device.A)

                    Module Device.A

                    include Owl_types_ndarray_algodiff.Sig
                    include Owl_types_ndarray_eltcmp.Sig
                    include Owl_types_ndarray_basic.Sig
                    type arr
                    type elt
                    val empty : int array -> arr
                    val zeros : int array -> arr
                    val ones : int array -> arr
                    val create : int array -> elt -> arr
                    val sequential : ?a:elt -> ?step:elt -> int array -> arr
                    val uniform : ?a:elt -> ?b:elt -> int array -> arr
                    val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                    val bernoulli : ?p:elt -> int array -> arr
                    val init : int array -> (int -> elt) -> arr
                    val init_nd : int array -> (int array -> elt) -> arr
                    val shape : arr -> int array
                    val numel : arr -> int
                    val get : arr -> int array -> elt
                    val set : arr -> int array -> elt -> unit
                    val get_slice : int list list -> arr -> arr
                    val set_slice : int list list -> arr -> arr -> unit
                    val get_fancy : Owl_types_common.index list -> arr -> arr
                    val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                    val copy : arr -> arr
                    val copy_ : out:arr -> arr -> unit
                    val reset : arr -> unit
                    val reshape : arr -> int array -> arr
                    val reverse : arr -> arr
                    val tile : arr -> int array -> arr
                    val repeat : arr -> int array -> arr
                    val concatenate : ?axis:int -> arr array -> arr
                    val stack : ?axis:int -> arr array -> arr
                    val split : ?axis:int -> int array -> arr -> arr array
                    val expand : ?hi:bool -> arr -> int -> arr
                    val squeeze : ?axis:int array -> arr -> arr
                    val draw : ?axis:int -> arr -> int -> arr * int array
                    val map : (elt -> elt) -> arr -> arr
                    val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                    val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                    val one_hot : int -> arr -> arr
                    val pad : ?v:elt -> int list list -> arr -> arr
                    val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/index.html index a40196162..c1f674aa2 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device)

                    Module Type.Device

                    Type definition
                    type device

                    TODO

                    type value

                    TODO

                    Core functions
                    val make_device : unit -> device

                    TODO

                    val arr_to_value : A.arr -> value

                    TODO

                    val value_to_arr : value -> A.arr

                    TODO

                    val elt_to_value : A.elt -> value

                    TODO

                    val value_to_elt : value -> A.elt

                    TODO

                    val value_to_float : value -> float

                    TODO

                    val is_arr : value -> bool

                    TODO

                    val is_elt : value -> bool

                    TODO

                    +Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type.Device)

                    Module Type.Device

                    Type definition
                    type device

                    TODO

                    type value

                    TODO

                    Core functions
                    val make_device : unit -> device

                    TODO

                    val arr_to_value : A.arr -> value

                    TODO

                    val value_to_arr : value -> A.arr

                    TODO

                    val elt_to_value : A.elt -> value

                    TODO

                    val value_to_elt : value -> A.elt

                    TODO

                    val value_to_float : value -> float

                    TODO

                    val is_arr : value -> bool

                    TODO

                    val is_elt : value -> bool

                    TODO

                    diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/index.html index c059248de..267ebc958 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type)

                    Module Shape.Type

                    Type definition
                    type state =
                    1. | Valid
                    2. | Invalid
                      (*

                      TODO

                      *)

                    TODO

                    and block = {
                    1. size : int;
                    2. block_id : int;
                    3. mutable active : t option;
                    4. mutable memory : Device.value;
                    5. mutable nodes : t list;
                    }

                    block type keeps a reference to a block of memory and to the nodes sharing that block.

                    and attr = {
                    1. mutable op : op;
                    2. mutable freeze : bool;
                    3. mutable reuse : bool;
                    4. mutable state : state;
                    5. mutable shape : int array option array;
                    6. mutable value : Device.value array;
                    7. mutable block : block array option;
                    }

                    TODO

                    and arr =
                    1. | Arr of t
                    and elt =
                    1. | Elt of t
                    and op =
                    1. | Noop
                    2. | Var
                    3. | Const
                    4. | Empty of int array
                    5. | Zeros of int array
                    6. | Ones of int array
                    7. | Create of int array
                    8. | Sequential of int array
                    9. | Uniform of int array
                    10. | Gaussian of int array
                    11. | Bernoulli of int array
                    12. | Init of int array * int -> elt
                    13. | Get of int array
                    14. | Set of int array
                    15. | GetSlice of int list list
                    16. | SetSlice of int list list
                    17. | GetFancy of Owl_types_common.index list
                    18. | SetFancy of Owl_types_common.index list
                    19. | Copy
                    20. | Reset
                    21. | Reshape of int array
                    22. | Reverse
                    23. | Tile of int array
                    24. | Repeat of int array
                    25. | Pad of elt * int list list
                    26. | Concatenate of int
                    27. | Stack of int
                    28. | Split of int * int array
                    29. | Draw of int * int
                    30. | Map of elt -> elt
                    31. | Fold of int * elt -> elt -> elt
                    32. | Scan of int * elt -> elt -> elt
                    33. | OneHot of int
                    34. | OfArray of int array
                    35. | Delay of Device.A.arr -> Device.A.arr
                    36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                    37. | LazyPrint of int option +Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape.Type)

                      Module Shape.Type

                      Type definition
                      type state =
                      1. | Valid
                      2. | Invalid
                        (*

                        TODO

                        *)

                      TODO

                      and block = {
                      1. size : int;
                      2. block_id : int;
                      3. mutable active : t option;
                      4. mutable memory : Device.value;
                      5. mutable nodes : t list;
                      }

                      block type keeps a reference to a block of memory and to the nodes sharing that block.

                      and attr = {
                      1. mutable op : op;
                      2. mutable freeze : bool;
                      3. mutable reuse : bool;
                      4. mutable state : state;
                      5. mutable shape : int array option array;
                      6. mutable value : Device.value array;
                      7. mutable block : block array option;
                      }

                      TODO

                      and arr =
                      1. | Arr of t
                      and elt =
                      1. | Elt of t
                      and op =
                      1. | Noop
                      2. | Var
                      3. | Const
                      4. | Empty of int array
                      5. | Zeros of int array
                      6. | Ones of int array
                      7. | Create of int array
                      8. | Sequential of int array
                      9. | Uniform of int array
                      10. | Gaussian of int array
                      11. | Bernoulli of int array
                      12. | Init of int array * int -> elt
                      13. | Get of int array
                      14. | Set of int array
                      15. | GetSlice of int list list
                      16. | SetSlice of int list list
                      17. | GetFancy of Owl_types_common.index list
                      18. | SetFancy of Owl_types_common.index list
                      19. | Copy
                      20. | Reset
                      21. | Reshape of int array
                      22. | Reverse
                      23. | Tile of int array
                      24. | Repeat of int array
                      25. | Pad of elt * int list list
                      26. | Concatenate of int
                      27. | Stack of int
                      28. | Split of int * int array
                      29. | Draw of int * int
                      30. | Map of elt -> elt
                      31. | Fold of int * elt -> elt -> elt
                      32. | Scan of int * elt -> elt -> elt
                      33. | OneHot of int
                      34. | OfArray of int array
                      35. | Delay of Device.A.arr -> Device.A.arr
                      36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                      37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                      38. | Abs
                      39. | Neg
                      40. | Floor
                      41. | Ceil
                      42. | Round
                      43. | Sqr
                      44. | Sqrt
                      45. | Log
                      46. | Log2
                      47. | Log10
                      48. | Exp
                      49. | Sin
                      50. | Cos
                      51. | Tan
                      52. | Sinh
                      53. | Cosh
                      54. | Tanh
                      55. | Asin
                      56. | Acos
                      57. | Atan
                      58. | Asinh
                      59. | Acosh
                      60. | Atanh
                      61. | Min of bool * int
                      62. | Max of bool * int
                      63. | Sum of bool * int
                      64. | SumReduce of int array
                      65. | Signum
                      66. | Sigmoid
                      67. | Relu
                      68. | Dawsn
                      69. | Min'
                      70. | Max'
                      71. | Sum'
                      72. | LogSumExp'
                      73. | LogSumExp of bool * int
                      74. | L1norm'
                      75. | L2norm'
                      76. | L2NormSqr'
                      77. | ClipByValue
                      78. | ClipByL2norm
                      79. | Pow
                      80. | ScalarPow
                      81. | PowScalar
                      82. | Atan2
                      83. | ScalarAtan2
                      84. | Atan2Scalar
                      85. | Hypot
                      86. | Min2
                      87. | Max2
                      88. | Add
                      89. | Sub
                      90. | Mul
                      91. | Div
                      92. | AddScalar
                      93. | SubScalar
                      94. | MulScalar
                      95. | DivScalar
                      96. | ScalarAdd
                      97. | ScalarSub
                      98. | ScalarMul
                      99. | ScalarDiv
                      100. | FMA
                      101. | EltEqual
                      102. | EltNotEqual
                      103. | EltLess
                      104. | EltGreater
                      105. | EltLessEqual
                      106. | EltGreaterEqual
                      107. | EltEqualScalar
                      108. | EltNotEqualScalar
                      109. | EltLessScalar
                      110. | EltGreaterScalar
                      111. | EltLessEqualScalar
                      112. | EltGreaterEqualScalar
                      113. | Conv1d of Owl_types_common.padding * int array
                      114. | Conv2d of Owl_types_common.padding * int array
                      115. | Conv3d of Owl_types_common.padding * int array
                      116. | TransposeConv1d of Owl_types_common.padding * int array
                      117. | TransposeConv2d of Owl_types_common.padding * int array
                      118. | TransposeConv3d of Owl_types_common.padding * int array
                      119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                      120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                      121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                      122. | MaxPool1d of Owl_types_common.padding * int array * int array
                      123. | MaxPool2d of Owl_types_common.padding * int array * int array
                      124. | MaxPool3d of Owl_types_common.padding * int array * int array
                      125. | AvgPool1d of Owl_types_common.padding * int array * int array
                      126. | AvgPool2d of Owl_types_common.padding * int array * int array
                      127. | AvgPool3d of Owl_types_common.padding * int array * int array
                      128. | UpSampling2d of int array
                      129. | Conv1dBackwardInput of int array
                      130. | Conv1dBackwardKernel of int array
                      131. | Conv2dBackwardInput of int array
                      132. | Conv2dBackwardKernel of int array
                      133. | Conv3dBackwardInput of int array
                      134. | Conv3dBackwardKernel of int array
                      135. | TransposeConv1dBackwardInput of int array
                      136. | TransposeConv1dBackwardKernel of int array
                      137. | TransposeConv2dBackwardInput of int array
                      138. | TransposeConv2dBackwardKernel of int array
                      139. | TransposeConv3dBackwardInput of int array
                      140. | TransposeConv3dBackwardKernel of int array
                      141. | DilatedConv1dBackwardInput of int array * int array
                      142. | DilatedConv1dBackwardKernel of int array * int array
                      143. | DilatedConv2dBackwardInput of int array * int array
                      144. | DilatedConv2dBackwardKernel of int array * int array
                      145. | DilatedConv3dBackwardInput of int array * int array
                      146. | DilatedConv3dBackwardKernel of int array * int array
                      147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                      148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                      149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                      150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                      151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                      152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                      153. | UpSampling2dBackward of int array
                      154. | RowNum
                      155. | ColNum
                      156. | Row
                      157. | Rows of int array
                      158. | CopyRowTo
                      159. | CopyColTo
                      160. | Dot of bool * bool * elt * elt
                      161. | Inv
                      162. | Trace
                      163. | Transpose of int array
                      164. | ToRows
                      165. | OfRows
                      166. | Scalar_Add
                      167. | Scalar_Sub
                      168. | Scalar_Mul
                      169. | Scalar_Div
                      170. | Scalar_Pow
                      171. | Scalar_Atan2
                      172. | Scalar_Abs
                      173. | Scalar_Neg
                      174. | Scalar_Sqr
                      175. | Scalar_Sqrt
                      176. | Scalar_Exp
                      177. | Scalar_Log
                      178. | Scalar_Log2
                      179. | Scalar_Log10
                      180. | Scalar_Signum
                      181. | Scalar_Floor
                      182. | Scalar_Ceil
                      183. | Scalar_Round
                      184. | Scalar_Sin
                      185. | Scalar_Cos
                      186. | Scalar_Tan
                      187. | Scalar_Sinh
                      188. | Scalar_Cosh
                      189. | Scalar_Tanh
                      190. | Scalar_Asin
                      191. | Scalar_Acos
                      192. | Scalar_Atan
                      193. | Scalar_Asinh
                      194. | Scalar_Acosh
                      195. | Scalar_Atanh
                      196. | Scalar_Relu
                      197. | Scalar_Dawsn
                      198. | Scalar_Sigmoid
                      199. | Fused_Adagrad of float * float
                        (*

                        TODO

                        *)
                      diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/index.html index 24d6d1a78..ac6e9b8a7 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape)

                      Module Symbol.Shape

                      Core functions
                      val infer_shape : +Shape (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol.Shape)

                      Module Symbol.Shape

                      Core functions
                      val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                      TODO

                      diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/index.html index c0c0c72d7..a1945f241 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol)

                      Module Flatten_Sig.Symbol

                      Core functions
                      val op_to_str : Shape.Type.op -> string

                      TODO

                      val is_random_variable : Shape.Type.op -> bool

                      TODO

                      val refnum : 'a Owl_graph.node -> int

                      TODO

                      val node_shape : Shape.Type.attr Owl_graph.node -> int array

                      TODO

                      val node_numel : Shape.Type.attr Owl_graph.node -> int

                      TODO

                      val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                      TODO

                      val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                      TODO

                      val shape_to_str : int array option array -> string

                      TODO

                      val node_to_str : Shape.Type.attr Owl_graph.node -> string

                      TODO

                      val node_to_arr : Shape.Type.t -> Shape.Type.arr

                      TODO

                      val arr_to_node : Shape.Type.arr -> Shape.Type.t

                      TODO

                      val node_to_elt : Shape.Type.t -> Shape.Type.elt

                      TODO

                      val elt_to_node : Shape.Type.elt -> Shape.Type.t

                      TODO

                      val make_node : +Symbol (owl-base.Owl_computation_engine_sig.Flatten_Sig.Symbol)

                      Module Flatten_Sig.Symbol

                      Core functions
                      val op_to_str : Shape.Type.op -> string

                      TODO

                      val is_random_variable : Shape.Type.op -> bool

                      TODO

                      val refnum : 'a Owl_graph.node -> int

                      TODO

                      val node_shape : Shape.Type.attr Owl_graph.node -> int array

                      TODO

                      val node_numel : Shape.Type.attr Owl_graph.node -> int

                      TODO

                      val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                      TODO

                      val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                      TODO

                      val shape_to_str : int array option array -> string

                      TODO

                      val node_to_str : Shape.Type.attr Owl_graph.node -> string

                      TODO

                      val node_to_arr : Shape.Type.t -> Shape.Type.arr

                      TODO

                      val arr_to_node : Shape.Type.arr -> Shape.Type.t

                      TODO

                      val node_to_elt : Shape.Type.t -> Shape.Type.elt

                      TODO

                      val elt_to_node : Shape.Type.elt -> Shape.Type.t

                      TODO

                      val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Linalg/index.html index e413e8ce5..6867a6d98 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device.A.Linalg)

                      Module A.Linalg

                      val inv : arr -> arr
                      val logdet : arr -> elt
                      val chol : ?upper:bool -> arr -> arr
                      val svd : ?thin:bool -> arr -> arr * arr * arr
                      val qr : arr -> arr * arr
                      val lq : arr -> arr * arr
                      val sylvester : arr -> arr -> arr -> arr
                      val lyapunov : arr -> arr -> arr
                      val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device.A.Linalg)

                      Module A.Linalg

                      val inv : arr -> arr
                      val logdet : arr -> elt
                      val chol : ?upper:bool -> arr -> arr
                      val svd : ?thin:bool -> arr -> arr * arr * arr
                      val qr : arr -> arr * arr
                      val lq : arr -> arr * arr
                      val sylvester : arr -> arr -> arr -> arr
                      val lyapunov : arr -> arr -> arr
                      val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Mat/index.html index 3b4f6b380..8b2d13e1c 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device.A.Mat)

                      Module A.Mat

                      val diagm : ?k:int -> arr -> arr
                      val triu : ?k:int -> arr -> arr
                      val tril : ?k:int -> arr -> arr
                      val eye : int -> arr
                      +Mat (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device.A.Mat)

                      Module A.Mat

                      val diagm : ?k:int -> arr -> arr
                      val triu : ?k:int -> arr -> arr
                      val tril : ?k:int -> arr -> arr
                      val eye : int -> arr
                      diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Scalar/index.html index e6dbaa86f..fe62408c4 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device.A.Scalar)

                      Module A.Scalar

                      val add : elt -> elt -> elt
                      val sub : elt -> elt -> elt
                      val mul : elt -> elt -> elt
                      val div : elt -> elt -> elt
                      val pow : elt -> elt -> elt
                      val atan2 : elt -> elt -> elt
                      val abs : elt -> elt
                      val neg : elt -> elt
                      val sqr : elt -> elt
                      val sqrt : elt -> elt
                      val exp : elt -> elt
                      val log : elt -> elt
                      val log2 : elt -> elt
                      val log10 : elt -> elt
                      val signum : elt -> elt
                      val floor : elt -> elt
                      val ceil : elt -> elt
                      val round : elt -> elt
                      val sin : elt -> elt
                      val cos : elt -> elt
                      val tan : elt -> elt
                      val sinh : elt -> elt
                      val cosh : elt -> elt
                      val tanh : elt -> elt
                      val asin : elt -> elt
                      val acos : elt -> elt
                      val atan : elt -> elt
                      val asinh : elt -> elt
                      val acosh : elt -> elt
                      val atanh : elt -> elt
                      val relu : elt -> elt
                      val dawsn : elt -> elt
                      val sigmoid : elt -> elt
                      +Scalar (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device.A.Scalar)

                      Module A.Scalar

                      val add : elt -> elt -> elt
                      val sub : elt -> elt -> elt
                      val mul : elt -> elt -> elt
                      val div : elt -> elt -> elt
                      val pow : elt -> elt -> elt
                      val atan2 : elt -> elt -> elt
                      val abs : elt -> elt
                      val neg : elt -> elt
                      val sqr : elt -> elt
                      val sqrt : elt -> elt
                      val exp : elt -> elt
                      val log : elt -> elt
                      val log2 : elt -> elt
                      val log10 : elt -> elt
                      val signum : elt -> elt
                      val floor : elt -> elt
                      val ceil : elt -> elt
                      val round : elt -> elt
                      val sin : elt -> elt
                      val cos : elt -> elt
                      val tan : elt -> elt
                      val sinh : elt -> elt
                      val cosh : elt -> elt
                      val tanh : elt -> elt
                      val asin : elt -> elt
                      val acos : elt -> elt
                      val atan : elt -> elt
                      val asinh : elt -> elt
                      val acosh : elt -> elt
                      val atanh : elt -> elt
                      val relu : elt -> elt
                      val dawsn : elt -> elt
                      val sigmoid : elt -> elt
                      diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/index.html index b40998338..6a4adfb56 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device.A)

                      Module Device.A

                      include Owl_types_ndarray_algodiff.Sig
                      include Owl_types_ndarray_eltcmp.Sig
                      include Owl_types_ndarray_basic.Sig
                      type arr
                      type elt
                      val empty : int array -> arr
                      val zeros : int array -> arr
                      val ones : int array -> arr
                      val create : int array -> elt -> arr
                      val sequential : ?a:elt -> ?step:elt -> int array -> arr
                      val uniform : ?a:elt -> ?b:elt -> int array -> arr
                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                      val bernoulli : ?p:elt -> int array -> arr
                      val init : int array -> (int -> elt) -> arr
                      val init_nd : int array -> (int array -> elt) -> arr
                      val shape : arr -> int array
                      val numel : arr -> int
                      val get : arr -> int array -> elt
                      val set : arr -> int array -> elt -> unit
                      val get_slice : int list list -> arr -> arr
                      val set_slice : int list list -> arr -> arr -> unit
                      val get_fancy : Owl_types_common.index list -> arr -> arr
                      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                      val copy : arr -> arr
                      val copy_ : out:arr -> arr -> unit
                      val reset : arr -> unit
                      val reshape : arr -> int array -> arr
                      val reverse : arr -> arr
                      val tile : arr -> int array -> arr
                      val repeat : arr -> int array -> arr
                      val concatenate : ?axis:int -> arr array -> arr
                      val stack : ?axis:int -> arr array -> arr
                      val split : ?axis:int -> int array -> arr -> arr array
                      val expand : ?hi:bool -> arr -> int -> arr
                      val squeeze : ?axis:int array -> arr -> arr
                      val draw : ?axis:int -> arr -> int -> arr * int array
                      val map : (elt -> elt) -> arr -> arr
                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                      val one_hot : int -> arr -> arr
                      val pad : ?v:elt -> int list list -> arr -> arr
                      val print : +A (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device.A)

                      Module Device.A

                      include Owl_types_ndarray_algodiff.Sig
                      include Owl_types_ndarray_eltcmp.Sig
                      include Owl_types_ndarray_basic.Sig
                      type arr
                      type elt
                      val empty : int array -> arr
                      val zeros : int array -> arr
                      val ones : int array -> arr
                      val create : int array -> elt -> arr
                      val sequential : ?a:elt -> ?step:elt -> int array -> arr
                      val uniform : ?a:elt -> ?b:elt -> int array -> arr
                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                      val bernoulli : ?p:elt -> int array -> arr
                      val init : int array -> (int -> elt) -> arr
                      val init_nd : int array -> (int array -> elt) -> arr
                      val shape : arr -> int array
                      val numel : arr -> int
                      val get : arr -> int array -> elt
                      val set : arr -> int array -> elt -> unit
                      val get_slice : int list list -> arr -> arr
                      val set_slice : int list list -> arr -> arr -> unit
                      val get_fancy : Owl_types_common.index list -> arr -> arr
                      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                      val copy : arr -> arr
                      val copy_ : out:arr -> arr -> unit
                      val reset : arr -> unit
                      val reshape : arr -> int array -> arr
                      val reverse : arr -> arr
                      val tile : arr -> int array -> arr
                      val repeat : arr -> int array -> arr
                      val concatenate : ?axis:int -> arr array -> arr
                      val stack : ?axis:int -> arr array -> arr
                      val split : ?axis:int -> int array -> arr -> arr array
                      val expand : ?hi:bool -> arr -> int -> arr
                      val squeeze : ?axis:int array -> arr -> arr
                      val draw : ?axis:int -> arr -> int -> arr * int array
                      val map : (elt -> elt) -> arr -> arr
                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                      val one_hot : int -> arr -> arr
                      val pad : ?v:elt -> int list list -> arr -> arr
                      val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/index.html index d4f3d841d..dc5681380 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device)

                      Module Type.Device

                      Type definition
                      type device

                      TODO

                      type value

                      TODO

                      Core functions
                      val make_device : unit -> device

                      TODO

                      val arr_to_value : A.arr -> value

                      TODO

                      val value_to_arr : value -> A.arr

                      TODO

                      val elt_to_value : A.elt -> value

                      TODO

                      val value_to_elt : value -> A.elt

                      TODO

                      val value_to_float : value -> float

                      TODO

                      val is_arr : value -> bool

                      TODO

                      val is_elt : value -> bool

                      TODO

                      +Device (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type.Device)

                      Module Type.Device

                      Type definition
                      type device

                      TODO

                      type value

                      TODO

                      Core functions
                      val make_device : unit -> device

                      TODO

                      val arr_to_value : A.arr -> value

                      TODO

                      val value_to_arr : value -> A.arr

                      TODO

                      val elt_to_value : A.elt -> value

                      TODO

                      val value_to_elt : value -> A.elt

                      TODO

                      val value_to_float : value -> float

                      TODO

                      val is_arr : value -> bool

                      TODO

                      val is_elt : value -> bool

                      TODO

                      diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/index.html index eb584365b..cc7837bab 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type)

                      Module Flatten_Sig.Type

                      Type definition
                      type state =
                      1. | Valid
                      2. | Invalid
                        (*

                        TODO

                        *)

                      TODO

                      and block = {
                      1. size : int;
                      2. block_id : int;
                      3. mutable active : t option;
                      4. mutable memory : Device.value;
                      5. mutable nodes : t list;
                      }

                      block type keeps a reference to a block of memory and to the nodes sharing that block.

                      and attr = {
                      1. mutable op : op;
                      2. mutable freeze : bool;
                      3. mutable reuse : bool;
                      4. mutable state : state;
                      5. mutable shape : int array option array;
                      6. mutable value : Device.value array;
                      7. mutable block : block array option;
                      }

                      TODO

                      and arr =
                      1. | Arr of t
                      and elt =
                      1. | Elt of t
                      and op =
                      1. | Noop
                      2. | Var
                      3. | Const
                      4. | Empty of int array
                      5. | Zeros of int array
                      6. | Ones of int array
                      7. | Create of int array
                      8. | Sequential of int array
                      9. | Uniform of int array
                      10. | Gaussian of int array
                      11. | Bernoulli of int array
                      12. | Init of int array * int -> elt
                      13. | Get of int array
                      14. | Set of int array
                      15. | GetSlice of int list list
                      16. | SetSlice of int list list
                      17. | GetFancy of Owl_types_common.index list
                      18. | SetFancy of Owl_types_common.index list
                      19. | Copy
                      20. | Reset
                      21. | Reshape of int array
                      22. | Reverse
                      23. | Tile of int array
                      24. | Repeat of int array
                      25. | Pad of elt * int list list
                      26. | Concatenate of int
                      27. | Stack of int
                      28. | Split of int * int array
                      29. | Draw of int * int
                      30. | Map of elt -> elt
                      31. | Fold of int * elt -> elt -> elt
                      32. | Scan of int * elt -> elt -> elt
                      33. | OneHot of int
                      34. | OfArray of int array
                      35. | Delay of Device.A.arr -> Device.A.arr
                      36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                      37. | LazyPrint of int option +Type (owl-base.Owl_computation_engine_sig.Flatten_Sig.Type)

                        Module Flatten_Sig.Type

                        Type definition
                        type state =
                        1. | Valid
                        2. | Invalid
                          (*

                          TODO

                          *)

                        TODO

                        and block = {
                        1. size : int;
                        2. block_id : int;
                        3. mutable active : t option;
                        4. mutable memory : Device.value;
                        5. mutable nodes : t list;
                        }

                        block type keeps a reference to a block of memory and to the nodes sharing that block.

                        and attr = {
                        1. mutable op : op;
                        2. mutable freeze : bool;
                        3. mutable reuse : bool;
                        4. mutable state : state;
                        5. mutable shape : int array option array;
                        6. mutable value : Device.value array;
                        7. mutable block : block array option;
                        }

                        TODO

                        and arr =
                        1. | Arr of t
                        and elt =
                        1. | Elt of t
                        and op =
                        1. | Noop
                        2. | Var
                        3. | Const
                        4. | Empty of int array
                        5. | Zeros of int array
                        6. | Ones of int array
                        7. | Create of int array
                        8. | Sequential of int array
                        9. | Uniform of int array
                        10. | Gaussian of int array
                        11. | Bernoulli of int array
                        12. | Init of int array * int -> elt
                        13. | Get of int array
                        14. | Set of int array
                        15. | GetSlice of int list list
                        16. | SetSlice of int list list
                        17. | GetFancy of Owl_types_common.index list
                        18. | SetFancy of Owl_types_common.index list
                        19. | Copy
                        20. | Reset
                        21. | Reshape of int array
                        22. | Reverse
                        23. | Tile of int array
                        24. | Repeat of int array
                        25. | Pad of elt * int list list
                        26. | Concatenate of int
                        27. | Stack of int
                        28. | Split of int * int array
                        29. | Draw of int * int
                        30. | Map of elt -> elt
                        31. | Fold of int * elt -> elt -> elt
                        32. | Scan of int * elt -> elt -> elt
                        33. | OneHot of int
                        34. | OfArray of int array
                        35. | Delay of Device.A.arr -> Device.A.arr
                        36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                        37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                        38. | Abs
                        39. | Neg
                        40. | Floor
                        41. | Ceil
                        42. | Round
                        43. | Sqr
                        44. | Sqrt
                        45. | Log
                        46. | Log2
                        47. | Log10
                        48. | Exp
                        49. | Sin
                        50. | Cos
                        51. | Tan
                        52. | Sinh
                        53. | Cosh
                        54. | Tanh
                        55. | Asin
                        56. | Acos
                        57. | Atan
                        58. | Asinh
                        59. | Acosh
                        60. | Atanh
                        61. | Min of bool * int
                        62. | Max of bool * int
                        63. | Sum of bool * int
                        64. | SumReduce of int array
                        65. | Signum
                        66. | Sigmoid
                        67. | Relu
                        68. | Dawsn
                        69. | Min'
                        70. | Max'
                        71. | Sum'
                        72. | LogSumExp'
                        73. | LogSumExp of bool * int
                        74. | L1norm'
                        75. | L2norm'
                        76. | L2NormSqr'
                        77. | ClipByValue
                        78. | ClipByL2norm
                        79. | Pow
                        80. | ScalarPow
                        81. | PowScalar
                        82. | Atan2
                        83. | ScalarAtan2
                        84. | Atan2Scalar
                        85. | Hypot
                        86. | Min2
                        87. | Max2
                        88. | Add
                        89. | Sub
                        90. | Mul
                        91. | Div
                        92. | AddScalar
                        93. | SubScalar
                        94. | MulScalar
                        95. | DivScalar
                        96. | ScalarAdd
                        97. | ScalarSub
                        98. | ScalarMul
                        99. | ScalarDiv
                        100. | FMA
                        101. | EltEqual
                        102. | EltNotEqual
                        103. | EltLess
                        104. | EltGreater
                        105. | EltLessEqual
                        106. | EltGreaterEqual
                        107. | EltEqualScalar
                        108. | EltNotEqualScalar
                        109. | EltLessScalar
                        110. | EltGreaterScalar
                        111. | EltLessEqualScalar
                        112. | EltGreaterEqualScalar
                        113. | Conv1d of Owl_types_common.padding * int array
                        114. | Conv2d of Owl_types_common.padding * int array
                        115. | Conv3d of Owl_types_common.padding * int array
                        116. | TransposeConv1d of Owl_types_common.padding * int array
                        117. | TransposeConv2d of Owl_types_common.padding * int array
                        118. | TransposeConv3d of Owl_types_common.padding * int array
                        119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                        120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                        121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                        122. | MaxPool1d of Owl_types_common.padding * int array * int array
                        123. | MaxPool2d of Owl_types_common.padding * int array * int array
                        124. | MaxPool3d of Owl_types_common.padding * int array * int array
                        125. | AvgPool1d of Owl_types_common.padding * int array * int array
                        126. | AvgPool2d of Owl_types_common.padding * int array * int array
                        127. | AvgPool3d of Owl_types_common.padding * int array * int array
                        128. | UpSampling2d of int array
                        129. | Conv1dBackwardInput of int array
                        130. | Conv1dBackwardKernel of int array
                        131. | Conv2dBackwardInput of int array
                        132. | Conv2dBackwardKernel of int array
                        133. | Conv3dBackwardInput of int array
                        134. | Conv3dBackwardKernel of int array
                        135. | TransposeConv1dBackwardInput of int array
                        136. | TransposeConv1dBackwardKernel of int array
                        137. | TransposeConv2dBackwardInput of int array
                        138. | TransposeConv2dBackwardKernel of int array
                        139. | TransposeConv3dBackwardInput of int array
                        140. | TransposeConv3dBackwardKernel of int array
                        141. | DilatedConv1dBackwardInput of int array * int array
                        142. | DilatedConv1dBackwardKernel of int array * int array
                        143. | DilatedConv2dBackwardInput of int array * int array
                        144. | DilatedConv2dBackwardKernel of int array * int array
                        145. | DilatedConv3dBackwardInput of int array * int array
                        146. | DilatedConv3dBackwardKernel of int array * int array
                        147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                        148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                        149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                        150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                        151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                        152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                        153. | UpSampling2dBackward of int array
                        154. | RowNum
                        155. | ColNum
                        156. | Row
                        157. | Rows of int array
                        158. | CopyRowTo
                        159. | CopyColTo
                        160. | Dot of bool * bool * elt * elt
                        161. | Inv
                        162. | Trace
                        163. | Transpose of int array
                        164. | ToRows
                        165. | OfRows
                        166. | Scalar_Add
                        167. | Scalar_Sub
                        168. | Scalar_Mul
                        169. | Scalar_Div
                        170. | Scalar_Pow
                        171. | Scalar_Atan2
                        172. | Scalar_Abs
                        173. | Scalar_Neg
                        174. | Scalar_Sqr
                        175. | Scalar_Sqrt
                        176. | Scalar_Exp
                        177. | Scalar_Log
                        178. | Scalar_Log2
                        179. | Scalar_Log10
                        180. | Scalar_Signum
                        181. | Scalar_Floor
                        182. | Scalar_Ceil
                        183. | Scalar_Round
                        184. | Scalar_Sin
                        185. | Scalar_Cos
                        186. | Scalar_Tan
                        187. | Scalar_Sinh
                        188. | Scalar_Cosh
                        189. | Scalar_Tanh
                        190. | Scalar_Asin
                        191. | Scalar_Acos
                        192. | Scalar_Atan
                        193. | Scalar_Asinh
                        194. | Scalar_Acosh
                        195. | Scalar_Atanh
                        196. | Scalar_Relu
                        197. | Scalar_Dawsn
                        198. | Scalar_Sigmoid
                        199. | Fused_Adagrad of float * float
                          (*

                          TODO

                          *)
                        diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/index.html index f42ce6488..be3377284 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Flatten_Sig/index.html @@ -1,5 +1,5 @@ -Flatten_Sig (owl-base.Owl_computation_engine_sig.Flatten_Sig)

                        Module type Owl_computation_engine_sig.Flatten_Sig

                        include Owl_types_computation_engine.Sig
                        Core evaluation functions of the engine

                        TODO

                        TODO

                        val eval_graph : Graph.graph -> unit

                        TODO

                        include Owl_computation_graph_sig.Sig
                        Type definition
                        type graph

                        TODO

                        Core functions
                        val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                        TODO

                        val graph_to_dot : graph -> string

                        TODO

                        val graph_to_trace : graph -> string

                        TODO

                        val save_graph : 'a -> string -> unit

                        TODO

                        val load_graph : string -> 'a * 'b

                        TODO

                        val collect_rvs : +Flatten_Sig (owl-base.Owl_computation_engine_sig.Flatten_Sig)

                        Module type Owl_computation_engine_sig.Flatten_Sig

                        include Owl_types_computation_engine.Sig
                        Core evaluation functions of the engine

                        TODO

                        TODO

                        val eval_graph : Graph.graph -> unit

                        TODO

                        include Owl_computation_graph_sig.Sig
                        Type definition
                        type graph

                        TODO

                        Core functions
                        val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                        TODO

                        val graph_to_dot : graph -> string

                        TODO

                        val graph_to_trace : graph -> string

                        TODO

                        val save_graph : 'a -> string -> unit

                        TODO

                        val load_graph : string -> 'a * 'b

                        TODO

                        val invalidate_rvs : graph -> unit

                        TODO

                        val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> @@ -32,60 +32,60 @@ graph -> unit

                        TODO

                        val optimise : graph -> unit

                        TODO

                        include Owl_computation_optimiser_sig.Sig
                        Core functions
                        val estimate_complexity : 'a Owl_graph.node array -> int * int

                        TODO

                        val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> - unit

                        TODO

                        include Owl_computation_operator_sig.Sig
                        Vectorised functions
                        val empty : int array -> Symbol.Shape.Type.arr

                        TODO

                        val zeros : int array -> Symbol.Shape.Type.arr

                        TODO

                        val ones : int array -> Symbol.Shape.Type.arr

                        TODO

                        val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                        TODO

                        val sequential : + unit

                        TODO

                        include Owl_computation_operator_sig.Sig
                        Vectorised functions

                        noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                        val empty : int array -> Symbol.Shape.Type.arr

                        empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                        val zeros : int array -> Symbol.Shape.Type.arr

                        zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                        val ones : int array -> Symbol.Shape.Type.arr

                        ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                        val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                        create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                        val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val uniform : + Symbol.Shape.Type.arr

                        sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                        val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val gaussian : + Symbol.Shape.Type.arr

                        uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                        val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                        TODO

                        val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                        TODO

                        val init_nd : + Symbol.Shape.Type.arr

                        gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                        val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                        bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                        val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                        init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                        val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                        TODO

                        val shape : Symbol.Shape.Type.arr -> int array

                        TODO

                        val numel : Symbol.Shape.Type.arr -> int

                        TODO

                        TODO

                        val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                        TODO

                        val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                        TODO

                        val set_slice : + Symbol.Shape.Type.arr

                        init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                        val shape : Symbol.Shape.Type.arr -> int array

                        shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                        val numel : Symbol.Shape.Type.arr -> int

                        numel arr returns the total number of elements in the array arr.

                        get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                        val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                        set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                        val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                        get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                        val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                        TODO

                        val get_fancy : + unit

                        set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                        val set_fancy : + Symbol.Shape.Type.arr

                        get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                        val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                        TODO

                        val copy_ : out:'a -> 'b -> 'c

                        TODO

                        val reset : Symbol.Shape.Type.arr -> unit

                        TODO

                        val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        TODO

                        val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        TODO

                        val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        TODO

                        val pad : + unit

                        set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                        copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                        val copy_ : out:'a -> 'b -> 'c

                        copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                        val reset : Symbol.Shape.Type.arr -> unit

                        reset arr sets all elements of the array arr to zero.

                        val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                        reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                        val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                        val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                        TODO

                        val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                        TODO

                        val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                        TODO

                        val concatenate : + Symbol.Shape.Type.arr

                        pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                        val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                        expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                        val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                        squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                        val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                        TODO

                        val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                        TODO

                        val concat : + Symbol.Shape.Type.arr

                        concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                        val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                        stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                        val split : ?axis:int -> 'a -> 'b -> 'c

                        TODO

                        concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                        val split : ?axis:int -> 'a -> 'b -> 'c

                        split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                        • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                        val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                        TODO

                        val map : + Symbol.Shape.Type.arr * 'a array

                        draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                        map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                        fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                        TODO

                        val delay : + Symbol.Shape.Type.arr

                        scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                        one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                        delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                        val delay_array : @@ -98,359 +98,359 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                        val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                        TODO

                        lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                        val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                        print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                        • max_row is an optional parameter specifying the maximum number of rows to print.
                        • max_col is an optional parameter specifying the maximum number of columns to print.
                        • header is an optional parameter to include a header in the output.
                        • fmt is an optional parameter to specify the format of the output.

                        abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                        neg arr negates each element in the array arr. Returns a new array with each element negated.

                        floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                        ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                        round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                        sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                        sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                        log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                        log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                        log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                        exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                        sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                        cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                        tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                        sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                        cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                        tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                        asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                        acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                        atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                        asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                        acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                        atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                        val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                        • axis specifies the axis along which to compute the minimum.
                        • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                        val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                        • axis specifies the axis along which to compute the maximum.
                        • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                        val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val sum_reduce : + Symbol.Shape.Type.arr

                        sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                        • axis specifies the axis along which to compute the sum.
                        • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                        val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val log_sum_exp : + Symbol.Shape.Type.arr

                        sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                        • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                        signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                        sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                        relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                        dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                        min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                        max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                        sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                        log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                        val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val clip_by_value : + Symbol.Shape.Type.arr

                        log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                        • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                        • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                        l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                        l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                        l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                        val clip_by_l2norm : + Symbol.Shape.Type.arr

                        clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                        • amin specifies the minimum value to clip to.
                        • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                        clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                        val scalar_pow : + Symbol.Shape.Type.arr

                        pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                        val pow_scalar : + Symbol.Shape.Type.arr

                        scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                        val atan2 : + Symbol.Shape.Type.arr

                        pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                        val scalar_atan2 : + Symbol.Shape.Type.arr

                        atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                        val atan2_scalar : + Symbol.Shape.Type.arr

                        scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                        val hypot : + Symbol.Shape.Type.arr

                        atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                        hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                        min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                        max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                        add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                        sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                        mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                        val add_scalar : + Symbol.Shape.Type.arr

                        div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                        val sub_scalar : + Symbol.Shape.Type.arr

                        add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                        val mul_scalar : + Symbol.Shape.Type.arr

                        sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                        val div_scalar : + Symbol.Shape.Type.arr

                        mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                        val scalar_add : + Symbol.Shape.Type.arr

                        div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                        val scalar_sub : + Symbol.Shape.Type.arr

                        scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                        val scalar_mul : + Symbol.Shape.Type.arr

                        scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                        val scalar_div : + Symbol.Shape.Type.arr

                        scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                        scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                        val elt_equal : + Symbol.Shape.Type.arr

                        fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                        val elt_not_equal : + Symbol.Shape.Type.arr

                        elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                        val elt_less : + Symbol.Shape.Type.arr

                        elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                        val elt_greater : + Symbol.Shape.Type.arr

                        elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                        val elt_less_equal : + Symbol.Shape.Type.arr

                        elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                        val elt_greater_equal : + Symbol.Shape.Type.arr

                        elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                        val elt_equal_scalar : + Symbol.Shape.Type.arr

                        elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                        val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                        elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                        val elt_less_scalar : + Symbol.Shape.Type.arr

                        elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                        val elt_greater_scalar : + Symbol.Shape.Type.arr

                        elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                        val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                        elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                        TODO

                        val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                        elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                        TODO

                        val conv1d : + Symbol.Shape.Type.arr

                        elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                        val conv2d : + Symbol.Shape.Type.arr

                        conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                        • padding specifies the padding strategy (default is "valid").
                        • strides specifies the stride length. Returns a new array with the result of the convolution.
                        val conv3d : + Symbol.Shape.Type.arr

                        conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                        • padding specifies the padding strategy (default is "valid").
                        • strides specifies the stride length. Returns a new array with the result of the convolution.
                        val transpose_conv1d : + Symbol.Shape.Type.arr

                        conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                        • padding specifies the padding strategy (default is "valid").
                        • strides specifies the stride length. Returns a new array with the result of the convolution.
                        val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val transpose_conv2d : + Symbol.Shape.Type.arr

                        transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                        • padding specifies the padding strategy (default is "valid").
                        • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                        val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val transpose_conv3d : + Symbol.Shape.Type.arr

                        transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                        • padding specifies the padding strategy (default is "valid").
                        • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                        val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val dilated_conv1d : + Symbol.Shape.Type.arr

                        transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                        • padding specifies the padding strategy (default is "valid").
                        • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                        val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val dilated_conv2d : + Symbol.Shape.Type.arr

                        dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                        • padding specifies the padding strategy (default is "valid").
                        • strides specifies the stride length.
                        • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                        val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val dilated_conv3d : + Symbol.Shape.Type.arr

                        dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                        • padding specifies the padding strategy (default is "valid").
                        • strides specifies the stride length.
                        • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                        val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val max_pool1d : + Symbol.Shape.Type.arr

                        dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                        • padding specifies the padding strategy (default is "valid").
                        • strides specifies the stride length.
                        • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                        val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val max_pool2d : + Symbol.Shape.Type.arr

                        max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                        • padding specifies the padding strategy (default is "valid").
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length. Returns a new array with the result of the max pooling.
                        val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val max_pool3d : + Symbol.Shape.Type.arr

                        max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                        • padding specifies the padding strategy (default is "valid").
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length. Returns a new array with the result of the max pooling.
                        val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val avg_pool1d : + Symbol.Shape.Type.arr

                        max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                        • padding specifies the padding strategy (default is "valid").
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length. Returns a new array with the result of the max pooling.
                        val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val avg_pool2d : + Symbol.Shape.Type.arr

                        avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                        • padding specifies the padding strategy (default is "valid").
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length. Returns a new array with the result of the average pooling.
                        val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val avg_pool3d : + Symbol.Shape.Type.arr

                        avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                        • padding specifies the padding strategy (default is "valid").
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length. Returns a new array with the result of the average pooling.
                        val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        TODO

                        val conv1d_backward_input : + Symbol.Shape.Type.arr

                        avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                        • padding specifies the padding strategy (default is "valid").
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length. Returns a new array with the result of the average pooling.
                        val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        upsampling2d input size performs a 2-dimensional upsampling on the input array.

                        • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                        TODO

                        val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                        conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                        • input is the original input array.
                        • kernel is the convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                        val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val conv2d_backward_input : + Symbol.Shape.Type.arr

                        conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                        • input is the original input array.
                        • kernel is the convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                        TODO

                        val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                        conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                        • input is the original input array.
                        • kernel is the convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                        val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val conv3d_backward_input : + Symbol.Shape.Type.arr

                        conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                        • input is the original input array.
                        • kernel is the convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                        TODO

                        val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                        conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                        • input is the original input array.
                        • kernel is the convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                        val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                        conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                        • input is the original input array.
                        • kernel is the convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                        val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                        transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                        • input is the original input array.
                        • kernel is the transposed convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                        val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                        transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                        • input is the original input array.
                        • kernel is the transposed convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                        val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                        transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                        • input is the original input array.
                        • kernel is the transposed convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                        val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                        transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                        • input is the original input array.
                        • kernel is the transposed convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                        val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                        transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                        • input is the original input array.
                        • kernel is the transposed convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                        val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                        transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                        • input is the original input array.
                        • kernel is the transposed convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                        val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                        dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                        • input is the original input array.
                        • kernel is the dilated convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • dilations specifies the dilation rate.
                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                        val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                        dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                        • input is the original input array.
                        • kernel is the dilated convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • dilations specifies the dilation rate.
                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                        val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                        dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                        • input is the original input array.
                        • kernel is the dilated convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • dilations specifies the dilation rate.
                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                        val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                        dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                        • input is the original input array.
                        • kernel is the dilated convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • dilations specifies the dilation rate.
                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                        val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                        dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                        • input is the original input array.
                        • kernel is the dilated convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • dilations specifies the dilation rate.
                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                        val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val max_pool1d_backward : + Symbol.Shape.Type.arr

                        dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                        • input is the original input array.
                        • kernel is the dilated convolutional kernel used during the forward pass.
                        • strides specifies the stride length.
                        • dilations specifies the dilation rate.
                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                        val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val max_pool2d_backward : + Symbol.Shape.Type.arr

                        max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                        • padding specifies the padding strategy used during the forward pass.
                        • input is the original input array.
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                        val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val max_pool3d_backward : + Symbol.Shape.Type.arr

                        max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                        • padding specifies the padding strategy used during the forward pass.
                        • input is the original input array.
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                        val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val avg_pool1d_backward : + Symbol.Shape.Type.arr

                        max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                        • padding specifies the padding strategy used during the forward pass.
                        • input is the original input array.
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                        val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val avg_pool2d_backward : + Symbol.Shape.Type.arr

                        avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                        • padding specifies the padding strategy used during the forward pass.
                        • input is the original input array.
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                        val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val avg_pool3d_backward : + Symbol.Shape.Type.arr

                        avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                        • padding specifies the padding strategy used during the forward pass.
                        • input is the original input array.
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                        val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val upsampling2d_backward : + Symbol.Shape.Type.arr

                        avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                        • padding specifies the padding strategy used during the forward pass.
                        • input is the original input array.
                        • pool_size specifies the size of the pooling window.
                        • strides specifies the stride length.
                        • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                        val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val row_num : Symbol.Shape.Type.arr -> int

                        TODO

                        val col_num : Symbol.Shape.Type.arr -> int

                        TODO

                        val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        TODO

                        val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                        TODO

                        val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                        TODO

                        TODO

                        upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                        • input is the original input array.
                        • size specifies the upsampling factors for each dimension.
                        • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                        val row_num : Symbol.Shape.Type.arr -> int

                        row_num arr returns the number of rows in the array arr.

                        val col_num : Symbol.Shape.Type.arr -> int

                        col_num arr returns the number of columns in the array arr.

                        row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                        val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                        rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                        val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                        copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                        val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                        copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                        diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                        trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                        val transpose : + Symbol.Shape.Type.arr

                        dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                        val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val to_rows : Symbol.Shape.Type.arr -> 'a array

                        TODO

                        TODO

                        val to_cols : Symbol.Shape.Type.arr -> 'a array

                        TODO

                        TODO

                        val of_array : + Symbol.Shape.Type.arr

                        transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                        val to_rows : Symbol.Shape.Type.arr -> 'a array

                        to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                        of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                        val to_cols : Symbol.Shape.Type.arr -> 'a array

                        to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                        of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                        val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                        TODO

                        val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                        TODO

                        val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                        TODO

                        Scalar functions
                        module Scalar : sig ... end
                        module Mat : sig ... end
                        module Linalg : sig ... end
                        include Owl_computation_symbol_sig.Sig
                        Core functions
                        val op_to_str : Shape.Type.op -> string

                        TODO

                        val is_random_variable : Shape.Type.op -> bool

                        TODO

                        val refnum : 'a Owl_graph.node -> int

                        TODO

                        val node_shape : Shape.Type.attr Owl_graph.node -> int array

                        TODO

                        val node_numel : Shape.Type.attr Owl_graph.node -> int

                        TODO

                        val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                        TODO

                        val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                        TODO

                        val shape_to_str : int array option array -> string

                        TODO

                        val node_to_str : Shape.Type.attr Owl_graph.node -> string

                        TODO

                        val node_to_arr : Shape.Type.t -> Shape.Type.arr

                        TODO

                        val arr_to_node : Shape.Type.arr -> Shape.Type.t

                        TODO

                        val node_to_elt : Shape.Type.t -> Shape.Type.elt

                        TODO

                        val elt_to_node : Shape.Type.elt -> Shape.Type.t

                        TODO

                        val make_node : + Symbol.Shape.Type.arr

                        of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                        val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                        of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                        val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                        to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                        Scalar functions
                        module Scalar : sig ... end
                        module Mat : sig ... end
                        module Linalg : sig ... end
                        include Owl_computation_symbol_sig.Sig
                        Core functions
                        val op_to_str : Shape.Type.op -> string

                        TODO

                        val is_random_variable : Shape.Type.op -> bool

                        TODO

                        val refnum : 'a Owl_graph.node -> int

                        TODO

                        val node_shape : Shape.Type.attr Owl_graph.node -> int array

                        TODO

                        val node_numel : Shape.Type.attr Owl_graph.node -> int

                        TODO

                        val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                        TODO

                        val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                        TODO

                        val shape_to_str : int array option array -> string

                        TODO

                        val node_to_str : Shape.Type.attr Owl_graph.node -> string

                        TODO

                        val node_to_arr : Shape.Type.t -> Shape.Type.arr

                        TODO

                        val arr_to_node : Shape.Type.arr -> Shape.Type.t

                        TODO

                        val node_to_elt : Shape.Type.t -> Shape.Type.elt

                        TODO

                        val elt_to_node : Shape.Type.elt -> Shape.Type.t

                        TODO

                        val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Linalg/index.html index 6872ed0bf..6f4beb4f5 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Linalg)

                        Module Operator.Linalg

                        val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                        TODO

                        val svd : +Linalg (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Linalg)

                        Module Operator.Linalg

                        inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

                        logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

                        val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                        chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

                        • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

                        qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

                        lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

                        svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

                        • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
                        val lyapunov : + Symbol.Shape.Type.arr

                        sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

                        val discrete_lyapunov : + Symbol.Shape.Type.arr

                        lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

                        val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        val linsolve : + Symbol.Shape.Type.arr

                        discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

                        • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
                        val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                        TODO

                        linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

                        • trans specifies whether to transpose the matrix A.
                        • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

                        care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

                        • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                        + Symbol.Shape.Type.arr

                        dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

                        • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                        diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Mat/index.html index 4e82004f8..8f33718f3 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Mat)

                        Module Operator.Mat

                        val eye : int -> Symbol.Shape.Type.arr

                        TODO

                        TODO

                        TODO

                        TODO

                        +Mat (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Mat)

                        Module Operator.Mat

                        val eye : int -> Symbol.Shape.Type.arr

                        eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

                        diagm ?k v creates a diagonal matrix from the array v.

                        • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

                        triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

                        tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

                        diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Scalar/index.html index 86f64e2bd..23fe4b790 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Scalar)

                        Module Operator.Scalar

                        val add : +Scalar (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Scalar)

                        Module Operator.Scalar

                        add a b returns the sum of the scalars a and b.

                        sub a b returns the difference of the scalars a and b.

                        mul a b returns the product of the scalars a and b.

                        div a b returns the quotient of the scalars a and b.

                        val atan2 : + Symbol.Shape.Type.elt

                        pow a b returns the scalar a raised to the power of b.

                        + Symbol.Shape.Type.elt

                        atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                        abs a returns the absolute value of the scalar a.

                        neg a returns the negation of the scalar a.

                        sqr a returns the square of the scalar a.

                        sqrt a returns the square root of the scalar a.

                        exp a returns the exponential of the scalar a.

                        log a returns the natural logarithm of the scalar a.

                        log2 a returns the base-2 logarithm of the scalar a.

                        log10 a returns the base-10 logarithm of the scalar a.

                        signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                        floor a returns the greatest integer less than or equal to the scalar a.

                        ceil a returns the smallest integer greater than or equal to the scalar a.

                        round a returns the nearest integer to the scalar a.

                        sin a returns the sine of the scalar a.

                        cos a returns the cosine of the scalar a.

                        tan a returns the tangent of the scalar a.

                        sinh a returns the hyperbolic sine of the scalar a.

                        cosh a returns the hyperbolic cosine of the scalar a.

                        tanh a returns the hyperbolic tangent of the scalar a.

                        asin a returns the arcsine of the scalar a.

                        acos a returns the arccosine of the scalar a.

                        atan a returns the arctangent of the scalar a.

                        asinh a returns the inverse hyperbolic sine of the scalar a.

                        acosh a returns the inverse hyperbolic cosine of the scalar a.

                        atanh a returns the inverse hyperbolic tangent of the scalar a.

                        relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                        dawsn a returns Dawson's function of the scalar a.

                        sigmoid a returns the sigmoid function of the scalar a.

                        diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 44f0359ef..d1e56caf0 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                        Module A.Linalg

                        val inv : arr -> arr
                        val logdet : arr -> elt
                        val chol : ?upper:bool -> arr -> arr
                        val svd : ?thin:bool -> arr -> arr * arr * arr
                        val qr : arr -> arr * arr
                        val lq : arr -> arr * arr
                        val sylvester : arr -> arr -> arr -> arr
                        val lyapunov : arr -> arr -> arr
                        val discrete_lyapunov : +Linalg (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                        Module A.Linalg

                        val inv : arr -> arr
                        val logdet : arr -> elt
                        val chol : ?upper:bool -> arr -> arr
                        val svd : ?thin:bool -> arr -> arr * arr * arr
                        val qr : arr -> arr * arr
                        val lq : arr -> arr * arr
                        val sylvester : arr -> arr -> arr -> arr
                        val lyapunov : arr -> arr -> arr
                        val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 6f5885481..39c15f82d 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                        Module A.Mat

                        val diagm : ?k:int -> arr -> arr
                        val triu : ?k:int -> arr -> arr
                        val tril : ?k:int -> arr -> arr
                        val eye : int -> arr
                        +Mat (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                        Module A.Mat

                        val diagm : ?k:int -> arr -> arr
                        val triu : ?k:int -> arr -> arr
                        val tril : ?k:int -> arr -> arr
                        val eye : int -> arr
                        diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index fae707430..862b409e6 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                        Module A.Scalar

                        val add : elt -> elt -> elt
                        val sub : elt -> elt -> elt
                        val mul : elt -> elt -> elt
                        val div : elt -> elt -> elt
                        val pow : elt -> elt -> elt
                        val atan2 : elt -> elt -> elt
                        val abs : elt -> elt
                        val neg : elt -> elt
                        val sqr : elt -> elt
                        val sqrt : elt -> elt
                        val exp : elt -> elt
                        val log : elt -> elt
                        val log2 : elt -> elt
                        val log10 : elt -> elt
                        val signum : elt -> elt
                        val floor : elt -> elt
                        val ceil : elt -> elt
                        val round : elt -> elt
                        val sin : elt -> elt
                        val cos : elt -> elt
                        val tan : elt -> elt
                        val sinh : elt -> elt
                        val cosh : elt -> elt
                        val tanh : elt -> elt
                        val asin : elt -> elt
                        val acos : elt -> elt
                        val atan : elt -> elt
                        val asinh : elt -> elt
                        val acosh : elt -> elt
                        val atanh : elt -> elt
                        val relu : elt -> elt
                        val dawsn : elt -> elt
                        val sigmoid : elt -> elt
                        +Scalar (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                        Module A.Scalar

                        val add : elt -> elt -> elt
                        val sub : elt -> elt -> elt
                        val mul : elt -> elt -> elt
                        val div : elt -> elt -> elt
                        val pow : elt -> elt -> elt
                        val atan2 : elt -> elt -> elt
                        val abs : elt -> elt
                        val neg : elt -> elt
                        val sqr : elt -> elt
                        val sqrt : elt -> elt
                        val exp : elt -> elt
                        val log : elt -> elt
                        val log2 : elt -> elt
                        val log10 : elt -> elt
                        val signum : elt -> elt
                        val floor : elt -> elt
                        val ceil : elt -> elt
                        val round : elt -> elt
                        val sin : elt -> elt
                        val cos : elt -> elt
                        val tan : elt -> elt
                        val sinh : elt -> elt
                        val cosh : elt -> elt
                        val tanh : elt -> elt
                        val asin : elt -> elt
                        val acos : elt -> elt
                        val atan : elt -> elt
                        val asinh : elt -> elt
                        val acosh : elt -> elt
                        val atanh : elt -> elt
                        val relu : elt -> elt
                        val dawsn : elt -> elt
                        val sigmoid : elt -> elt
                        diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index 6ac7c244b..59a04340d 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                        Module Device.A

                        include Owl_types_ndarray_algodiff.Sig
                        include Owl_types_ndarray_eltcmp.Sig
                        include Owl_types_ndarray_basic.Sig
                        type arr
                        type elt
                        val empty : int array -> arr
                        val zeros : int array -> arr
                        val ones : int array -> arr
                        val create : int array -> elt -> arr
                        val sequential : ?a:elt -> ?step:elt -> int array -> arr
                        val uniform : ?a:elt -> ?b:elt -> int array -> arr
                        val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                        val bernoulli : ?p:elt -> int array -> arr
                        val init : int array -> (int -> elt) -> arr
                        val init_nd : int array -> (int array -> elt) -> arr
                        val shape : arr -> int array
                        val numel : arr -> int
                        val get : arr -> int array -> elt
                        val set : arr -> int array -> elt -> unit
                        val get_slice : int list list -> arr -> arr
                        val set_slice : int list list -> arr -> arr -> unit
                        val get_fancy : Owl_types_common.index list -> arr -> arr
                        val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                        val copy : arr -> arr
                        val copy_ : out:arr -> arr -> unit
                        val reset : arr -> unit
                        val reshape : arr -> int array -> arr
                        val reverse : arr -> arr
                        val tile : arr -> int array -> arr
                        val repeat : arr -> int array -> arr
                        val concatenate : ?axis:int -> arr array -> arr
                        val stack : ?axis:int -> arr array -> arr
                        val split : ?axis:int -> int array -> arr -> arr array
                        val expand : ?hi:bool -> arr -> int -> arr
                        val squeeze : ?axis:int array -> arr -> arr
                        val draw : ?axis:int -> arr -> int -> arr * int array
                        val map : (elt -> elt) -> arr -> arr
                        val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                        val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                        val one_hot : int -> arr -> arr
                        val pad : ?v:elt -> int list list -> arr -> arr
                        val print : +A (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                        Module Device.A

                        include Owl_types_ndarray_algodiff.Sig
                        include Owl_types_ndarray_eltcmp.Sig
                        include Owl_types_ndarray_basic.Sig
                        type arr
                        type elt
                        val empty : int array -> arr
                        val zeros : int array -> arr
                        val ones : int array -> arr
                        val create : int array -> elt -> arr
                        val sequential : ?a:elt -> ?step:elt -> int array -> arr
                        val uniform : ?a:elt -> ?b:elt -> int array -> arr
                        val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                        val bernoulli : ?p:elt -> int array -> arr
                        val init : int array -> (int -> elt) -> arr
                        val init_nd : int array -> (int array -> elt) -> arr
                        val shape : arr -> int array
                        val numel : arr -> int
                        val get : arr -> int array -> elt
                        val set : arr -> int array -> elt -> unit
                        val get_slice : int list list -> arr -> arr
                        val set_slice : int list list -> arr -> arr -> unit
                        val get_fancy : Owl_types_common.index list -> arr -> arr
                        val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                        val copy : arr -> arr
                        val copy_ : out:arr -> arr -> unit
                        val reset : arr -> unit
                        val reshape : arr -> int array -> arr
                        val reverse : arr -> arr
                        val tile : arr -> int array -> arr
                        val repeat : arr -> int array -> arr
                        val concatenate : ?axis:int -> arr array -> arr
                        val stack : ?axis:int -> arr array -> arr
                        val split : ?axis:int -> int array -> arr -> arr array
                        val expand : ?hi:bool -> arr -> int -> arr
                        val squeeze : ?axis:int array -> arr -> arr
                        val draw : ?axis:int -> arr -> int -> arr * int array
                        val map : (elt -> elt) -> arr -> arr
                        val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                        val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                        val one_hot : int -> arr -> arr
                        val pad : ?v:elt -> int list list -> arr -> arr
                        val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index cca088f91..dfb60dae5 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device)

                        Module Type.Device

                        Type definition
                        type device

                        TODO

                        type value

                        TODO

                        Core functions
                        val make_device : unit -> device

                        TODO

                        val arr_to_value : A.arr -> value

                        TODO

                        val value_to_arr : value -> A.arr

                        TODO

                        val elt_to_value : A.elt -> value

                        TODO

                        val value_to_elt : value -> A.elt

                        TODO

                        val value_to_float : value -> float

                        TODO

                        val is_arr : value -> bool

                        TODO

                        val is_elt : value -> bool

                        TODO

                        +Device (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type.Device)

                        Module Type.Device

                        Type definition
                        type device

                        TODO

                        type value

                        TODO

                        Core functions
                        val make_device : unit -> device

                        TODO

                        val arr_to_value : A.arr -> value

                        TODO

                        val value_to_arr : value -> A.arr

                        TODO

                        val elt_to_value : A.elt -> value

                        TODO

                        val value_to_elt : value -> A.elt

                        TODO

                        val value_to_float : value -> float

                        TODO

                        val is_arr : value -> bool

                        TODO

                        val is_elt : value -> bool

                        TODO

                        diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/index.html index 03b39a75a..fad59d49e 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type)

                        Module Shape.Type

                        Type definition
                        type state =
                        1. | Valid
                        2. | Invalid
                          (*

                          TODO

                          *)

                        TODO

                        and block = {
                        1. size : int;
                        2. block_id : int;
                        3. mutable active : t option;
                        4. mutable memory : Device.value;
                        5. mutable nodes : t list;
                        }

                        block type keeps a reference to a block of memory and to the nodes sharing that block.

                        and attr = {
                        1. mutable op : op;
                        2. mutable freeze : bool;
                        3. mutable reuse : bool;
                        4. mutable state : state;
                        5. mutable shape : int array option array;
                        6. mutable value : Device.value array;
                        7. mutable block : block array option;
                        }

                        TODO

                        and arr =
                        1. | Arr of t
                        and elt =
                        1. | Elt of t
                        and op =
                        1. | Noop
                        2. | Var
                        3. | Const
                        4. | Empty of int array
                        5. | Zeros of int array
                        6. | Ones of int array
                        7. | Create of int array
                        8. | Sequential of int array
                        9. | Uniform of int array
                        10. | Gaussian of int array
                        11. | Bernoulli of int array
                        12. | Init of int array * int -> elt
                        13. | Get of int array
                        14. | Set of int array
                        15. | GetSlice of int list list
                        16. | SetSlice of int list list
                        17. | GetFancy of Owl_types_common.index list
                        18. | SetFancy of Owl_types_common.index list
                        19. | Copy
                        20. | Reset
                        21. | Reshape of int array
                        22. | Reverse
                        23. | Tile of int array
                        24. | Repeat of int array
                        25. | Pad of elt * int list list
                        26. | Concatenate of int
                        27. | Stack of int
                        28. | Split of int * int array
                        29. | Draw of int * int
                        30. | Map of elt -> elt
                        31. | Fold of int * elt -> elt -> elt
                        32. | Scan of int * elt -> elt -> elt
                        33. | OneHot of int
                        34. | OfArray of int array
                        35. | Delay of Device.A.arr -> Device.A.arr
                        36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                        37. | LazyPrint of int option +Type (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape.Type)

                          Module Shape.Type

                          Type definition
                          type state =
                          1. | Valid
                          2. | Invalid
                            (*

                            TODO

                            *)

                          TODO

                          and block = {
                          1. size : int;
                          2. block_id : int;
                          3. mutable active : t option;
                          4. mutable memory : Device.value;
                          5. mutable nodes : t list;
                          }

                          block type keeps a reference to a block of memory and to the nodes sharing that block.

                          and attr = {
                          1. mutable op : op;
                          2. mutable freeze : bool;
                          3. mutable reuse : bool;
                          4. mutable state : state;
                          5. mutable shape : int array option array;
                          6. mutable value : Device.value array;
                          7. mutable block : block array option;
                          }

                          TODO

                          and arr =
                          1. | Arr of t
                          and elt =
                          1. | Elt of t
                          and op =
                          1. | Noop
                          2. | Var
                          3. | Const
                          4. | Empty of int array
                          5. | Zeros of int array
                          6. | Ones of int array
                          7. | Create of int array
                          8. | Sequential of int array
                          9. | Uniform of int array
                          10. | Gaussian of int array
                          11. | Bernoulli of int array
                          12. | Init of int array * int -> elt
                          13. | Get of int array
                          14. | Set of int array
                          15. | GetSlice of int list list
                          16. | SetSlice of int list list
                          17. | GetFancy of Owl_types_common.index list
                          18. | SetFancy of Owl_types_common.index list
                          19. | Copy
                          20. | Reset
                          21. | Reshape of int array
                          22. | Reverse
                          23. | Tile of int array
                          24. | Repeat of int array
                          25. | Pad of elt * int list list
                          26. | Concatenate of int
                          27. | Stack of int
                          28. | Split of int * int array
                          29. | Draw of int * int
                          30. | Map of elt -> elt
                          31. | Fold of int * elt -> elt -> elt
                          32. | Scan of int * elt -> elt -> elt
                          33. | OneHot of int
                          34. | OfArray of int array
                          35. | Delay of Device.A.arr -> Device.A.arr
                          36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                          37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                          38. | Abs
                          39. | Neg
                          40. | Floor
                          41. | Ceil
                          42. | Round
                          43. | Sqr
                          44. | Sqrt
                          45. | Log
                          46. | Log2
                          47. | Log10
                          48. | Exp
                          49. | Sin
                          50. | Cos
                          51. | Tan
                          52. | Sinh
                          53. | Cosh
                          54. | Tanh
                          55. | Asin
                          56. | Acos
                          57. | Atan
                          58. | Asinh
                          59. | Acosh
                          60. | Atanh
                          61. | Min of bool * int
                          62. | Max of bool * int
                          63. | Sum of bool * int
                          64. | SumReduce of int array
                          65. | Signum
                          66. | Sigmoid
                          67. | Relu
                          68. | Dawsn
                          69. | Min'
                          70. | Max'
                          71. | Sum'
                          72. | LogSumExp'
                          73. | LogSumExp of bool * int
                          74. | L1norm'
                          75. | L2norm'
                          76. | L2NormSqr'
                          77. | ClipByValue
                          78. | ClipByL2norm
                          79. | Pow
                          80. | ScalarPow
                          81. | PowScalar
                          82. | Atan2
                          83. | ScalarAtan2
                          84. | Atan2Scalar
                          85. | Hypot
                          86. | Min2
                          87. | Max2
                          88. | Add
                          89. | Sub
                          90. | Mul
                          91. | Div
                          92. | AddScalar
                          93. | SubScalar
                          94. | MulScalar
                          95. | DivScalar
                          96. | ScalarAdd
                          97. | ScalarSub
                          98. | ScalarMul
                          99. | ScalarDiv
                          100. | FMA
                          101. | EltEqual
                          102. | EltNotEqual
                          103. | EltLess
                          104. | EltGreater
                          105. | EltLessEqual
                          106. | EltGreaterEqual
                          107. | EltEqualScalar
                          108. | EltNotEqualScalar
                          109. | EltLessScalar
                          110. | EltGreaterScalar
                          111. | EltLessEqualScalar
                          112. | EltGreaterEqualScalar
                          113. | Conv1d of Owl_types_common.padding * int array
                          114. | Conv2d of Owl_types_common.padding * int array
                          115. | Conv3d of Owl_types_common.padding * int array
                          116. | TransposeConv1d of Owl_types_common.padding * int array
                          117. | TransposeConv2d of Owl_types_common.padding * int array
                          118. | TransposeConv3d of Owl_types_common.padding * int array
                          119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                          120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                          121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                          122. | MaxPool1d of Owl_types_common.padding * int array * int array
                          123. | MaxPool2d of Owl_types_common.padding * int array * int array
                          124. | MaxPool3d of Owl_types_common.padding * int array * int array
                          125. | AvgPool1d of Owl_types_common.padding * int array * int array
                          126. | AvgPool2d of Owl_types_common.padding * int array * int array
                          127. | AvgPool3d of Owl_types_common.padding * int array * int array
                          128. | UpSampling2d of int array
                          129. | Conv1dBackwardInput of int array
                          130. | Conv1dBackwardKernel of int array
                          131. | Conv2dBackwardInput of int array
                          132. | Conv2dBackwardKernel of int array
                          133. | Conv3dBackwardInput of int array
                          134. | Conv3dBackwardKernel of int array
                          135. | TransposeConv1dBackwardInput of int array
                          136. | TransposeConv1dBackwardKernel of int array
                          137. | TransposeConv2dBackwardInput of int array
                          138. | TransposeConv2dBackwardKernel of int array
                          139. | TransposeConv3dBackwardInput of int array
                          140. | TransposeConv3dBackwardKernel of int array
                          141. | DilatedConv1dBackwardInput of int array * int array
                          142. | DilatedConv1dBackwardKernel of int array * int array
                          143. | DilatedConv2dBackwardInput of int array * int array
                          144. | DilatedConv2dBackwardKernel of int array * int array
                          145. | DilatedConv3dBackwardInput of int array * int array
                          146. | DilatedConv3dBackwardKernel of int array * int array
                          147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                          148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                          149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                          150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                          151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                          152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                          153. | UpSampling2dBackward of int array
                          154. | RowNum
                          155. | ColNum
                          156. | Row
                          157. | Rows of int array
                          158. | CopyRowTo
                          159. | CopyColTo
                          160. | Dot of bool * bool * elt * elt
                          161. | Inv
                          162. | Trace
                          163. | Transpose of int array
                          164. | ToRows
                          165. | OfRows
                          166. | Scalar_Add
                          167. | Scalar_Sub
                          168. | Scalar_Mul
                          169. | Scalar_Div
                          170. | Scalar_Pow
                          171. | Scalar_Atan2
                          172. | Scalar_Abs
                          173. | Scalar_Neg
                          174. | Scalar_Sqr
                          175. | Scalar_Sqrt
                          176. | Scalar_Exp
                          177. | Scalar_Log
                          178. | Scalar_Log2
                          179. | Scalar_Log10
                          180. | Scalar_Signum
                          181. | Scalar_Floor
                          182. | Scalar_Ceil
                          183. | Scalar_Round
                          184. | Scalar_Sin
                          185. | Scalar_Cos
                          186. | Scalar_Tan
                          187. | Scalar_Sinh
                          188. | Scalar_Cosh
                          189. | Scalar_Tanh
                          190. | Scalar_Asin
                          191. | Scalar_Acos
                          192. | Scalar_Atan
                          193. | Scalar_Asinh
                          194. | Scalar_Acosh
                          195. | Scalar_Atanh
                          196. | Scalar_Relu
                          197. | Scalar_Dawsn
                          198. | Scalar_Sigmoid
                          199. | Fused_Adagrad of float * float
                            (*

                            TODO

                            *)
                          diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/index.html index 13fbb0b76..a6844387d 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape)

                          Module Symbol.Shape

                          Core functions
                          val infer_shape : +Shape (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol.Shape)

                          Module Symbol.Shape

                          Core functions
                          val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                          TODO

                          diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/index.html index cf4fad5bd..9ef99a2f9 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol)

                          Module Operator.Symbol

                          Core functions
                          val op_to_str : Shape.Type.op -> string

                          TODO

                          val is_random_variable : Shape.Type.op -> bool

                          TODO

                          val refnum : 'a Owl_graph.node -> int

                          TODO

                          val node_shape : Shape.Type.attr Owl_graph.node -> int array

                          TODO

                          val node_numel : Shape.Type.attr Owl_graph.node -> int

                          TODO

                          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                          TODO

                          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                          TODO

                          val shape_to_str : int array option array -> string

                          TODO

                          val node_to_str : Shape.Type.attr Owl_graph.node -> string

                          TODO

                          val node_to_arr : Shape.Type.t -> Shape.Type.arr

                          TODO

                          val arr_to_node : Shape.Type.arr -> Shape.Type.t

                          TODO

                          val node_to_elt : Shape.Type.t -> Shape.Type.elt

                          TODO

                          val elt_to_node : Shape.Type.elt -> Shape.Type.t

                          TODO

                          val make_node : +Symbol (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator.Symbol)

                          Module Operator.Symbol

                          Core functions
                          val op_to_str : Shape.Type.op -> string

                          TODO

                          val is_random_variable : Shape.Type.op -> bool

                          TODO

                          val refnum : 'a Owl_graph.node -> int

                          TODO

                          val node_shape : Shape.Type.attr Owl_graph.node -> int array

                          TODO

                          val node_numel : Shape.Type.attr Owl_graph.node -> int

                          TODO

                          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                          TODO

                          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                          TODO

                          val shape_to_str : int array option array -> string

                          TODO

                          val node_to_str : Shape.Type.attr Owl_graph.node -> string

                          TODO

                          val node_to_arr : Shape.Type.t -> Shape.Type.arr

                          TODO

                          val arr_to_node : Shape.Type.arr -> Shape.Type.t

                          TODO

                          val node_to_elt : Shape.Type.t -> Shape.Type.elt

                          TODO

                          val elt_to_node : Shape.Type.elt -> Shape.Type.t

                          TODO

                          val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/index.html index ca2cdfc2b..6d818e93b 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator)

                          Module Optimiser.Operator

                          Vectorised functions
                          val empty : int array -> Symbol.Shape.Type.arr

                          TODO

                          val zeros : int array -> Symbol.Shape.Type.arr

                          TODO

                          val ones : int array -> Symbol.Shape.Type.arr

                          TODO

                          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                          TODO

                          val sequential : +Operator (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser.Operator)

                          Module Optimiser.Operator

                          Vectorised functions

                          noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                          val empty : int array -> Symbol.Shape.Type.arr

                          empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                          val zeros : int array -> Symbol.Shape.Type.arr

                          zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                          val ones : int array -> Symbol.Shape.Type.arr

                          ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                          create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                          val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val uniform : + Symbol.Shape.Type.arr

                          sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                          val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val gaussian : + Symbol.Shape.Type.arr

                          uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                          val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                          TODO

                          val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                          TODO

                          val init_nd : + Symbol.Shape.Type.arr

                          gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                          val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                          bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                          val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                          init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                          val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                          TODO

                          val shape : Symbol.Shape.Type.arr -> int array

                          TODO

                          val numel : Symbol.Shape.Type.arr -> int

                          TODO

                          TODO

                          val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                          TODO

                          val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                          TODO

                          val set_slice : + Symbol.Shape.Type.arr

                          init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                          val shape : Symbol.Shape.Type.arr -> int array

                          shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                          val numel : Symbol.Shape.Type.arr -> int

                          numel arr returns the total number of elements in the array arr.

                          get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                          val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                          set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                          val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                          get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                          val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                          TODO

                          val get_fancy : + unit

                          set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                          val set_fancy : + Symbol.Shape.Type.arr

                          get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                          val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                          TODO

                          val copy_ : out:'a -> 'b -> 'c

                          TODO

                          val reset : Symbol.Shape.Type.arr -> unit

                          TODO

                          val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          TODO

                          val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          TODO

                          val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          TODO

                          val pad : + unit

                          set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                          copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                          val copy_ : out:'a -> 'b -> 'c

                          copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                          val reset : Symbol.Shape.Type.arr -> unit

                          reset arr sets all elements of the array arr to zero.

                          val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                          reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                          val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                          val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                          TODO

                          val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                          TODO

                          val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                          TODO

                          val concatenate : + Symbol.Shape.Type.arr

                          pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                          val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                          expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                          val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                          squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                          val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                          TODO

                          val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                          TODO

                          val concat : + Symbol.Shape.Type.arr

                          concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                          val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                          stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                          val split : ?axis:int -> 'a -> 'b -> 'c

                          TODO

                          concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                          val split : ?axis:int -> 'a -> 'b -> 'c

                          split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                          • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                          val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                          TODO

                          val map : + Symbol.Shape.Type.arr * 'a array

                          draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                          map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                          fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                          TODO

                          val delay : + Symbol.Shape.Type.arr

                          scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                          one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                          delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                          val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                          val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                          TODO

                          lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                          val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                          print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                          • max_row is an optional parameter specifying the maximum number of rows to print.
                          • max_col is an optional parameter specifying the maximum number of columns to print.
                          • header is an optional parameter to include a header in the output.
                          • fmt is an optional parameter to specify the format of the output.

                          abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                          neg arr negates each element in the array arr. Returns a new array with each element negated.

                          floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                          ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                          round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                          sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                          sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                          log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                          log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                          log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                          exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                          sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                          cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                          tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                          sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                          cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                          tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                          asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                          acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                          atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                          asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                          acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                          atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                          val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                          • axis specifies the axis along which to compute the minimum.
                          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                          val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                          • axis specifies the axis along which to compute the maximum.
                          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                          val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val sum_reduce : + Symbol.Shape.Type.arr

                          sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                          • axis specifies the axis along which to compute the sum.
                          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                          val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val log_sum_exp : + Symbol.Shape.Type.arr

                          sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                          • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                          signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                          sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                          relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                          dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                          min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                          max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                          sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                          log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val clip_by_value : + Symbol.Shape.Type.arr

                          log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                          • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                          • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                          l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                          l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                          l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                          val clip_by_l2norm : + Symbol.Shape.Type.arr

                          clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                          • amin specifies the minimum value to clip to.
                          • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                          clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                          val scalar_pow : + Symbol.Shape.Type.arr

                          pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                          val pow_scalar : + Symbol.Shape.Type.arr

                          scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                          val atan2 : + Symbol.Shape.Type.arr

                          pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                          val scalar_atan2 : + Symbol.Shape.Type.arr

                          atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                          val atan2_scalar : + Symbol.Shape.Type.arr

                          scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                          val hypot : + Symbol.Shape.Type.arr

                          atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                          hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                          min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                          max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                          add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                          sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                          mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                          val add_scalar : + Symbol.Shape.Type.arr

                          div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                          val sub_scalar : + Symbol.Shape.Type.arr

                          add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                          val mul_scalar : + Symbol.Shape.Type.arr

                          sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                          val div_scalar : + Symbol.Shape.Type.arr

                          mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                          val scalar_add : + Symbol.Shape.Type.arr

                          div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                          val scalar_sub : + Symbol.Shape.Type.arr

                          scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                          val scalar_mul : + Symbol.Shape.Type.arr

                          scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                          val scalar_div : + Symbol.Shape.Type.arr

                          scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                          scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                          val elt_equal : + Symbol.Shape.Type.arr

                          fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                          val elt_not_equal : + Symbol.Shape.Type.arr

                          elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                          val elt_less : + Symbol.Shape.Type.arr

                          elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                          val elt_greater : + Symbol.Shape.Type.arr

                          elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                          val elt_less_equal : + Symbol.Shape.Type.arr

                          elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                          val elt_greater_equal : + Symbol.Shape.Type.arr

                          elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                          val elt_equal_scalar : + Symbol.Shape.Type.arr

                          elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                          val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                          elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                          val elt_less_scalar : + Symbol.Shape.Type.arr

                          elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                          val elt_greater_scalar : + Symbol.Shape.Type.arr

                          elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                          val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                          elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                          TODO

                          val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                          elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                          TODO

                          val conv1d : + Symbol.Shape.Type.arr

                          elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                          val conv2d : + Symbol.Shape.Type.arr

                          conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                          • padding specifies the padding strategy (default is "valid").
                          • strides specifies the stride length. Returns a new array with the result of the convolution.
                          val conv3d : + Symbol.Shape.Type.arr

                          conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                          • padding specifies the padding strategy (default is "valid").
                          • strides specifies the stride length. Returns a new array with the result of the convolution.
                          val transpose_conv1d : + Symbol.Shape.Type.arr

                          conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                          • padding specifies the padding strategy (default is "valid").
                          • strides specifies the stride length. Returns a new array with the result of the convolution.
                          val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val transpose_conv2d : + Symbol.Shape.Type.arr

                          transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                          • padding specifies the padding strategy (default is "valid").
                          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                          val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val transpose_conv3d : + Symbol.Shape.Type.arr

                          transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                          • padding specifies the padding strategy (default is "valid").
                          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                          val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val dilated_conv1d : + Symbol.Shape.Type.arr

                          transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                          • padding specifies the padding strategy (default is "valid").
                          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                          val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val dilated_conv2d : + Symbol.Shape.Type.arr

                          dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                          • padding specifies the padding strategy (default is "valid").
                          • strides specifies the stride length.
                          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                          val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val dilated_conv3d : + Symbol.Shape.Type.arr

                          dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                          • padding specifies the padding strategy (default is "valid").
                          • strides specifies the stride length.
                          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                          val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val max_pool1d : + Symbol.Shape.Type.arr

                          dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                          • padding specifies the padding strategy (default is "valid").
                          • strides specifies the stride length.
                          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                          val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val max_pool2d : + Symbol.Shape.Type.arr

                          max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                          • padding specifies the padding strategy (default is "valid").
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length. Returns a new array with the result of the max pooling.
                          val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val max_pool3d : + Symbol.Shape.Type.arr

                          max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                          • padding specifies the padding strategy (default is "valid").
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length. Returns a new array with the result of the max pooling.
                          val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val avg_pool1d : + Symbol.Shape.Type.arr

                          max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                          • padding specifies the padding strategy (default is "valid").
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length. Returns a new array with the result of the max pooling.
                          val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val avg_pool2d : + Symbol.Shape.Type.arr

                          avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                          • padding specifies the padding strategy (default is "valid").
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length. Returns a new array with the result of the average pooling.
                          val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val avg_pool3d : + Symbol.Shape.Type.arr

                          avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                          • padding specifies the padding strategy (default is "valid").
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length. Returns a new array with the result of the average pooling.
                          val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          TODO

                          val conv1d_backward_input : + Symbol.Shape.Type.arr

                          avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                          • padding specifies the padding strategy (default is "valid").
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length. Returns a new array with the result of the average pooling.
                          val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          upsampling2d input size performs a 2-dimensional upsampling on the input array.

                          • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                          TODO

                          val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                          conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                          • input is the original input array.
                          • kernel is the convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                          val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val conv2d_backward_input : + Symbol.Shape.Type.arr

                          conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                          • input is the original input array.
                          • kernel is the convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                          TODO

                          val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                          conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                          • input is the original input array.
                          • kernel is the convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                          val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val conv3d_backward_input : + Symbol.Shape.Type.arr

                          conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                          • input is the original input array.
                          • kernel is the convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                          TODO

                          val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                          conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                          • input is the original input array.
                          • kernel is the convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                          val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                          conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                          • input is the original input array.
                          • kernel is the convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                          val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                          transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                          • input is the original input array.
                          • kernel is the transposed convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                          val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                          transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                          • input is the original input array.
                          • kernel is the transposed convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                          val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                          transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                          • input is the original input array.
                          • kernel is the transposed convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                          val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                          transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                          • input is the original input array.
                          • kernel is the transposed convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                          val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                          transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                          • input is the original input array.
                          • kernel is the transposed convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                          val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                          transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                          • input is the original input array.
                          • kernel is the transposed convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                          val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                          dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                          • input is the original input array.
                          • kernel is the dilated convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • dilations specifies the dilation rate.
                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                          val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                          dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                          • input is the original input array.
                          • kernel is the dilated convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • dilations specifies the dilation rate.
                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                          val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                          dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                          • input is the original input array.
                          • kernel is the dilated convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • dilations specifies the dilation rate.
                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                          val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                          dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                          • input is the original input array.
                          • kernel is the dilated convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • dilations specifies the dilation rate.
                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                          val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                          dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                          • input is the original input array.
                          • kernel is the dilated convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • dilations specifies the dilation rate.
                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                          val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val max_pool1d_backward : + Symbol.Shape.Type.arr

                          dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                          • input is the original input array.
                          • kernel is the dilated convolutional kernel used during the forward pass.
                          • strides specifies the stride length.
                          • dilations specifies the dilation rate.
                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                          val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val max_pool2d_backward : + Symbol.Shape.Type.arr

                          max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                          • padding specifies the padding strategy used during the forward pass.
                          • input is the original input array.
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                          val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val max_pool3d_backward : + Symbol.Shape.Type.arr

                          max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                          • padding specifies the padding strategy used during the forward pass.
                          • input is the original input array.
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                          val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val avg_pool1d_backward : + Symbol.Shape.Type.arr

                          max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                          • padding specifies the padding strategy used during the forward pass.
                          • input is the original input array.
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                          val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val avg_pool2d_backward : + Symbol.Shape.Type.arr

                          avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                          • padding specifies the padding strategy used during the forward pass.
                          • input is the original input array.
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                          val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val avg_pool3d_backward : + Symbol.Shape.Type.arr

                          avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                          • padding specifies the padding strategy used during the forward pass.
                          • input is the original input array.
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                          val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val upsampling2d_backward : + Symbol.Shape.Type.arr

                          avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                          • padding specifies the padding strategy used during the forward pass.
                          • input is the original input array.
                          • pool_size specifies the size of the pooling window.
                          • strides specifies the stride length.
                          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                          val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val row_num : Symbol.Shape.Type.arr -> int

                          TODO

                          val col_num : Symbol.Shape.Type.arr -> int

                          TODO

                          val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          TODO

                          val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                          TODO

                          val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                          TODO

                          TODO

                          upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                          • input is the original input array.
                          • size specifies the upsampling factors for each dimension.
                          • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                          val row_num : Symbol.Shape.Type.arr -> int

                          row_num arr returns the number of rows in the array arr.

                          val col_num : Symbol.Shape.Type.arr -> int

                          col_num arr returns the number of columns in the array arr.

                          row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                          val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                          rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                          val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                          copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                          val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                          copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                          diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                          trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                          val transpose : + Symbol.Shape.Type.arr

                          dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                          val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val to_rows : Symbol.Shape.Type.arr -> 'a array

                          TODO

                          TODO

                          val to_cols : Symbol.Shape.Type.arr -> 'a array

                          TODO

                          TODO

                          val of_array : + Symbol.Shape.Type.arr

                          transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                          val to_rows : Symbol.Shape.Type.arr -> 'a array

                          to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                          of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                          val to_cols : Symbol.Shape.Type.arr -> 'a array

                          to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                          of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                          val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                          TODO

                          val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                          TODO

                          val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                          TODO

                          Scalar functions
                          module Scalar : sig ... end
                          module Mat : sig ... end
                          module Linalg : sig ... end
                          + Symbol.Shape.Type.arr

                          of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                          val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                          of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                          val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                          to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                          Scalar functions
                          module Scalar : sig ... end
                          module Mat : sig ... end
                          module Linalg : sig ... end
                          diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/index.html index 7b1411c09..397a8e47b 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser)

                          Module Make_Graph_Sig.Optimiser

                          Core functions
                          val estimate_complexity : 'a Owl_graph.node array -> int * int

                          TODO

                          val optimise_nodes : +Optimiser (owl-base.Owl_computation_engine_sig.Make_Graph_Sig.Optimiser)

                          Module Make_Graph_Sig.Optimiser

                          Core functions
                          val estimate_complexity : 'a Owl_graph.node array -> int * int

                          TODO

                          val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

                          TODO

                          diff --git a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/index.html b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/index.html index dda9828c7..15b81226e 100644 --- a/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/index.html +++ b/docs/owl-base/Owl_computation_engine_sig/module-type-Make_Graph_Sig/index.html @@ -1,5 +1,5 @@ -Make_Graph_Sig (owl-base.Owl_computation_engine_sig.Make_Graph_Sig)

                          Module type Owl_computation_engine_sig.Make_Graph_Sig

                          include Owl_computation_graph_sig.Sig
                          Type definition
                          type graph

                          TODO

                          Core functions
                          val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                          TODO

                          val graph_to_dot : graph -> string

                          TODO

                          val graph_to_trace : graph -> string

                          TODO

                          val save_graph : 'a -> string -> unit

                          TODO

                          val load_graph : string -> 'a * 'b

                          TODO

                          val collect_rvs : +Make_Graph_Sig (owl-base.Owl_computation_engine_sig.Make_Graph_Sig)

                          Module type Owl_computation_engine_sig.Make_Graph_Sig

                          include Owl_computation_graph_sig.Sig
                          Type definition
                          type graph

                          TODO

                          Core functions
                          val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                          TODO

                          val graph_to_dot : graph -> string

                          TODO

                          val graph_to_trace : graph -> string

                          TODO

                          val save_graph : 'a -> string -> unit

                          TODO

                          val load_graph : string -> 'a * 'b

                          TODO

                          val invalidate_rvs : graph -> unit

                          TODO

                          val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Linalg/index.html index 04e25a4aa..992d3a41d 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Linalg)

                          Module Operator.Linalg

                          val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                          TODO

                          val svd : +Linalg (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Linalg)

                          Module Operator.Linalg

                          inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

                          logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

                          val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                          chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

                          • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

                          qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

                          lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

                          svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

                          • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
                          val lyapunov : + Symbol.Shape.Type.arr

                          sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

                          val discrete_lyapunov : + Symbol.Shape.Type.arr

                          lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          val linsolve : + Symbol.Shape.Type.arr

                          discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

                          • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
                          val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                          TODO

                          linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

                          • trans specifies whether to transpose the matrix A.
                          • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

                          care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

                          • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                          + Symbol.Shape.Type.arr

                          dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

                          • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                          diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Mat/index.html index 001e72297..9ecb7514f 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Mat)

                          Module Operator.Mat

                          val eye : int -> Symbol.Shape.Type.arr

                          TODO

                          TODO

                          TODO

                          TODO

                          +Mat (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Mat)

                          Module Operator.Mat

                          val eye : int -> Symbol.Shape.Type.arr

                          eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

                          diagm ?k v creates a diagonal matrix from the array v.

                          • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

                          triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

                          tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

                          diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Scalar/index.html index 90f75c5be..b33bfeb21 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Scalar)

                          Module Operator.Scalar

                          val add : +Scalar (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Scalar)

                          Module Operator.Scalar

                          add a b returns the sum of the scalars a and b.

                          sub a b returns the difference of the scalars a and b.

                          mul a b returns the product of the scalars a and b.

                          div a b returns the quotient of the scalars a and b.

                          val atan2 : + Symbol.Shape.Type.elt

                          pow a b returns the scalar a raised to the power of b.

                          + Symbol.Shape.Type.elt

                          atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                          abs a returns the absolute value of the scalar a.

                          neg a returns the negation of the scalar a.

                          sqr a returns the square of the scalar a.

                          sqrt a returns the square root of the scalar a.

                          exp a returns the exponential of the scalar a.

                          log a returns the natural logarithm of the scalar a.

                          log2 a returns the base-2 logarithm of the scalar a.

                          log10 a returns the base-10 logarithm of the scalar a.

                          signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                          floor a returns the greatest integer less than or equal to the scalar a.

                          ceil a returns the smallest integer greater than or equal to the scalar a.

                          round a returns the nearest integer to the scalar a.

                          sin a returns the sine of the scalar a.

                          cos a returns the cosine of the scalar a.

                          tan a returns the tangent of the scalar a.

                          sinh a returns the hyperbolic sine of the scalar a.

                          cosh a returns the hyperbolic cosine of the scalar a.

                          tanh a returns the hyperbolic tangent of the scalar a.

                          asin a returns the arcsine of the scalar a.

                          acos a returns the arccosine of the scalar a.

                          atan a returns the arctangent of the scalar a.

                          asinh a returns the inverse hyperbolic sine of the scalar a.

                          acosh a returns the inverse hyperbolic cosine of the scalar a.

                          atanh a returns the inverse hyperbolic tangent of the scalar a.

                          relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                          dawsn a returns Dawson's function of the scalar a.

                          sigmoid a returns the sigmoid function of the scalar a.

                          diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 94032620d..fff6b2429 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                          Module A.Linalg

                          val inv : arr -> arr
                          val logdet : arr -> elt
                          val chol : ?upper:bool -> arr -> arr
                          val svd : ?thin:bool -> arr -> arr * arr * arr
                          val qr : arr -> arr * arr
                          val lq : arr -> arr * arr
                          val sylvester : arr -> arr -> arr -> arr
                          val lyapunov : arr -> arr -> arr
                          val discrete_lyapunov : +Linalg (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                          Module A.Linalg

                          val inv : arr -> arr
                          val logdet : arr -> elt
                          val chol : ?upper:bool -> arr -> arr
                          val svd : ?thin:bool -> arr -> arr * arr * arr
                          val qr : arr -> arr * arr
                          val lq : arr -> arr * arr
                          val sylvester : arr -> arr -> arr -> arr
                          val lyapunov : arr -> arr -> arr
                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index d621ffd2b..6efc2fd19 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                          Module A.Mat

                          val diagm : ?k:int -> arr -> arr
                          val triu : ?k:int -> arr -> arr
                          val tril : ?k:int -> arr -> arr
                          val eye : int -> arr
                          +Mat (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                          Module A.Mat

                          val diagm : ?k:int -> arr -> arr
                          val triu : ?k:int -> arr -> arr
                          val tril : ?k:int -> arr -> arr
                          val eye : int -> arr
                          diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index a39eb0056..09b1abc4d 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                          Module A.Scalar

                          val add : elt -> elt -> elt
                          val sub : elt -> elt -> elt
                          val mul : elt -> elt -> elt
                          val div : elt -> elt -> elt
                          val pow : elt -> elt -> elt
                          val atan2 : elt -> elt -> elt
                          val abs : elt -> elt
                          val neg : elt -> elt
                          val sqr : elt -> elt
                          val sqrt : elt -> elt
                          val exp : elt -> elt
                          val log : elt -> elt
                          val log2 : elt -> elt
                          val log10 : elt -> elt
                          val signum : elt -> elt
                          val floor : elt -> elt
                          val ceil : elt -> elt
                          val round : elt -> elt
                          val sin : elt -> elt
                          val cos : elt -> elt
                          val tan : elt -> elt
                          val sinh : elt -> elt
                          val cosh : elt -> elt
                          val tanh : elt -> elt
                          val asin : elt -> elt
                          val acos : elt -> elt
                          val atan : elt -> elt
                          val asinh : elt -> elt
                          val acosh : elt -> elt
                          val atanh : elt -> elt
                          val relu : elt -> elt
                          val dawsn : elt -> elt
                          val sigmoid : elt -> elt
                          +Scalar (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                          Module A.Scalar

                          val add : elt -> elt -> elt
                          val sub : elt -> elt -> elt
                          val mul : elt -> elt -> elt
                          val div : elt -> elt -> elt
                          val pow : elt -> elt -> elt
                          val atan2 : elt -> elt -> elt
                          val abs : elt -> elt
                          val neg : elt -> elt
                          val sqr : elt -> elt
                          val sqrt : elt -> elt
                          val exp : elt -> elt
                          val log : elt -> elt
                          val log2 : elt -> elt
                          val log10 : elt -> elt
                          val signum : elt -> elt
                          val floor : elt -> elt
                          val ceil : elt -> elt
                          val round : elt -> elt
                          val sin : elt -> elt
                          val cos : elt -> elt
                          val tan : elt -> elt
                          val sinh : elt -> elt
                          val cosh : elt -> elt
                          val tanh : elt -> elt
                          val asin : elt -> elt
                          val acos : elt -> elt
                          val atan : elt -> elt
                          val asinh : elt -> elt
                          val acosh : elt -> elt
                          val atanh : elt -> elt
                          val relu : elt -> elt
                          val dawsn : elt -> elt
                          val sigmoid : elt -> elt
                          diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index 127c8abcf..3b3fd0eae 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                          Module Device.A

                          include Owl_types_ndarray_algodiff.Sig
                          include Owl_types_ndarray_eltcmp.Sig
                          include Owl_types_ndarray_basic.Sig
                          type arr
                          type elt
                          val empty : int array -> arr
                          val zeros : int array -> arr
                          val ones : int array -> arr
                          val create : int array -> elt -> arr
                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                          val bernoulli : ?p:elt -> int array -> arr
                          val init : int array -> (int -> elt) -> arr
                          val init_nd : int array -> (int array -> elt) -> arr
                          val shape : arr -> int array
                          val numel : arr -> int
                          val get : arr -> int array -> elt
                          val set : arr -> int array -> elt -> unit
                          val get_slice : int list list -> arr -> arr
                          val set_slice : int list list -> arr -> arr -> unit
                          val get_fancy : Owl_types_common.index list -> arr -> arr
                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                          val copy : arr -> arr
                          val copy_ : out:arr -> arr -> unit
                          val reset : arr -> unit
                          val reshape : arr -> int array -> arr
                          val reverse : arr -> arr
                          val tile : arr -> int array -> arr
                          val repeat : arr -> int array -> arr
                          val concatenate : ?axis:int -> arr array -> arr
                          val stack : ?axis:int -> arr array -> arr
                          val split : ?axis:int -> int array -> arr -> arr array
                          val expand : ?hi:bool -> arr -> int -> arr
                          val squeeze : ?axis:int array -> arr -> arr
                          val draw : ?axis:int -> arr -> int -> arr * int array
                          val map : (elt -> elt) -> arr -> arr
                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                          val one_hot : int -> arr -> arr
                          val pad : ?v:elt -> int list list -> arr -> arr
                          val print : +A (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                          Module Device.A

                          include Owl_types_ndarray_algodiff.Sig
                          include Owl_types_ndarray_eltcmp.Sig
                          include Owl_types_ndarray_basic.Sig
                          type arr
                          type elt
                          val empty : int array -> arr
                          val zeros : int array -> arr
                          val ones : int array -> arr
                          val create : int array -> elt -> arr
                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                          val bernoulli : ?p:elt -> int array -> arr
                          val init : int array -> (int -> elt) -> arr
                          val init_nd : int array -> (int array -> elt) -> arr
                          val shape : arr -> int array
                          val numel : arr -> int
                          val get : arr -> int array -> elt
                          val set : arr -> int array -> elt -> unit
                          val get_slice : int list list -> arr -> arr
                          val set_slice : int list list -> arr -> arr -> unit
                          val get_fancy : Owl_types_common.index list -> arr -> arr
                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                          val copy : arr -> arr
                          val copy_ : out:arr -> arr -> unit
                          val reset : arr -> unit
                          val reshape : arr -> int array -> arr
                          val reverse : arr -> arr
                          val tile : arr -> int array -> arr
                          val repeat : arr -> int array -> arr
                          val concatenate : ?axis:int -> arr array -> arr
                          val stack : ?axis:int -> arr array -> arr
                          val split : ?axis:int -> int array -> arr -> arr array
                          val expand : ?hi:bool -> arr -> int -> arr
                          val squeeze : ?axis:int array -> arr -> arr
                          val draw : ?axis:int -> arr -> int -> arr * int array
                          val map : (elt -> elt) -> arr -> arr
                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                          val one_hot : int -> arr -> arr
                          val pad : ?v:elt -> int list list -> arr -> arr
                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/index.html index cbd69ba5e..5b0e9f747 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device)

                          Module Type.Device

                          Type definition
                          type device

                          TODO

                          type value

                          TODO

                          Core functions
                          val make_device : unit -> device

                          TODO

                          val arr_to_value : A.arr -> value

                          TODO

                          val value_to_arr : value -> A.arr

                          TODO

                          val elt_to_value : A.elt -> value

                          TODO

                          val value_to_elt : value -> A.elt

                          TODO

                          val value_to_float : value -> float

                          TODO

                          val is_arr : value -> bool

                          TODO

                          val is_elt : value -> bool

                          TODO

                          +Device (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type.Device)

                          Module Type.Device

                          Type definition
                          type device

                          TODO

                          type value

                          TODO

                          Core functions
                          val make_device : unit -> device

                          TODO

                          val arr_to_value : A.arr -> value

                          TODO

                          val value_to_arr : value -> A.arr

                          TODO

                          val elt_to_value : A.elt -> value

                          TODO

                          val value_to_elt : value -> A.elt

                          TODO

                          val value_to_float : value -> float

                          TODO

                          val is_arr : value -> bool

                          TODO

                          val is_elt : value -> bool

                          TODO

                          diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/index.html index c27354bba..fcc2e0247 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type)

                          Module Shape.Type

                          Type definition
                          type state =
                          1. | Valid
                          2. | Invalid
                            (*

                            TODO

                            *)

                          TODO

                          and block = {
                          1. size : int;
                          2. block_id : int;
                          3. mutable active : t option;
                          4. mutable memory : Device.value;
                          5. mutable nodes : t list;
                          }

                          block type keeps a reference to a block of memory and to the nodes sharing that block.

                          and attr = {
                          1. mutable op : op;
                          2. mutable freeze : bool;
                          3. mutable reuse : bool;
                          4. mutable state : state;
                          5. mutable shape : int array option array;
                          6. mutable value : Device.value array;
                          7. mutable block : block array option;
                          }

                          TODO

                          and arr =
                          1. | Arr of t
                          and elt =
                          1. | Elt of t
                          and op =
                          1. | Noop
                          2. | Var
                          3. | Const
                          4. | Empty of int array
                          5. | Zeros of int array
                          6. | Ones of int array
                          7. | Create of int array
                          8. | Sequential of int array
                          9. | Uniform of int array
                          10. | Gaussian of int array
                          11. | Bernoulli of int array
                          12. | Init of int array * int -> elt
                          13. | Get of int array
                          14. | Set of int array
                          15. | GetSlice of int list list
                          16. | SetSlice of int list list
                          17. | GetFancy of Owl_types_common.index list
                          18. | SetFancy of Owl_types_common.index list
                          19. | Copy
                          20. | Reset
                          21. | Reshape of int array
                          22. | Reverse
                          23. | Tile of int array
                          24. | Repeat of int array
                          25. | Pad of elt * int list list
                          26. | Concatenate of int
                          27. | Stack of int
                          28. | Split of int * int array
                          29. | Draw of int * int
                          30. | Map of elt -> elt
                          31. | Fold of int * elt -> elt -> elt
                          32. | Scan of int * elt -> elt -> elt
                          33. | OneHot of int
                          34. | OfArray of int array
                          35. | Delay of Device.A.arr -> Device.A.arr
                          36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                          37. | LazyPrint of int option +Type (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape.Type)

                            Module Shape.Type

                            Type definition
                            type state =
                            1. | Valid
                            2. | Invalid
                              (*

                              TODO

                              *)

                            TODO

                            and block = {
                            1. size : int;
                            2. block_id : int;
                            3. mutable active : t option;
                            4. mutable memory : Device.value;
                            5. mutable nodes : t list;
                            }

                            block type keeps a reference to a block of memory and to the nodes sharing that block.

                            and attr = {
                            1. mutable op : op;
                            2. mutable freeze : bool;
                            3. mutable reuse : bool;
                            4. mutable state : state;
                            5. mutable shape : int array option array;
                            6. mutable value : Device.value array;
                            7. mutable block : block array option;
                            }

                            TODO

                            and arr =
                            1. | Arr of t
                            and elt =
                            1. | Elt of t
                            and op =
                            1. | Noop
                            2. | Var
                            3. | Const
                            4. | Empty of int array
                            5. | Zeros of int array
                            6. | Ones of int array
                            7. | Create of int array
                            8. | Sequential of int array
                            9. | Uniform of int array
                            10. | Gaussian of int array
                            11. | Bernoulli of int array
                            12. | Init of int array * int -> elt
                            13. | Get of int array
                            14. | Set of int array
                            15. | GetSlice of int list list
                            16. | SetSlice of int list list
                            17. | GetFancy of Owl_types_common.index list
                            18. | SetFancy of Owl_types_common.index list
                            19. | Copy
                            20. | Reset
                            21. | Reshape of int array
                            22. | Reverse
                            23. | Tile of int array
                            24. | Repeat of int array
                            25. | Pad of elt * int list list
                            26. | Concatenate of int
                            27. | Stack of int
                            28. | Split of int * int array
                            29. | Draw of int * int
                            30. | Map of elt -> elt
                            31. | Fold of int * elt -> elt -> elt
                            32. | Scan of int * elt -> elt -> elt
                            33. | OneHot of int
                            34. | OfArray of int array
                            35. | Delay of Device.A.arr -> Device.A.arr
                            36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                            37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                            38. | Abs
                            39. | Neg
                            40. | Floor
                            41. | Ceil
                            42. | Round
                            43. | Sqr
                            44. | Sqrt
                            45. | Log
                            46. | Log2
                            47. | Log10
                            48. | Exp
                            49. | Sin
                            50. | Cos
                            51. | Tan
                            52. | Sinh
                            53. | Cosh
                            54. | Tanh
                            55. | Asin
                            56. | Acos
                            57. | Atan
                            58. | Asinh
                            59. | Acosh
                            60. | Atanh
                            61. | Min of bool * int
                            62. | Max of bool * int
                            63. | Sum of bool * int
                            64. | SumReduce of int array
                            65. | Signum
                            66. | Sigmoid
                            67. | Relu
                            68. | Dawsn
                            69. | Min'
                            70. | Max'
                            71. | Sum'
                            72. | LogSumExp'
                            73. | LogSumExp of bool * int
                            74. | L1norm'
                            75. | L2norm'
                            76. | L2NormSqr'
                            77. | ClipByValue
                            78. | ClipByL2norm
                            79. | Pow
                            80. | ScalarPow
                            81. | PowScalar
                            82. | Atan2
                            83. | ScalarAtan2
                            84. | Atan2Scalar
                            85. | Hypot
                            86. | Min2
                            87. | Max2
                            88. | Add
                            89. | Sub
                            90. | Mul
                            91. | Div
                            92. | AddScalar
                            93. | SubScalar
                            94. | MulScalar
                            95. | DivScalar
                            96. | ScalarAdd
                            97. | ScalarSub
                            98. | ScalarMul
                            99. | ScalarDiv
                            100. | FMA
                            101. | EltEqual
                            102. | EltNotEqual
                            103. | EltLess
                            104. | EltGreater
                            105. | EltLessEqual
                            106. | EltGreaterEqual
                            107. | EltEqualScalar
                            108. | EltNotEqualScalar
                            109. | EltLessScalar
                            110. | EltGreaterScalar
                            111. | EltLessEqualScalar
                            112. | EltGreaterEqualScalar
                            113. | Conv1d of Owl_types_common.padding * int array
                            114. | Conv2d of Owl_types_common.padding * int array
                            115. | Conv3d of Owl_types_common.padding * int array
                            116. | TransposeConv1d of Owl_types_common.padding * int array
                            117. | TransposeConv2d of Owl_types_common.padding * int array
                            118. | TransposeConv3d of Owl_types_common.padding * int array
                            119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                            120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                            121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                            122. | MaxPool1d of Owl_types_common.padding * int array * int array
                            123. | MaxPool2d of Owl_types_common.padding * int array * int array
                            124. | MaxPool3d of Owl_types_common.padding * int array * int array
                            125. | AvgPool1d of Owl_types_common.padding * int array * int array
                            126. | AvgPool2d of Owl_types_common.padding * int array * int array
                            127. | AvgPool3d of Owl_types_common.padding * int array * int array
                            128. | UpSampling2d of int array
                            129. | Conv1dBackwardInput of int array
                            130. | Conv1dBackwardKernel of int array
                            131. | Conv2dBackwardInput of int array
                            132. | Conv2dBackwardKernel of int array
                            133. | Conv3dBackwardInput of int array
                            134. | Conv3dBackwardKernel of int array
                            135. | TransposeConv1dBackwardInput of int array
                            136. | TransposeConv1dBackwardKernel of int array
                            137. | TransposeConv2dBackwardInput of int array
                            138. | TransposeConv2dBackwardKernel of int array
                            139. | TransposeConv3dBackwardInput of int array
                            140. | TransposeConv3dBackwardKernel of int array
                            141. | DilatedConv1dBackwardInput of int array * int array
                            142. | DilatedConv1dBackwardKernel of int array * int array
                            143. | DilatedConv2dBackwardInput of int array * int array
                            144. | DilatedConv2dBackwardKernel of int array * int array
                            145. | DilatedConv3dBackwardInput of int array * int array
                            146. | DilatedConv3dBackwardKernel of int array * int array
                            147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                            148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                            149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                            150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                            151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                            152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                            153. | UpSampling2dBackward of int array
                            154. | RowNum
                            155. | ColNum
                            156. | Row
                            157. | Rows of int array
                            158. | CopyRowTo
                            159. | CopyColTo
                            160. | Dot of bool * bool * elt * elt
                            161. | Inv
                            162. | Trace
                            163. | Transpose of int array
                            164. | ToRows
                            165. | OfRows
                            166. | Scalar_Add
                            167. | Scalar_Sub
                            168. | Scalar_Mul
                            169. | Scalar_Div
                            170. | Scalar_Pow
                            171. | Scalar_Atan2
                            172. | Scalar_Abs
                            173. | Scalar_Neg
                            174. | Scalar_Sqr
                            175. | Scalar_Sqrt
                            176. | Scalar_Exp
                            177. | Scalar_Log
                            178. | Scalar_Log2
                            179. | Scalar_Log10
                            180. | Scalar_Signum
                            181. | Scalar_Floor
                            182. | Scalar_Ceil
                            183. | Scalar_Round
                            184. | Scalar_Sin
                            185. | Scalar_Cos
                            186. | Scalar_Tan
                            187. | Scalar_Sinh
                            188. | Scalar_Cosh
                            189. | Scalar_Tanh
                            190. | Scalar_Asin
                            191. | Scalar_Acos
                            192. | Scalar_Atan
                            193. | Scalar_Asinh
                            194. | Scalar_Acosh
                            195. | Scalar_Atanh
                            196. | Scalar_Relu
                            197. | Scalar_Dawsn
                            198. | Scalar_Sigmoid
                            199. | Fused_Adagrad of float * float
                              (*

                              TODO

                              *)
                            diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/index.html index bc5b504ef..c85877b02 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape)

                            Module Symbol.Shape

                            Core functions
                            val infer_shape : +Shape (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol.Shape)

                            Module Symbol.Shape

                            Core functions
                            val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                            TODO

                            diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/index.html index 160db7d90..2d0abcd38 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol)

                            Module Operator.Symbol

                            Core functions
                            val op_to_str : Shape.Type.op -> string

                            TODO

                            val is_random_variable : Shape.Type.op -> bool

                            TODO

                            val refnum : 'a Owl_graph.node -> int

                            TODO

                            val node_shape : Shape.Type.attr Owl_graph.node -> int array

                            TODO

                            val node_numel : Shape.Type.attr Owl_graph.node -> int

                            TODO

                            val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                            TODO

                            val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                            TODO

                            val shape_to_str : int array option array -> string

                            TODO

                            val node_to_str : Shape.Type.attr Owl_graph.node -> string

                            TODO

                            val node_to_arr : Shape.Type.t -> Shape.Type.arr

                            TODO

                            val arr_to_node : Shape.Type.arr -> Shape.Type.t

                            TODO

                            val node_to_elt : Shape.Type.t -> Shape.Type.elt

                            TODO

                            val elt_to_node : Shape.Type.elt -> Shape.Type.t

                            TODO

                            val make_node : +Symbol (owl-base.Owl_computation_graph.Make.Optimiser.Operator.Symbol)

                            Module Operator.Symbol

                            Core functions
                            val op_to_str : Shape.Type.op -> string

                            TODO

                            val is_random_variable : Shape.Type.op -> bool

                            TODO

                            val refnum : 'a Owl_graph.node -> int

                            TODO

                            val node_shape : Shape.Type.attr Owl_graph.node -> int array

                            TODO

                            val node_numel : Shape.Type.attr Owl_graph.node -> int

                            TODO

                            val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                            TODO

                            val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                            TODO

                            val shape_to_str : int array option array -> string

                            TODO

                            val node_to_str : Shape.Type.attr Owl_graph.node -> string

                            TODO

                            val node_to_arr : Shape.Type.t -> Shape.Type.arr

                            TODO

                            val arr_to_node : Shape.Type.arr -> Shape.Type.t

                            TODO

                            val node_to_elt : Shape.Type.t -> Shape.Type.elt

                            TODO

                            val elt_to_node : Shape.Type.elt -> Shape.Type.t

                            TODO

                            val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/index.html index cf0ac6cab..f07b4efb4 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_graph.Make.Optimiser.Operator)

                            Module Optimiser.Operator

                            Vectorised functions
                            val empty : int array -> Symbol.Shape.Type.arr

                            TODO

                            val zeros : int array -> Symbol.Shape.Type.arr

                            TODO

                            val ones : int array -> Symbol.Shape.Type.arr

                            TODO

                            val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                            TODO

                            val sequential : +Operator (owl-base.Owl_computation_graph.Make.Optimiser.Operator)

                            Module Optimiser.Operator

                            Vectorised functions

                            noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                            val empty : int array -> Symbol.Shape.Type.arr

                            empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                            val zeros : int array -> Symbol.Shape.Type.arr

                            zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                            val ones : int array -> Symbol.Shape.Type.arr

                            ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                            val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                            create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                            val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val uniform : + Symbol.Shape.Type.arr

                            sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                            val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val gaussian : + Symbol.Shape.Type.arr

                            uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                            val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                            TODO

                            val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                            TODO

                            val init_nd : + Symbol.Shape.Type.arr

                            gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                            val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                            bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                            val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                            init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                            val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                            TODO

                            val shape : Symbol.Shape.Type.arr -> int array

                            TODO

                            val numel : Symbol.Shape.Type.arr -> int

                            TODO

                            TODO

                            val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                            TODO

                            val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                            TODO

                            val set_slice : + Symbol.Shape.Type.arr

                            init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                            val shape : Symbol.Shape.Type.arr -> int array

                            shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                            val numel : Symbol.Shape.Type.arr -> int

                            numel arr returns the total number of elements in the array arr.

                            get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                            val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                            set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                            val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                            get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                            val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                            TODO

                            val get_fancy : + unit

                            set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                            val set_fancy : + Symbol.Shape.Type.arr

                            get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                            val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                            TODO

                            val copy_ : out:'a -> 'b -> 'c

                            TODO

                            val reset : Symbol.Shape.Type.arr -> unit

                            TODO

                            val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            TODO

                            val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            TODO

                            val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            TODO

                            val pad : + unit

                            set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                            copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                            val copy_ : out:'a -> 'b -> 'c

                            copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                            val reset : Symbol.Shape.Type.arr -> unit

                            reset arr sets all elements of the array arr to zero.

                            val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                            reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                            val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                            val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                            TODO

                            val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                            TODO

                            val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                            TODO

                            val concatenate : + Symbol.Shape.Type.arr

                            pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                            val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                            expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                            val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                            squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                            val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                            TODO

                            val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                            TODO

                            val concat : + Symbol.Shape.Type.arr

                            concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                            val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                            stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                            val split : ?axis:int -> 'a -> 'b -> 'c

                            TODO

                            concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                            val split : ?axis:int -> 'a -> 'b -> 'c

                            split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                            • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                            val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                            TODO

                            val map : + Symbol.Shape.Type.arr * 'a array

                            draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                            map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                            fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                            TODO

                            val delay : + Symbol.Shape.Type.arr

                            scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                            one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                            delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                            val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                            val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                            TODO

                            lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                            val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                            print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                            • max_row is an optional parameter specifying the maximum number of rows to print.
                            • max_col is an optional parameter specifying the maximum number of columns to print.
                            • header is an optional parameter to include a header in the output.
                            • fmt is an optional parameter to specify the format of the output.

                            abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                            neg arr negates each element in the array arr. Returns a new array with each element negated.

                            floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                            ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                            round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                            sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                            sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                            log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                            log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                            log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                            exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                            sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                            cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                            tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                            sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                            cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                            tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                            asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                            acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                            atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                            asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                            acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                            atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                            val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                            • axis specifies the axis along which to compute the minimum.
                            • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                            val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                            • axis specifies the axis along which to compute the maximum.
                            • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                            val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val sum_reduce : + Symbol.Shape.Type.arr

                            sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                            • axis specifies the axis along which to compute the sum.
                            • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                            val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val log_sum_exp : + Symbol.Shape.Type.arr

                            sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                            • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                            signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                            sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                            relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                            dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                            min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                            max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                            sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                            log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val clip_by_value : + Symbol.Shape.Type.arr

                            log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                            • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                            • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                            l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                            l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                            l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                            val clip_by_l2norm : + Symbol.Shape.Type.arr

                            clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                            • amin specifies the minimum value to clip to.
                            • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                            clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                            val scalar_pow : + Symbol.Shape.Type.arr

                            pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                            val pow_scalar : + Symbol.Shape.Type.arr

                            scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                            val atan2 : + Symbol.Shape.Type.arr

                            pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                            val scalar_atan2 : + Symbol.Shape.Type.arr

                            atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                            val atan2_scalar : + Symbol.Shape.Type.arr

                            scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                            val hypot : + Symbol.Shape.Type.arr

                            atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                            hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                            min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                            max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                            add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                            sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                            mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                            val add_scalar : + Symbol.Shape.Type.arr

                            div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                            val sub_scalar : + Symbol.Shape.Type.arr

                            add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                            val mul_scalar : + Symbol.Shape.Type.arr

                            sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                            val div_scalar : + Symbol.Shape.Type.arr

                            mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                            val scalar_add : + Symbol.Shape.Type.arr

                            div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                            val scalar_sub : + Symbol.Shape.Type.arr

                            scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                            val scalar_mul : + Symbol.Shape.Type.arr

                            scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                            val scalar_div : + Symbol.Shape.Type.arr

                            scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                            scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                            val elt_equal : + Symbol.Shape.Type.arr

                            fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                            val elt_not_equal : + Symbol.Shape.Type.arr

                            elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                            val elt_less : + Symbol.Shape.Type.arr

                            elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                            val elt_greater : + Symbol.Shape.Type.arr

                            elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                            val elt_less_equal : + Symbol.Shape.Type.arr

                            elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                            val elt_greater_equal : + Symbol.Shape.Type.arr

                            elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                            val elt_equal_scalar : + Symbol.Shape.Type.arr

                            elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                            val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                            elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                            val elt_less_scalar : + Symbol.Shape.Type.arr

                            elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                            val elt_greater_scalar : + Symbol.Shape.Type.arr

                            elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                            val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                            elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                            TODO

                            val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                            elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                            TODO

                            val conv1d : + Symbol.Shape.Type.arr

                            elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                            val conv2d : + Symbol.Shape.Type.arr

                            conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                            • padding specifies the padding strategy (default is "valid").
                            • strides specifies the stride length. Returns a new array with the result of the convolution.
                            val conv3d : + Symbol.Shape.Type.arr

                            conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                            • padding specifies the padding strategy (default is "valid").
                            • strides specifies the stride length. Returns a new array with the result of the convolution.
                            val transpose_conv1d : + Symbol.Shape.Type.arr

                            conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                            • padding specifies the padding strategy (default is "valid").
                            • strides specifies the stride length. Returns a new array with the result of the convolution.
                            val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val transpose_conv2d : + Symbol.Shape.Type.arr

                            transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                            • padding specifies the padding strategy (default is "valid").
                            • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                            val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val transpose_conv3d : + Symbol.Shape.Type.arr

                            transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                            • padding specifies the padding strategy (default is "valid").
                            • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                            val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val dilated_conv1d : + Symbol.Shape.Type.arr

                            transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                            • padding specifies the padding strategy (default is "valid").
                            • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                            val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val dilated_conv2d : + Symbol.Shape.Type.arr

                            dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                            • padding specifies the padding strategy (default is "valid").
                            • strides specifies the stride length.
                            • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                            val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val dilated_conv3d : + Symbol.Shape.Type.arr

                            dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                            • padding specifies the padding strategy (default is "valid").
                            • strides specifies the stride length.
                            • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                            val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val max_pool1d : + Symbol.Shape.Type.arr

                            dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                            • padding specifies the padding strategy (default is "valid").
                            • strides specifies the stride length.
                            • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                            val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val max_pool2d : + Symbol.Shape.Type.arr

                            max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                            • padding specifies the padding strategy (default is "valid").
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length. Returns a new array with the result of the max pooling.
                            val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val max_pool3d : + Symbol.Shape.Type.arr

                            max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                            • padding specifies the padding strategy (default is "valid").
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length. Returns a new array with the result of the max pooling.
                            val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val avg_pool1d : + Symbol.Shape.Type.arr

                            max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                            • padding specifies the padding strategy (default is "valid").
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length. Returns a new array with the result of the max pooling.
                            val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val avg_pool2d : + Symbol.Shape.Type.arr

                            avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                            • padding specifies the padding strategy (default is "valid").
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length. Returns a new array with the result of the average pooling.
                            val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val avg_pool3d : + Symbol.Shape.Type.arr

                            avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                            • padding specifies the padding strategy (default is "valid").
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length. Returns a new array with the result of the average pooling.
                            val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            TODO

                            val conv1d_backward_input : + Symbol.Shape.Type.arr

                            avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                            • padding specifies the padding strategy (default is "valid").
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length. Returns a new array with the result of the average pooling.
                            val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            upsampling2d input size performs a 2-dimensional upsampling on the input array.

                            • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                            TODO

                            val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                            conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                            • input is the original input array.
                            • kernel is the convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                            val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val conv2d_backward_input : + Symbol.Shape.Type.arr

                            conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                            • input is the original input array.
                            • kernel is the convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                            TODO

                            val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                            conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                            • input is the original input array.
                            • kernel is the convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                            val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val conv3d_backward_input : + Symbol.Shape.Type.arr

                            conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                            • input is the original input array.
                            • kernel is the convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                            TODO

                            val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                            conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                            • input is the original input array.
                            • kernel is the convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                            val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                            conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                            • input is the original input array.
                            • kernel is the convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                            val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                            transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                            • input is the original input array.
                            • kernel is the transposed convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                            val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                            transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                            • input is the original input array.
                            • kernel is the transposed convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                            val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                            transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                            • input is the original input array.
                            • kernel is the transposed convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                            val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                            transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                            • input is the original input array.
                            • kernel is the transposed convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                            val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                            transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                            • input is the original input array.
                            • kernel is the transposed convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                            val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                            transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                            • input is the original input array.
                            • kernel is the transposed convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                            val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                            dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                            • input is the original input array.
                            • kernel is the dilated convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • dilations specifies the dilation rate.
                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                            val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                            dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                            • input is the original input array.
                            • kernel is the dilated convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • dilations specifies the dilation rate.
                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                            val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                            dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                            • input is the original input array.
                            • kernel is the dilated convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • dilations specifies the dilation rate.
                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                            val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                            dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                            • input is the original input array.
                            • kernel is the dilated convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • dilations specifies the dilation rate.
                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                            val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                            dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                            • input is the original input array.
                            • kernel is the dilated convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • dilations specifies the dilation rate.
                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                            val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val max_pool1d_backward : + Symbol.Shape.Type.arr

                            dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                            • input is the original input array.
                            • kernel is the dilated convolutional kernel used during the forward pass.
                            • strides specifies the stride length.
                            • dilations specifies the dilation rate.
                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                            val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val max_pool2d_backward : + Symbol.Shape.Type.arr

                            max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                            • padding specifies the padding strategy used during the forward pass.
                            • input is the original input array.
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                            val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val max_pool3d_backward : + Symbol.Shape.Type.arr

                            max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                            • padding specifies the padding strategy used during the forward pass.
                            • input is the original input array.
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                            val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val avg_pool1d_backward : + Symbol.Shape.Type.arr

                            max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                            • padding specifies the padding strategy used during the forward pass.
                            • input is the original input array.
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                            val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val avg_pool2d_backward : + Symbol.Shape.Type.arr

                            avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                            • padding specifies the padding strategy used during the forward pass.
                            • input is the original input array.
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                            val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val avg_pool3d_backward : + Symbol.Shape.Type.arr

                            avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                            • padding specifies the padding strategy used during the forward pass.
                            • input is the original input array.
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                            val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val upsampling2d_backward : + Symbol.Shape.Type.arr

                            avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                            • padding specifies the padding strategy used during the forward pass.
                            • input is the original input array.
                            • pool_size specifies the size of the pooling window.
                            • strides specifies the stride length.
                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                            val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val row_num : Symbol.Shape.Type.arr -> int

                            TODO

                            val col_num : Symbol.Shape.Type.arr -> int

                            TODO

                            val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            TODO

                            val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                            TODO

                            val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                            TODO

                            TODO

                            upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                            • input is the original input array.
                            • size specifies the upsampling factors for each dimension.
                            • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                            val row_num : Symbol.Shape.Type.arr -> int

                            row_num arr returns the number of rows in the array arr.

                            val col_num : Symbol.Shape.Type.arr -> int

                            col_num arr returns the number of columns in the array arr.

                            row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                            val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                            rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                            val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                            copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                            val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                            copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                            diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                            trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                            val transpose : + Symbol.Shape.Type.arr

                            dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                            val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                            TODO

                            val to_rows : Symbol.Shape.Type.arr -> 'a array

                            TODO

                            TODO

                            val to_cols : Symbol.Shape.Type.arr -> 'a array

                            TODO

                            TODO

                            val of_array : + Symbol.Shape.Type.arr

                            transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                            val to_rows : Symbol.Shape.Type.arr -> 'a array

                            to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                            of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                            val to_cols : Symbol.Shape.Type.arr -> 'a array

                            to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                            of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                            val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                            TODO

                            val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                            TODO

                            val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                            TODO

                            Scalar functions
                            module Scalar : sig ... end
                            module Mat : sig ... end
                            module Linalg : sig ... end
                            + Symbol.Shape.Type.arr

                            of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                            val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                            of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                            val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                            to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                            Scalar functions
                            module Scalar : sig ... end
                            module Mat : sig ... end
                            module Linalg : sig ... end
                            diff --git a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/index.html b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/index.html index 7fed53230..d109f99a6 100644 --- a/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/argument-1-Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_graph.Make.Optimiser)

                            Parameter Make.Optimiser

                            Core functions
                            val estimate_complexity : 'a Owl_graph.node array -> int * int

                            TODO

                            val optimise_nodes : +Optimiser (owl-base.Owl_computation_graph.Make.Optimiser)

                            Parameter Make.Optimiser

                            Core functions
                            val estimate_complexity : 'a Owl_graph.node array -> int * int

                            TODO

                            val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

                            TODO

                            diff --git a/docs/owl-base/Owl_computation_graph/Make/index.html b/docs/owl-base/Owl_computation_graph/Make/index.html index 197c86f6f..161daf20b 100644 --- a/docs/owl-base/Owl_computation_graph/Make/index.html +++ b/docs/owl-base/Owl_computation_graph/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_computation_graph.Make)

                            Module Owl_computation_graph.Make

                            Parameters

                            Signature

                            module Optimiser = Optimiser
                            type graph = {
                            1. mutable name : string;
                            2. mutable input : Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array;
                            3. mutable output : Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array;
                            4. mutable iopair : (Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node +Make (owl-base.Owl_computation_graph.Make)

                              Module Owl_computation_graph.Make

                              Parameters

                              Signature

                              module Optimiser = Optimiser
                              type graph = {
                              1. mutable name : string;
                              2. mutable input : Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array;
                              3. mutable output : Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array;
                              4. mutable iopair : (Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node * Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node) array;
                              5. mutable iosafe : bool array;
                              6. mutable random : Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array;
                              7. mutable htbl : (string, Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node) diff --git a/docs/owl-base/Owl_computation_graph/index.html b/docs/owl-base/Owl_computation_graph/index.html index 703f8b3ad..e8e48fae7 100644 --- a/docs/owl-base/Owl_computation_graph/index.html +++ b/docs/owl-base/Owl_computation_graph/index.html @@ -1,2 +1,2 @@ -Owl_computation_graph (owl-base.Owl_computation_graph)

                                Module Owl_computation_graph

                                +Owl_computation_graph (owl-base.Owl_computation_graph)

                                Module Owl_computation_graph

                                diff --git a/docs/owl-base/Owl_computation_graph_sig/index.html b/docs/owl-base/Owl_computation_graph_sig/index.html index 2e8e786af..90332e759 100644 --- a/docs/owl-base/Owl_computation_graph_sig/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/index.html @@ -1,2 +1,2 @@ -Owl_computation_graph_sig (owl-base.Owl_computation_graph_sig)

                                Module Owl_computation_graph_sig

                                module type Sig = sig ... end
                                +Owl_computation_graph_sig (owl-base.Owl_computation_graph_sig)

                                Module Owl_computation_graph_sig

                                module type Sig = sig ... end
                                diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Linalg/index.html index f6be8a62d..e88066d06 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Linalg)

                                Module Operator.Linalg

                                val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                TODO

                                val svd : +Linalg (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Linalg)

                                Module Operator.Linalg

                                inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

                                logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

                                val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

                                • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

                                qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

                                lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

                                svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

                                • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
                                val lyapunov : + Symbol.Shape.Type.arr

                                sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

                                val discrete_lyapunov : + Symbol.Shape.Type.arr

                                lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

                                val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                TODO

                                val linsolve : + Symbol.Shape.Type.arr

                                discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

                                • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
                                val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                TODO

                                linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

                                • trans specifies whether to transpose the matrix A.
                                • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

                                care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

                                • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                + Symbol.Shape.Type.arr

                                dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

                                • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Mat/index.html index 816f2d78b..889736fc7 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Mat)

                                Module Operator.Mat

                                val eye : int -> Symbol.Shape.Type.arr

                                TODO

                                TODO

                                TODO

                                TODO

                                +Mat (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Mat)

                                Module Operator.Mat

                                val eye : int -> Symbol.Shape.Type.arr

                                eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

                                diagm ?k v creates a diagonal matrix from the array v.

                                • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

                                triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

                                tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

                                diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Scalar/index.html index 726280ae5..63cdb315b 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Scalar)

                                Module Operator.Scalar

                                val add : +Scalar (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Scalar)

                                Module Operator.Scalar

                                add a b returns the sum of the scalars a and b.

                                sub a b returns the difference of the scalars a and b.

                                mul a b returns the product of the scalars a and b.

                                div a b returns the quotient of the scalars a and b.

                                val atan2 : + Symbol.Shape.Type.elt

                                pow a b returns the scalar a raised to the power of b.

                                + Symbol.Shape.Type.elt

                                atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                                abs a returns the absolute value of the scalar a.

                                neg a returns the negation of the scalar a.

                                sqr a returns the square of the scalar a.

                                sqrt a returns the square root of the scalar a.

                                exp a returns the exponential of the scalar a.

                                log a returns the natural logarithm of the scalar a.

                                log2 a returns the base-2 logarithm of the scalar a.

                                log10 a returns the base-10 logarithm of the scalar a.

                                signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                                floor a returns the greatest integer less than or equal to the scalar a.

                                ceil a returns the smallest integer greater than or equal to the scalar a.

                                round a returns the nearest integer to the scalar a.

                                sin a returns the sine of the scalar a.

                                cos a returns the cosine of the scalar a.

                                tan a returns the tangent of the scalar a.

                                sinh a returns the hyperbolic sine of the scalar a.

                                cosh a returns the hyperbolic cosine of the scalar a.

                                tanh a returns the hyperbolic tangent of the scalar a.

                                asin a returns the arcsine of the scalar a.

                                acos a returns the arccosine of the scalar a.

                                atan a returns the arctangent of the scalar a.

                                asinh a returns the inverse hyperbolic sine of the scalar a.

                                acosh a returns the inverse hyperbolic cosine of the scalar a.

                                atanh a returns the inverse hyperbolic tangent of the scalar a.

                                relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                                dawsn a returns Dawson's function of the scalar a.

                                sigmoid a returns the sigmoid function of the scalar a.

                                diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index ac63a7709..6b61f5b0c 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                Module A.Linalg

                                val inv : arr -> arr
                                val logdet : arr -> elt
                                val chol : ?upper:bool -> arr -> arr
                                val svd : ?thin:bool -> arr -> arr * arr * arr
                                val qr : arr -> arr * arr
                                val lq : arr -> arr * arr
                                val sylvester : arr -> arr -> arr -> arr
                                val lyapunov : arr -> arr -> arr
                                val discrete_lyapunov : +Linalg (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                Module A.Linalg

                                val inv : arr -> arr
                                val logdet : arr -> elt
                                val chol : ?upper:bool -> arr -> arr
                                val svd : ?thin:bool -> arr -> arr * arr * arr
                                val qr : arr -> arr * arr
                                val lq : arr -> arr * arr
                                val sylvester : arr -> arr -> arr -> arr
                                val lyapunov : arr -> arr -> arr
                                val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index ed26facae..98a579ad0 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                                Module A.Mat

                                val diagm : ?k:int -> arr -> arr
                                val triu : ?k:int -> arr -> arr
                                val tril : ?k:int -> arr -> arr
                                val eye : int -> arr
                                +Mat (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                                Module A.Mat

                                val diagm : ?k:int -> arr -> arr
                                val triu : ?k:int -> arr -> arr
                                val tril : ?k:int -> arr -> arr
                                val eye : int -> arr
                                diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index 0f29c03bd..70e008f65 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                Module A.Scalar

                                val add : elt -> elt -> elt
                                val sub : elt -> elt -> elt
                                val mul : elt -> elt -> elt
                                val div : elt -> elt -> elt
                                val pow : elt -> elt -> elt
                                val atan2 : elt -> elt -> elt
                                val abs : elt -> elt
                                val neg : elt -> elt
                                val sqr : elt -> elt
                                val sqrt : elt -> elt
                                val exp : elt -> elt
                                val log : elt -> elt
                                val log2 : elt -> elt
                                val log10 : elt -> elt
                                val signum : elt -> elt
                                val floor : elt -> elt
                                val ceil : elt -> elt
                                val round : elt -> elt
                                val sin : elt -> elt
                                val cos : elt -> elt
                                val tan : elt -> elt
                                val sinh : elt -> elt
                                val cosh : elt -> elt
                                val tanh : elt -> elt
                                val asin : elt -> elt
                                val acos : elt -> elt
                                val atan : elt -> elt
                                val asinh : elt -> elt
                                val acosh : elt -> elt
                                val atanh : elt -> elt
                                val relu : elt -> elt
                                val dawsn : elt -> elt
                                val sigmoid : elt -> elt
                                +Scalar (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                Module A.Scalar

                                val add : elt -> elt -> elt
                                val sub : elt -> elt -> elt
                                val mul : elt -> elt -> elt
                                val div : elt -> elt -> elt
                                val pow : elt -> elt -> elt
                                val atan2 : elt -> elt -> elt
                                val abs : elt -> elt
                                val neg : elt -> elt
                                val sqr : elt -> elt
                                val sqrt : elt -> elt
                                val exp : elt -> elt
                                val log : elt -> elt
                                val log2 : elt -> elt
                                val log10 : elt -> elt
                                val signum : elt -> elt
                                val floor : elt -> elt
                                val ceil : elt -> elt
                                val round : elt -> elt
                                val sin : elt -> elt
                                val cos : elt -> elt
                                val tan : elt -> elt
                                val sinh : elt -> elt
                                val cosh : elt -> elt
                                val tanh : elt -> elt
                                val asin : elt -> elt
                                val acos : elt -> elt
                                val atan : elt -> elt
                                val asinh : elt -> elt
                                val acosh : elt -> elt
                                val atanh : elt -> elt
                                val relu : elt -> elt
                                val dawsn : elt -> elt
                                val sigmoid : elt -> elt
                                diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index 5afb3aa3e..64d7e9621 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                                Module Device.A

                                include Owl_types_ndarray_algodiff.Sig
                                include Owl_types_ndarray_eltcmp.Sig
                                include Owl_types_ndarray_basic.Sig
                                type arr
                                type elt
                                val empty : int array -> arr
                                val zeros : int array -> arr
                                val ones : int array -> arr
                                val create : int array -> elt -> arr
                                val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                val bernoulli : ?p:elt -> int array -> arr
                                val init : int array -> (int -> elt) -> arr
                                val init_nd : int array -> (int array -> elt) -> arr
                                val shape : arr -> int array
                                val numel : arr -> int
                                val get : arr -> int array -> elt
                                val set : arr -> int array -> elt -> unit
                                val get_slice : int list list -> arr -> arr
                                val set_slice : int list list -> arr -> arr -> unit
                                val get_fancy : Owl_types_common.index list -> arr -> arr
                                val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                val copy : arr -> arr
                                val copy_ : out:arr -> arr -> unit
                                val reset : arr -> unit
                                val reshape : arr -> int array -> arr
                                val reverse : arr -> arr
                                val tile : arr -> int array -> arr
                                val repeat : arr -> int array -> arr
                                val concatenate : ?axis:int -> arr array -> arr
                                val stack : ?axis:int -> arr array -> arr
                                val split : ?axis:int -> int array -> arr -> arr array
                                val expand : ?hi:bool -> arr -> int -> arr
                                val squeeze : ?axis:int array -> arr -> arr
                                val draw : ?axis:int -> arr -> int -> arr * int array
                                val map : (elt -> elt) -> arr -> arr
                                val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                val one_hot : int -> arr -> arr
                                val pad : ?v:elt -> int list list -> arr -> arr
                                val print : +A (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                                Module Device.A

                                include Owl_types_ndarray_algodiff.Sig
                                include Owl_types_ndarray_eltcmp.Sig
                                include Owl_types_ndarray_basic.Sig
                                type arr
                                type elt
                                val empty : int array -> arr
                                val zeros : int array -> arr
                                val ones : int array -> arr
                                val create : int array -> elt -> arr
                                val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                val bernoulli : ?p:elt -> int array -> arr
                                val init : int array -> (int -> elt) -> arr
                                val init_nd : int array -> (int array -> elt) -> arr
                                val shape : arr -> int array
                                val numel : arr -> int
                                val get : arr -> int array -> elt
                                val set : arr -> int array -> elt -> unit
                                val get_slice : int list list -> arr -> arr
                                val set_slice : int list list -> arr -> arr -> unit
                                val get_fancy : Owl_types_common.index list -> arr -> arr
                                val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                val copy : arr -> arr
                                val copy_ : out:arr -> arr -> unit
                                val reset : arr -> unit
                                val reshape : arr -> int array -> arr
                                val reverse : arr -> arr
                                val tile : arr -> int array -> arr
                                val repeat : arr -> int array -> arr
                                val concatenate : ?axis:int -> arr array -> arr
                                val stack : ?axis:int -> arr array -> arr
                                val split : ?axis:int -> int array -> arr -> arr array
                                val expand : ?hi:bool -> arr -> int -> arr
                                val squeeze : ?axis:int array -> arr -> arr
                                val draw : ?axis:int -> arr -> int -> arr * int array
                                val map : (elt -> elt) -> arr -> arr
                                val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                val one_hot : int -> arr -> arr
                                val pad : ?v:elt -> int list list -> arr -> arr
                                val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index 5d8204c68..7cd32ae30 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device)

                                Module Type.Device

                                Type definition
                                type device

                                TODO

                                type value

                                TODO

                                Core functions
                                val make_device : unit -> device

                                TODO

                                val arr_to_value : A.arr -> value

                                TODO

                                val value_to_arr : value -> A.arr

                                TODO

                                val elt_to_value : A.elt -> value

                                TODO

                                val value_to_elt : value -> A.elt

                                TODO

                                val value_to_float : value -> float

                                TODO

                                val is_arr : value -> bool

                                TODO

                                val is_elt : value -> bool

                                TODO

                                +Device (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type.Device)

                                Module Type.Device

                                Type definition
                                type device

                                TODO

                                type value

                                TODO

                                Core functions
                                val make_device : unit -> device

                                TODO

                                val arr_to_value : A.arr -> value

                                TODO

                                val value_to_arr : value -> A.arr

                                TODO

                                val elt_to_value : A.elt -> value

                                TODO

                                val value_to_elt : value -> A.elt

                                TODO

                                val value_to_float : value -> float

                                TODO

                                val is_arr : value -> bool

                                TODO

                                val is_elt : value -> bool

                                TODO

                                diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/index.html index 9deaeb2ed..0ce883fed 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type)

                                Module Shape.Type

                                Type definition
                                type state =
                                1. | Valid
                                2. | Invalid
                                  (*

                                  TODO

                                  *)

                                TODO

                                and block = {
                                1. size : int;
                                2. block_id : int;
                                3. mutable active : t option;
                                4. mutable memory : Device.value;
                                5. mutable nodes : t list;
                                }

                                block type keeps a reference to a block of memory and to the nodes sharing that block.

                                and attr = {
                                1. mutable op : op;
                                2. mutable freeze : bool;
                                3. mutable reuse : bool;
                                4. mutable state : state;
                                5. mutable shape : int array option array;
                                6. mutable value : Device.value array;
                                7. mutable block : block array option;
                                }

                                TODO

                                and arr =
                                1. | Arr of t
                                and elt =
                                1. | Elt of t
                                and op =
                                1. | Noop
                                2. | Var
                                3. | Const
                                4. | Empty of int array
                                5. | Zeros of int array
                                6. | Ones of int array
                                7. | Create of int array
                                8. | Sequential of int array
                                9. | Uniform of int array
                                10. | Gaussian of int array
                                11. | Bernoulli of int array
                                12. | Init of int array * int -> elt
                                13. | Get of int array
                                14. | Set of int array
                                15. | GetSlice of int list list
                                16. | SetSlice of int list list
                                17. | GetFancy of Owl_types_common.index list
                                18. | SetFancy of Owl_types_common.index list
                                19. | Copy
                                20. | Reset
                                21. | Reshape of int array
                                22. | Reverse
                                23. | Tile of int array
                                24. | Repeat of int array
                                25. | Pad of elt * int list list
                                26. | Concatenate of int
                                27. | Stack of int
                                28. | Split of int * int array
                                29. | Draw of int * int
                                30. | Map of elt -> elt
                                31. | Fold of int * elt -> elt -> elt
                                32. | Scan of int * elt -> elt -> elt
                                33. | OneHot of int
                                34. | OfArray of int array
                                35. | Delay of Device.A.arr -> Device.A.arr
                                36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                37. | LazyPrint of int option +Type (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape.Type)

                                  Module Shape.Type

                                  Type definition
                                  type state =
                                  1. | Valid
                                  2. | Invalid
                                    (*

                                    TODO

                                    *)

                                  TODO

                                  and block = {
                                  1. size : int;
                                  2. block_id : int;
                                  3. mutable active : t option;
                                  4. mutable memory : Device.value;
                                  5. mutable nodes : t list;
                                  }

                                  block type keeps a reference to a block of memory and to the nodes sharing that block.

                                  and attr = {
                                  1. mutable op : op;
                                  2. mutable freeze : bool;
                                  3. mutable reuse : bool;
                                  4. mutable state : state;
                                  5. mutable shape : int array option array;
                                  6. mutable value : Device.value array;
                                  7. mutable block : block array option;
                                  }

                                  TODO

                                  and arr =
                                  1. | Arr of t
                                  and elt =
                                  1. | Elt of t
                                  and op =
                                  1. | Noop
                                  2. | Var
                                  3. | Const
                                  4. | Empty of int array
                                  5. | Zeros of int array
                                  6. | Ones of int array
                                  7. | Create of int array
                                  8. | Sequential of int array
                                  9. | Uniform of int array
                                  10. | Gaussian of int array
                                  11. | Bernoulli of int array
                                  12. | Init of int array * int -> elt
                                  13. | Get of int array
                                  14. | Set of int array
                                  15. | GetSlice of int list list
                                  16. | SetSlice of int list list
                                  17. | GetFancy of Owl_types_common.index list
                                  18. | SetFancy of Owl_types_common.index list
                                  19. | Copy
                                  20. | Reset
                                  21. | Reshape of int array
                                  22. | Reverse
                                  23. | Tile of int array
                                  24. | Repeat of int array
                                  25. | Pad of elt * int list list
                                  26. | Concatenate of int
                                  27. | Stack of int
                                  28. | Split of int * int array
                                  29. | Draw of int * int
                                  30. | Map of elt -> elt
                                  31. | Fold of int * elt -> elt -> elt
                                  32. | Scan of int * elt -> elt -> elt
                                  33. | OneHot of int
                                  34. | OfArray of int array
                                  35. | Delay of Device.A.arr -> Device.A.arr
                                  36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                  37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                  38. | Abs
                                  39. | Neg
                                  40. | Floor
                                  41. | Ceil
                                  42. | Round
                                  43. | Sqr
                                  44. | Sqrt
                                  45. | Log
                                  46. | Log2
                                  47. | Log10
                                  48. | Exp
                                  49. | Sin
                                  50. | Cos
                                  51. | Tan
                                  52. | Sinh
                                  53. | Cosh
                                  54. | Tanh
                                  55. | Asin
                                  56. | Acos
                                  57. | Atan
                                  58. | Asinh
                                  59. | Acosh
                                  60. | Atanh
                                  61. | Min of bool * int
                                  62. | Max of bool * int
                                  63. | Sum of bool * int
                                  64. | SumReduce of int array
                                  65. | Signum
                                  66. | Sigmoid
                                  67. | Relu
                                  68. | Dawsn
                                  69. | Min'
                                  70. | Max'
                                  71. | Sum'
                                  72. | LogSumExp'
                                  73. | LogSumExp of bool * int
                                  74. | L1norm'
                                  75. | L2norm'
                                  76. | L2NormSqr'
                                  77. | ClipByValue
                                  78. | ClipByL2norm
                                  79. | Pow
                                  80. | ScalarPow
                                  81. | PowScalar
                                  82. | Atan2
                                  83. | ScalarAtan2
                                  84. | Atan2Scalar
                                  85. | Hypot
                                  86. | Min2
                                  87. | Max2
                                  88. | Add
                                  89. | Sub
                                  90. | Mul
                                  91. | Div
                                  92. | AddScalar
                                  93. | SubScalar
                                  94. | MulScalar
                                  95. | DivScalar
                                  96. | ScalarAdd
                                  97. | ScalarSub
                                  98. | ScalarMul
                                  99. | ScalarDiv
                                  100. | FMA
                                  101. | EltEqual
                                  102. | EltNotEqual
                                  103. | EltLess
                                  104. | EltGreater
                                  105. | EltLessEqual
                                  106. | EltGreaterEqual
                                  107. | EltEqualScalar
                                  108. | EltNotEqualScalar
                                  109. | EltLessScalar
                                  110. | EltGreaterScalar
                                  111. | EltLessEqualScalar
                                  112. | EltGreaterEqualScalar
                                  113. | Conv1d of Owl_types_common.padding * int array
                                  114. | Conv2d of Owl_types_common.padding * int array
                                  115. | Conv3d of Owl_types_common.padding * int array
                                  116. | TransposeConv1d of Owl_types_common.padding * int array
                                  117. | TransposeConv2d of Owl_types_common.padding * int array
                                  118. | TransposeConv3d of Owl_types_common.padding * int array
                                  119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                  120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                  121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                  122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                  123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                  124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                  125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                  126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                  127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                  128. | UpSampling2d of int array
                                  129. | Conv1dBackwardInput of int array
                                  130. | Conv1dBackwardKernel of int array
                                  131. | Conv2dBackwardInput of int array
                                  132. | Conv2dBackwardKernel of int array
                                  133. | Conv3dBackwardInput of int array
                                  134. | Conv3dBackwardKernel of int array
                                  135. | TransposeConv1dBackwardInput of int array
                                  136. | TransposeConv1dBackwardKernel of int array
                                  137. | TransposeConv2dBackwardInput of int array
                                  138. | TransposeConv2dBackwardKernel of int array
                                  139. | TransposeConv3dBackwardInput of int array
                                  140. | TransposeConv3dBackwardKernel of int array
                                  141. | DilatedConv1dBackwardInput of int array * int array
                                  142. | DilatedConv1dBackwardKernel of int array * int array
                                  143. | DilatedConv2dBackwardInput of int array * int array
                                  144. | DilatedConv2dBackwardKernel of int array * int array
                                  145. | DilatedConv3dBackwardInput of int array * int array
                                  146. | DilatedConv3dBackwardKernel of int array * int array
                                  147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                  148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                  149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                  150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                  151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                  152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                  153. | UpSampling2dBackward of int array
                                  154. | RowNum
                                  155. | ColNum
                                  156. | Row
                                  157. | Rows of int array
                                  158. | CopyRowTo
                                  159. | CopyColTo
                                  160. | Dot of bool * bool * elt * elt
                                  161. | Inv
                                  162. | Trace
                                  163. | Transpose of int array
                                  164. | ToRows
                                  165. | OfRows
                                  166. | Scalar_Add
                                  167. | Scalar_Sub
                                  168. | Scalar_Mul
                                  169. | Scalar_Div
                                  170. | Scalar_Pow
                                  171. | Scalar_Atan2
                                  172. | Scalar_Abs
                                  173. | Scalar_Neg
                                  174. | Scalar_Sqr
                                  175. | Scalar_Sqrt
                                  176. | Scalar_Exp
                                  177. | Scalar_Log
                                  178. | Scalar_Log2
                                  179. | Scalar_Log10
                                  180. | Scalar_Signum
                                  181. | Scalar_Floor
                                  182. | Scalar_Ceil
                                  183. | Scalar_Round
                                  184. | Scalar_Sin
                                  185. | Scalar_Cos
                                  186. | Scalar_Tan
                                  187. | Scalar_Sinh
                                  188. | Scalar_Cosh
                                  189. | Scalar_Tanh
                                  190. | Scalar_Asin
                                  191. | Scalar_Acos
                                  192. | Scalar_Atan
                                  193. | Scalar_Asinh
                                  194. | Scalar_Acosh
                                  195. | Scalar_Atanh
                                  196. | Scalar_Relu
                                  197. | Scalar_Dawsn
                                  198. | Scalar_Sigmoid
                                  199. | Fused_Adagrad of float * float
                                    (*

                                    TODO

                                    *)
                                  diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/index.html index 9f03d5aff..c54e3f32f 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape)

                                  Module Symbol.Shape

                                  Core functions
                                  val infer_shape : +Shape (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol.Shape)

                                  Module Symbol.Shape

                                  Core functions
                                  val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                  TODO

                                  diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/index.html index bbac1517d..9b5cf481f 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol)

                                  Module Operator.Symbol

                                  Core functions
                                  val op_to_str : Shape.Type.op -> string

                                  TODO

                                  val is_random_variable : Shape.Type.op -> bool

                                  TODO

                                  val refnum : 'a Owl_graph.node -> int

                                  TODO

                                  val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                  TODO

                                  val node_numel : Shape.Type.attr Owl_graph.node -> int

                                  TODO

                                  val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                  TODO

                                  val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                  TODO

                                  val shape_to_str : int array option array -> string

                                  TODO

                                  val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                  TODO

                                  val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                  TODO

                                  val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                  TODO

                                  val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                  TODO

                                  val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                  TODO

                                  val make_node : +Symbol (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator.Symbol)

                                  Module Operator.Symbol

                                  Core functions
                                  val op_to_str : Shape.Type.op -> string

                                  TODO

                                  val is_random_variable : Shape.Type.op -> bool

                                  TODO

                                  val refnum : 'a Owl_graph.node -> int

                                  TODO

                                  val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                  TODO

                                  val node_numel : Shape.Type.attr Owl_graph.node -> int

                                  TODO

                                  val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                  TODO

                                  val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                  TODO

                                  val shape_to_str : int array option array -> string

                                  TODO

                                  val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                  TODO

                                  val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                  TODO

                                  val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                  TODO

                                  val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                  TODO

                                  val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                  TODO

                                  val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/index.html index a1162f352..ad7105a7b 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator)

                                  Module Optimiser.Operator

                                  Vectorised functions
                                  val empty : int array -> Symbol.Shape.Type.arr

                                  TODO

                                  val zeros : int array -> Symbol.Shape.Type.arr

                                  TODO

                                  val ones : int array -> Symbol.Shape.Type.arr

                                  TODO

                                  val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                  TODO

                                  val sequential : +Operator (owl-base.Owl_computation_graph_sig.Sig.Optimiser.Operator)

                                  Module Optimiser.Operator

                                  Vectorised functions

                                  noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                                  val empty : int array -> Symbol.Shape.Type.arr

                                  empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                                  val zeros : int array -> Symbol.Shape.Type.arr

                                  zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                                  val ones : int array -> Symbol.Shape.Type.arr

                                  ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                                  val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                  create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                                  val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val uniform : + Symbol.Shape.Type.arr

                                  sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                                  val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val gaussian : + Symbol.Shape.Type.arr

                                  uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                                  val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                  TODO

                                  val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                  TODO

                                  val init_nd : + Symbol.Shape.Type.arr

                                  gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                                  val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                  bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                                  val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                  init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                                  val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                                  TODO

                                  val shape : Symbol.Shape.Type.arr -> int array

                                  TODO

                                  val numel : Symbol.Shape.Type.arr -> int

                                  TODO

                                  TODO

                                  val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                  TODO

                                  val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                  TODO

                                  val set_slice : + Symbol.Shape.Type.arr

                                  init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                                  val shape : Symbol.Shape.Type.arr -> int array

                                  shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                                  val numel : Symbol.Shape.Type.arr -> int

                                  numel arr returns the total number of elements in the array arr.

                                  get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                                  val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                  set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                                  val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                  get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                                  val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                  TODO

                                  val get_fancy : + unit

                                  set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                                  val set_fancy : + Symbol.Shape.Type.arr

                                  get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                                  val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                  TODO

                                  val copy_ : out:'a -> 'b -> 'c

                                  TODO

                                  val reset : Symbol.Shape.Type.arr -> unit

                                  TODO

                                  val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  TODO

                                  val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  TODO

                                  val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  TODO

                                  val pad : + unit

                                  set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                                  copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                                  val copy_ : out:'a -> 'b -> 'c

                                  copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                                  val reset : Symbol.Shape.Type.arr -> unit

                                  reset arr sets all elements of the array arr to zero.

                                  val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                                  reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                                  val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                                  val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                                  TODO

                                  val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                  TODO

                                  val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                  TODO

                                  val concatenate : + Symbol.Shape.Type.arr

                                  pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                                  val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                  expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                                  val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                  squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                                  val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                  TODO

                                  val concat : + Symbol.Shape.Type.arr

                                  concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                                  val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                  stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                                  val split : ?axis:int -> 'a -> 'b -> 'c

                                  TODO

                                  concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                                  val split : ?axis:int -> 'a -> 'b -> 'c

                                  split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                                  • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                                  val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                                  TODO

                                  val map : + Symbol.Shape.Type.arr * 'a array

                                  draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                                  map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                                  fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                                  TODO

                                  val delay : + Symbol.Shape.Type.arr

                                  scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                                  one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                                  delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                                  val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                  val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                  TODO

                                  lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                  val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                  print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                                  • max_row is an optional parameter specifying the maximum number of rows to print.
                                  • max_col is an optional parameter specifying the maximum number of columns to print.
                                  • header is an optional parameter to include a header in the output.
                                  • fmt is an optional parameter to specify the format of the output.

                                  abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                                  neg arr negates each element in the array arr. Returns a new array with each element negated.

                                  floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                                  ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                                  round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                                  sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                                  sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                                  log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                                  log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                                  log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                                  exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                                  sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                                  cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                                  tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                                  sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                                  cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                                  tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                                  asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                                  acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                                  atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                                  asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                                  acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                                  atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                                  val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                                  • axis specifies the axis along which to compute the minimum.
                                  • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                                  val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                                  • axis specifies the axis along which to compute the maximum.
                                  • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                                  val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val sum_reduce : + Symbol.Shape.Type.arr

                                  sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                                  • axis specifies the axis along which to compute the sum.
                                  • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                                  val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val log_sum_exp : + Symbol.Shape.Type.arr

                                  sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                                  • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                                  signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                                  sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                                  relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                                  dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                                  min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                                  max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                                  sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                                  log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                                  val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val clip_by_value : + Symbol.Shape.Type.arr

                                  log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                                  • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                                  • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                                  l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                                  l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                                  l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                                  val clip_by_l2norm : + Symbol.Shape.Type.arr

                                  clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                                  • amin specifies the minimum value to clip to.
                                  • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                                  clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                                  val scalar_pow : + Symbol.Shape.Type.arr

                                  pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                                  val pow_scalar : + Symbol.Shape.Type.arr

                                  scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                                  val atan2 : + Symbol.Shape.Type.arr

                                  pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                                  val scalar_atan2 : + Symbol.Shape.Type.arr

                                  atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                                  val atan2_scalar : + Symbol.Shape.Type.arr

                                  scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                                  val hypot : + Symbol.Shape.Type.arr

                                  atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                                  hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                                  min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                                  max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                                  add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                                  sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                                  mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                                  val add_scalar : + Symbol.Shape.Type.arr

                                  div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                                  val sub_scalar : + Symbol.Shape.Type.arr

                                  add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                  val mul_scalar : + Symbol.Shape.Type.arr

                                  sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                                  val div_scalar : + Symbol.Shape.Type.arr

                                  mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                  val scalar_add : + Symbol.Shape.Type.arr

                                  div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                  val scalar_sub : + Symbol.Shape.Type.arr

                                  scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                  val scalar_mul : + Symbol.Shape.Type.arr

                                  scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                                  val scalar_div : + Symbol.Shape.Type.arr

                                  scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                  scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                                  val elt_equal : + Symbol.Shape.Type.arr

                                  fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                                  val elt_not_equal : + Symbol.Shape.Type.arr

                                  elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                                  val elt_less : + Symbol.Shape.Type.arr

                                  elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                                  val elt_greater : + Symbol.Shape.Type.arr

                                  elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                                  val elt_less_equal : + Symbol.Shape.Type.arr

                                  elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                                  val elt_greater_equal : + Symbol.Shape.Type.arr

                                  elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                                  val elt_equal_scalar : + Symbol.Shape.Type.arr

                                  elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                                  val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                                  elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                                  val elt_less_scalar : + Symbol.Shape.Type.arr

                                  elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                                  val elt_greater_scalar : + Symbol.Shape.Type.arr

                                  elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                                  val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                                  elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                                  TODO

                                  val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                                  elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                                  TODO

                                  val conv1d : + Symbol.Shape.Type.arr

                                  elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                                  val conv2d : + Symbol.Shape.Type.arr

                                  conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                                  • padding specifies the padding strategy (default is "valid").
                                  • strides specifies the stride length. Returns a new array with the result of the convolution.
                                  val conv3d : + Symbol.Shape.Type.arr

                                  conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                                  • padding specifies the padding strategy (default is "valid").
                                  • strides specifies the stride length. Returns a new array with the result of the convolution.
                                  val transpose_conv1d : + Symbol.Shape.Type.arr

                                  conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                                  • padding specifies the padding strategy (default is "valid").
                                  • strides specifies the stride length. Returns a new array with the result of the convolution.
                                  val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val transpose_conv2d : + Symbol.Shape.Type.arr

                                  transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                  • padding specifies the padding strategy (default is "valid").
                                  • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                  val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val transpose_conv3d : + Symbol.Shape.Type.arr

                                  transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                  • padding specifies the padding strategy (default is "valid").
                                  • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                  val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val dilated_conv1d : + Symbol.Shape.Type.arr

                                  transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                  • padding specifies the padding strategy (default is "valid").
                                  • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                  val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val dilated_conv2d : + Symbol.Shape.Type.arr

                                  dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                                  • padding specifies the padding strategy (default is "valid").
                                  • strides specifies the stride length.
                                  • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                  val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val dilated_conv3d : + Symbol.Shape.Type.arr

                                  dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                                  • padding specifies the padding strategy (default is "valid").
                                  • strides specifies the stride length.
                                  • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                  val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val max_pool1d : + Symbol.Shape.Type.arr

                                  dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                                  • padding specifies the padding strategy (default is "valid").
                                  • strides specifies the stride length.
                                  • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                  val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val max_pool2d : + Symbol.Shape.Type.arr

                                  max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                                  • padding specifies the padding strategy (default is "valid").
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                  val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val max_pool3d : + Symbol.Shape.Type.arr

                                  max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                                  • padding specifies the padding strategy (default is "valid").
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                  val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val avg_pool1d : + Symbol.Shape.Type.arr

                                  max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                                  • padding specifies the padding strategy (default is "valid").
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                  val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val avg_pool2d : + Symbol.Shape.Type.arr

                                  avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                                  • padding specifies the padding strategy (default is "valid").
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                  val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val avg_pool3d : + Symbol.Shape.Type.arr

                                  avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                                  • padding specifies the padding strategy (default is "valid").
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                  val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  TODO

                                  val conv1d_backward_input : + Symbol.Shape.Type.arr

                                  avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                                  • padding specifies the padding strategy (default is "valid").
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                  val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  upsampling2d input size performs a 2-dimensional upsampling on the input array.

                                  • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                                  TODO

                                  val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                  conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                                  • input is the original input array.
                                  • kernel is the convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                  val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val conv2d_backward_input : + Symbol.Shape.Type.arr

                                  conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                                  • input is the original input array.
                                  • kernel is the convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                  TODO

                                  val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                  conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                                  • input is the original input array.
                                  • kernel is the convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                  val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val conv3d_backward_input : + Symbol.Shape.Type.arr

                                  conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                                  • input is the original input array.
                                  • kernel is the convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                  TODO

                                  val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                  conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                                  • input is the original input array.
                                  • kernel is the convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                  val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                                  conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                                  • input is the original input array.
                                  • kernel is the convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                                  val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                  transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                                  • input is the original input array.
                                  • kernel is the transposed convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                  val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                                  transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                                  • input is the original input array.
                                  • kernel is the transposed convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                  val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                  transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                                  • input is the original input array.
                                  • kernel is the transposed convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                  val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                                  transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                                  • input is the original input array.
                                  • kernel is the transposed convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                  val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                  transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                                  • input is the original input array.
                                  • kernel is the transposed convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                  val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                                  transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                                  • input is the original input array.
                                  • kernel is the transposed convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                  val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                  dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                                  • input is the original input array.
                                  • kernel is the dilated convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • dilations specifies the dilation rate.
                                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                  val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                                  dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                                  • input is the original input array.
                                  • kernel is the dilated convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • dilations specifies the dilation rate.
                                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                  val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                  dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                                  • input is the original input array.
                                  • kernel is the dilated convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • dilations specifies the dilation rate.
                                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                  val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                                  dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                                  • input is the original input array.
                                  • kernel is the dilated convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • dilations specifies the dilation rate.
                                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                  val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                  dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                                  • input is the original input array.
                                  • kernel is the dilated convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • dilations specifies the dilation rate.
                                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                  val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val max_pool1d_backward : + Symbol.Shape.Type.arr

                                  dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                                  • input is the original input array.
                                  • kernel is the dilated convolutional kernel used during the forward pass.
                                  • strides specifies the stride length.
                                  • dilations specifies the dilation rate.
                                  • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                  val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val max_pool2d_backward : + Symbol.Shape.Type.arr

                                  max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                                  • padding specifies the padding strategy used during the forward pass.
                                  • input is the original input array.
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                  val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val max_pool3d_backward : + Symbol.Shape.Type.arr

                                  max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                                  • padding specifies the padding strategy used during the forward pass.
                                  • input is the original input array.
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                  val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val avg_pool1d_backward : + Symbol.Shape.Type.arr

                                  max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                                  • padding specifies the padding strategy used during the forward pass.
                                  • input is the original input array.
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                  val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val avg_pool2d_backward : + Symbol.Shape.Type.arr

                                  avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                                  • padding specifies the padding strategy used during the forward pass.
                                  • input is the original input array.
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                  val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val avg_pool3d_backward : + Symbol.Shape.Type.arr

                                  avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                                  • padding specifies the padding strategy used during the forward pass.
                                  • input is the original input array.
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                  val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val upsampling2d_backward : + Symbol.Shape.Type.arr

                                  avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                                  • padding specifies the padding strategy used during the forward pass.
                                  • input is the original input array.
                                  • pool_size specifies the size of the pooling window.
                                  • strides specifies the stride length.
                                  • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                  val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val row_num : Symbol.Shape.Type.arr -> int

                                  TODO

                                  val col_num : Symbol.Shape.Type.arr -> int

                                  TODO

                                  val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  TODO

                                  val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                  TODO

                                  val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                  TODO

                                  TODO

                                  upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                                  • input is the original input array.
                                  • size specifies the upsampling factors for each dimension.
                                  • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                                  val row_num : Symbol.Shape.Type.arr -> int

                                  row_num arr returns the number of rows in the array arr.

                                  val col_num : Symbol.Shape.Type.arr -> int

                                  col_num arr returns the number of columns in the array arr.

                                  row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                                  val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                  rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                                  val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                  copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                                  val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                  copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                                  diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                                  trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                                  val transpose : + Symbol.Shape.Type.arr

                                  dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                                  val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                  TODO

                                  val to_rows : Symbol.Shape.Type.arr -> 'a array

                                  TODO

                                  TODO

                                  val to_cols : Symbol.Shape.Type.arr -> 'a array

                                  TODO

                                  TODO

                                  val of_array : + Symbol.Shape.Type.arr

                                  transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                                  val to_rows : Symbol.Shape.Type.arr -> 'a array

                                  to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                                  of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                                  val to_cols : Symbol.Shape.Type.arr -> 'a array

                                  to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                                  of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                                  val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                                  TODO

                                  val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                  TODO

                                  val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                  TODO

                                  Scalar functions
                                  module Scalar : sig ... end
                                  module Mat : sig ... end
                                  module Linalg : sig ... end
                                  + Symbol.Shape.Type.arr

                                  of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                                  val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                  of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                                  val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                  to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                                  Scalar functions
                                  module Scalar : sig ... end
                                  module Mat : sig ... end
                                  module Linalg : sig ... end
                                  diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/index.html index 16e2293c4..85e6b4e40 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_computation_graph_sig.Sig.Optimiser)

                                  Module Sig.Optimiser

                                  Core functions
                                  val estimate_complexity : 'a Owl_graph.node array -> int * int

                                  TODO

                                  val optimise_nodes : +Optimiser (owl-base.Owl_computation_graph_sig.Sig.Optimiser)

                                  Module Sig.Optimiser

                                  Core functions
                                  val estimate_complexity : 'a Owl_graph.node array -> int * int

                                  TODO

                                  val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

                                  TODO

                                  diff --git a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/index.html b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/index.html index bba892c32..75728aaec 100644 --- a/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_computation_graph_sig/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_computation_graph_sig.Sig)

                                  Module type Owl_computation_graph_sig.Sig

                                  Type definition
                                  type graph

                                  TODO

                                  Core functions
                                  val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                                  TODO

                                  val graph_to_dot : graph -> string

                                  TODO

                                  val graph_to_trace : graph -> string

                                  TODO

                                  val save_graph : 'a -> string -> unit

                                  TODO

                                  val load_graph : string -> 'a * 'b

                                  TODO

                                  val collect_rvs : +Sig (owl-base.Owl_computation_graph_sig.Sig)

                                  Module type Owl_computation_graph_sig.Sig

                                  Type definition
                                  type graph

                                  TODO

                                  Core functions
                                  val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                                  TODO

                                  val graph_to_dot : graph -> string

                                  TODO

                                  val graph_to_trace : graph -> string

                                  TODO

                                  val save_graph : 'a -> string -> unit

                                  TODO

                                  val load_graph : string -> 'a * 'b

                                  TODO

                                  val invalidate_rvs : graph -> unit

                                  TODO

                                  val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_computation_operator/Make/Linalg/index.html b/docs/owl-base/Owl_computation_operator/Make/Linalg/index.html index d0f060c84..fc6e4b87e 100644 --- a/docs/owl-base/Owl_computation_operator/Make/Linalg/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/Linalg/index.html @@ -1,2 +1,2 @@ -Linalg (owl-base.Owl_computation_operator.Make.Linalg)

                                  Module Make.Linalg

                                  val logdet : 'a -> 'b
                                  val chol : ?upper:bool -> 'a -> 'b
                                  val svd : ?thin:bool -> 'a -> 'b
                                  val qr : 'a -> 'b
                                  val lq : 'a -> 'b
                                  val sylvester : 'a -> 'b -> 'c -> 'd
                                  val lyapunov : 'a -> 'b -> 'c
                                  val discrete_lyapunov : ?solver:[> `default ] -> 'a -> 'b -> 'c
                                  val linsolve : ?trans:'a -> ?typ:[> `n ] -> 'b -> 'c -> 'd
                                  val care : ?diag_r:bool -> 'a -> 'b -> 'c -> 'd -> 'e
                                  val dare : ?diag_r:bool -> 'a -> 'b -> 'c -> 'd -> 'e
                                  +Linalg (owl-base.Owl_computation_operator.Make.Linalg)

                                  Module Make.Linalg

                                  val logdet : 'a -> 'b
                                  val chol : ?upper:bool -> 'a -> 'b
                                  val svd : ?thin:bool -> 'a -> 'b
                                  val qr : 'a -> 'b
                                  val lq : 'a -> 'b
                                  val sylvester : 'a -> 'b -> 'c -> 'd
                                  val lyapunov : 'a -> 'b -> 'c
                                  val discrete_lyapunov : ?solver:[> `default ] -> 'a -> 'b -> 'c
                                  val linsolve : ?trans:'a -> ?typ:[> `n ] -> 'b -> 'c -> 'd
                                  val care : ?diag_r:bool -> 'a -> 'b -> 'c -> 'd -> 'e
                                  val dare : ?diag_r:bool -> 'a -> 'b -> 'c -> 'd -> 'e
                                  diff --git a/docs/owl-base/Owl_computation_operator/Make/Mat/index.html b/docs/owl-base/Owl_computation_operator/Make/Mat/index.html index b8a0276aa..146785fd7 100644 --- a/docs/owl-base/Owl_computation_operator/Make/Mat/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_operator.Make.Mat)

                                  Module Make.Mat

                                  val eye : 'a -> 'b
                                  val diagm : ?k:'a -> 'b -> 'c
                                  val tril : ?k:'a -> 'b -> 'c
                                  val triu : ?k:'a -> 'b -> 'c
                                  +Mat (owl-base.Owl_computation_operator.Make.Mat)

                                  Module Make.Mat

                                  val eye : 'a -> 'b
                                  val diagm : ?k:'a -> 'b -> 'c
                                  val tril : ?k:'a -> 'b -> 'c
                                  val triu : ?k:'a -> 'b -> 'c
                                  diff --git a/docs/owl-base/Owl_computation_operator/Make/Scalar/index.html b/docs/owl-base/Owl_computation_operator/Make/Scalar/index.html index 4d9cfdc31..f1accd252 100644 --- a/docs/owl-base/Owl_computation_operator/Make/Scalar/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/Scalar/index.html @@ -1,5 +1,5 @@ -Scalar (owl-base.Owl_computation_operator.Make.Scalar)

                                  Module Make.Scalar

                                  val add : +Scalar (owl-base.Owl_computation_operator.Make.Scalar)

                                  Module Make.Scalar

                                  val sub : diff --git a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Linalg/index.html index 5d01867ac..f373bfcbc 100644 --- a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device.A.Linalg)

                                  Module A.Linalg

                                  val inv : arr -> arr
                                  val logdet : arr -> elt
                                  val chol : ?upper:bool -> arr -> arr
                                  val svd : ?thin:bool -> arr -> arr * arr * arr
                                  val qr : arr -> arr * arr
                                  val lq : arr -> arr * arr
                                  val sylvester : arr -> arr -> arr -> arr
                                  val lyapunov : arr -> arr -> arr
                                  val discrete_lyapunov : +Linalg (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device.A.Linalg)

                                  Module A.Linalg

                                  val inv : arr -> arr
                                  val logdet : arr -> elt
                                  val chol : ?upper:bool -> arr -> arr
                                  val svd : ?thin:bool -> arr -> arr * arr * arr
                                  val qr : arr -> arr * arr
                                  val lq : arr -> arr * arr
                                  val sylvester : arr -> arr -> arr -> arr
                                  val lyapunov : arr -> arr -> arr
                                  val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Mat/index.html index 4f0344c6f..61eaedde2 100644 --- a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device.A.Mat)

                                  Module A.Mat

                                  val diagm : ?k:int -> arr -> arr
                                  val triu : ?k:int -> arr -> arr
                                  val tril : ?k:int -> arr -> arr
                                  val eye : int -> arr
                                  +Mat (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device.A.Mat)

                                  Module A.Mat

                                  val diagm : ?k:int -> arr -> arr
                                  val triu : ?k:int -> arr -> arr
                                  val tril : ?k:int -> arr -> arr
                                  val eye : int -> arr
                                  diff --git a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Scalar/index.html index b83479ea9..1c13f3f55 100644 --- a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device.A.Scalar)

                                  Module A.Scalar

                                  val add : elt -> elt -> elt
                                  val sub : elt -> elt -> elt
                                  val mul : elt -> elt -> elt
                                  val div : elt -> elt -> elt
                                  val pow : elt -> elt -> elt
                                  val atan2 : elt -> elt -> elt
                                  val abs : elt -> elt
                                  val neg : elt -> elt
                                  val sqr : elt -> elt
                                  val sqrt : elt -> elt
                                  val exp : elt -> elt
                                  val log : elt -> elt
                                  val log2 : elt -> elt
                                  val log10 : elt -> elt
                                  val signum : elt -> elt
                                  val floor : elt -> elt
                                  val ceil : elt -> elt
                                  val round : elt -> elt
                                  val sin : elt -> elt
                                  val cos : elt -> elt
                                  val tan : elt -> elt
                                  val sinh : elt -> elt
                                  val cosh : elt -> elt
                                  val tanh : elt -> elt
                                  val asin : elt -> elt
                                  val acos : elt -> elt
                                  val atan : elt -> elt
                                  val asinh : elt -> elt
                                  val acosh : elt -> elt
                                  val atanh : elt -> elt
                                  val relu : elt -> elt
                                  val dawsn : elt -> elt
                                  val sigmoid : elt -> elt
                                  +Scalar (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device.A.Scalar)

                                  Module A.Scalar

                                  val add : elt -> elt -> elt
                                  val sub : elt -> elt -> elt
                                  val mul : elt -> elt -> elt
                                  val div : elt -> elt -> elt
                                  val pow : elt -> elt -> elt
                                  val atan2 : elt -> elt -> elt
                                  val abs : elt -> elt
                                  val neg : elt -> elt
                                  val sqr : elt -> elt
                                  val sqrt : elt -> elt
                                  val exp : elt -> elt
                                  val log : elt -> elt
                                  val log2 : elt -> elt
                                  val log10 : elt -> elt
                                  val signum : elt -> elt
                                  val floor : elt -> elt
                                  val ceil : elt -> elt
                                  val round : elt -> elt
                                  val sin : elt -> elt
                                  val cos : elt -> elt
                                  val tan : elt -> elt
                                  val sinh : elt -> elt
                                  val cosh : elt -> elt
                                  val tanh : elt -> elt
                                  val asin : elt -> elt
                                  val acos : elt -> elt
                                  val atan : elt -> elt
                                  val asinh : elt -> elt
                                  val acosh : elt -> elt
                                  val atanh : elt -> elt
                                  val relu : elt -> elt
                                  val dawsn : elt -> elt
                                  val sigmoid : elt -> elt
                                  diff --git a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/index.html index cf7f6c337..7ddc6c61c 100644 --- a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device.A)

                                  Module Device.A

                                  include Owl_types_ndarray_algodiff.Sig
                                  include Owl_types_ndarray_eltcmp.Sig
                                  include Owl_types_ndarray_basic.Sig
                                  type arr
                                  type elt
                                  val empty : int array -> arr
                                  val zeros : int array -> arr
                                  val ones : int array -> arr
                                  val create : int array -> elt -> arr
                                  val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                  val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                  val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                  val bernoulli : ?p:elt -> int array -> arr
                                  val init : int array -> (int -> elt) -> arr
                                  val init_nd : int array -> (int array -> elt) -> arr
                                  val shape : arr -> int array
                                  val numel : arr -> int
                                  val get : arr -> int array -> elt
                                  val set : arr -> int array -> elt -> unit
                                  val get_slice : int list list -> arr -> arr
                                  val set_slice : int list list -> arr -> arr -> unit
                                  val get_fancy : Owl_types_common.index list -> arr -> arr
                                  val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                  val copy : arr -> arr
                                  val copy_ : out:arr -> arr -> unit
                                  val reset : arr -> unit
                                  val reshape : arr -> int array -> arr
                                  val reverse : arr -> arr
                                  val tile : arr -> int array -> arr
                                  val repeat : arr -> int array -> arr
                                  val concatenate : ?axis:int -> arr array -> arr
                                  val stack : ?axis:int -> arr array -> arr
                                  val split : ?axis:int -> int array -> arr -> arr array
                                  val expand : ?hi:bool -> arr -> int -> arr
                                  val squeeze : ?axis:int array -> arr -> arr
                                  val draw : ?axis:int -> arr -> int -> arr * int array
                                  val map : (elt -> elt) -> arr -> arr
                                  val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                  val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                  val one_hot : int -> arr -> arr
                                  val pad : ?v:elt -> int list list -> arr -> arr
                                  val print : +A (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device.A)

                                  Module Device.A

                                  include Owl_types_ndarray_algodiff.Sig
                                  include Owl_types_ndarray_eltcmp.Sig
                                  include Owl_types_ndarray_basic.Sig
                                  type arr
                                  type elt
                                  val empty : int array -> arr
                                  val zeros : int array -> arr
                                  val ones : int array -> arr
                                  val create : int array -> elt -> arr
                                  val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                  val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                  val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                  val bernoulli : ?p:elt -> int array -> arr
                                  val init : int array -> (int -> elt) -> arr
                                  val init_nd : int array -> (int array -> elt) -> arr
                                  val shape : arr -> int array
                                  val numel : arr -> int
                                  val get : arr -> int array -> elt
                                  val set : arr -> int array -> elt -> unit
                                  val get_slice : int list list -> arr -> arr
                                  val set_slice : int list list -> arr -> arr -> unit
                                  val get_fancy : Owl_types_common.index list -> arr -> arr
                                  val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                  val copy : arr -> arr
                                  val copy_ : out:arr -> arr -> unit
                                  val reset : arr -> unit
                                  val reshape : arr -> int array -> arr
                                  val reverse : arr -> arr
                                  val tile : arr -> int array -> arr
                                  val repeat : arr -> int array -> arr
                                  val concatenate : ?axis:int -> arr array -> arr
                                  val stack : ?axis:int -> arr array -> arr
                                  val split : ?axis:int -> int array -> arr -> arr array
                                  val expand : ?hi:bool -> arr -> int -> arr
                                  val squeeze : ?axis:int array -> arr -> arr
                                  val draw : ?axis:int -> arr -> int -> arr * int array
                                  val map : (elt -> elt) -> arr -> arr
                                  val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                  val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                  val one_hot : int -> arr -> arr
                                  val pad : ?v:elt -> int list list -> arr -> arr
                                  val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/index.html index fa07b5c4e..4e65b0fb5 100644 --- a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device)

                                  Module Type.Device

                                  Type definition
                                  type device

                                  TODO

                                  type value

                                  TODO

                                  Core functions
                                  val make_device : unit -> device

                                  TODO

                                  val arr_to_value : A.arr -> value

                                  TODO

                                  val value_to_arr : value -> A.arr

                                  TODO

                                  val elt_to_value : A.elt -> value

                                  TODO

                                  val value_to_elt : value -> A.elt

                                  TODO

                                  val value_to_float : value -> float

                                  TODO

                                  val is_arr : value -> bool

                                  TODO

                                  val is_elt : value -> bool

                                  TODO

                                  +Device (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type.Device)

                                  Module Type.Device

                                  Type definition
                                  type device

                                  TODO

                                  type value

                                  TODO

                                  Core functions
                                  val make_device : unit -> device

                                  TODO

                                  val arr_to_value : A.arr -> value

                                  TODO

                                  val value_to_arr : value -> A.arr

                                  TODO

                                  val elt_to_value : A.elt -> value

                                  TODO

                                  val value_to_elt : value -> A.elt

                                  TODO

                                  val value_to_float : value -> float

                                  TODO

                                  val is_arr : value -> bool

                                  TODO

                                  val is_elt : value -> bool

                                  TODO

                                  diff --git a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/index.html index 9e4a61664..c2bca76fd 100644 --- a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type)

                                  Module Shape.Type

                                  Type definition
                                  type state =
                                  1. | Valid
                                  2. | Invalid
                                    (*

                                    TODO

                                    *)

                                  TODO

                                  and block = {
                                  1. size : int;
                                  2. block_id : int;
                                  3. mutable active : t option;
                                  4. mutable memory : Device.value;
                                  5. mutable nodes : t list;
                                  }

                                  block type keeps a reference to a block of memory and to the nodes sharing that block.

                                  and attr = {
                                  1. mutable op : op;
                                  2. mutable freeze : bool;
                                  3. mutable reuse : bool;
                                  4. mutable state : state;
                                  5. mutable shape : int array option array;
                                  6. mutable value : Device.value array;
                                  7. mutable block : block array option;
                                  }

                                  TODO

                                  and arr =
                                  1. | Arr of t
                                  and elt =
                                  1. | Elt of t
                                  and op =
                                  1. | Noop
                                  2. | Var
                                  3. | Const
                                  4. | Empty of int array
                                  5. | Zeros of int array
                                  6. | Ones of int array
                                  7. | Create of int array
                                  8. | Sequential of int array
                                  9. | Uniform of int array
                                  10. | Gaussian of int array
                                  11. | Bernoulli of int array
                                  12. | Init of int array * int -> elt
                                  13. | Get of int array
                                  14. | Set of int array
                                  15. | GetSlice of int list list
                                  16. | SetSlice of int list list
                                  17. | GetFancy of Owl_types_common.index list
                                  18. | SetFancy of Owl_types_common.index list
                                  19. | Copy
                                  20. | Reset
                                  21. | Reshape of int array
                                  22. | Reverse
                                  23. | Tile of int array
                                  24. | Repeat of int array
                                  25. | Pad of elt * int list list
                                  26. | Concatenate of int
                                  27. | Stack of int
                                  28. | Split of int * int array
                                  29. | Draw of int * int
                                  30. | Map of elt -> elt
                                  31. | Fold of int * elt -> elt -> elt
                                  32. | Scan of int * elt -> elt -> elt
                                  33. | OneHot of int
                                  34. | OfArray of int array
                                  35. | Delay of Device.A.arr -> Device.A.arr
                                  36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                  37. | LazyPrint of int option +Type (owl-base.Owl_computation_operator.Make.Symbol.Shape.Type)

                                    Module Shape.Type

                                    Type definition
                                    type state =
                                    1. | Valid
                                    2. | Invalid
                                      (*

                                      TODO

                                      *)

                                    TODO

                                    and block = {
                                    1. size : int;
                                    2. block_id : int;
                                    3. mutable active : t option;
                                    4. mutable memory : Device.value;
                                    5. mutable nodes : t list;
                                    }

                                    block type keeps a reference to a block of memory and to the nodes sharing that block.

                                    and attr = {
                                    1. mutable op : op;
                                    2. mutable freeze : bool;
                                    3. mutable reuse : bool;
                                    4. mutable state : state;
                                    5. mutable shape : int array option array;
                                    6. mutable value : Device.value array;
                                    7. mutable block : block array option;
                                    }

                                    TODO

                                    and arr =
                                    1. | Arr of t
                                    and elt =
                                    1. | Elt of t
                                    and op =
                                    1. | Noop
                                    2. | Var
                                    3. | Const
                                    4. | Empty of int array
                                    5. | Zeros of int array
                                    6. | Ones of int array
                                    7. | Create of int array
                                    8. | Sequential of int array
                                    9. | Uniform of int array
                                    10. | Gaussian of int array
                                    11. | Bernoulli of int array
                                    12. | Init of int array * int -> elt
                                    13. | Get of int array
                                    14. | Set of int array
                                    15. | GetSlice of int list list
                                    16. | SetSlice of int list list
                                    17. | GetFancy of Owl_types_common.index list
                                    18. | SetFancy of Owl_types_common.index list
                                    19. | Copy
                                    20. | Reset
                                    21. | Reshape of int array
                                    22. | Reverse
                                    23. | Tile of int array
                                    24. | Repeat of int array
                                    25. | Pad of elt * int list list
                                    26. | Concatenate of int
                                    27. | Stack of int
                                    28. | Split of int * int array
                                    29. | Draw of int * int
                                    30. | Map of elt -> elt
                                    31. | Fold of int * elt -> elt -> elt
                                    32. | Scan of int * elt -> elt -> elt
                                    33. | OneHot of int
                                    34. | OfArray of int array
                                    35. | Delay of Device.A.arr -> Device.A.arr
                                    36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                    37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                    38. | Abs
                                    39. | Neg
                                    40. | Floor
                                    41. | Ceil
                                    42. | Round
                                    43. | Sqr
                                    44. | Sqrt
                                    45. | Log
                                    46. | Log2
                                    47. | Log10
                                    48. | Exp
                                    49. | Sin
                                    50. | Cos
                                    51. | Tan
                                    52. | Sinh
                                    53. | Cosh
                                    54. | Tanh
                                    55. | Asin
                                    56. | Acos
                                    57. | Atan
                                    58. | Asinh
                                    59. | Acosh
                                    60. | Atanh
                                    61. | Min of bool * int
                                    62. | Max of bool * int
                                    63. | Sum of bool * int
                                    64. | SumReduce of int array
                                    65. | Signum
                                    66. | Sigmoid
                                    67. | Relu
                                    68. | Dawsn
                                    69. | Min'
                                    70. | Max'
                                    71. | Sum'
                                    72. | LogSumExp'
                                    73. | LogSumExp of bool * int
                                    74. | L1norm'
                                    75. | L2norm'
                                    76. | L2NormSqr'
                                    77. | ClipByValue
                                    78. | ClipByL2norm
                                    79. | Pow
                                    80. | ScalarPow
                                    81. | PowScalar
                                    82. | Atan2
                                    83. | ScalarAtan2
                                    84. | Atan2Scalar
                                    85. | Hypot
                                    86. | Min2
                                    87. | Max2
                                    88. | Add
                                    89. | Sub
                                    90. | Mul
                                    91. | Div
                                    92. | AddScalar
                                    93. | SubScalar
                                    94. | MulScalar
                                    95. | DivScalar
                                    96. | ScalarAdd
                                    97. | ScalarSub
                                    98. | ScalarMul
                                    99. | ScalarDiv
                                    100. | FMA
                                    101. | EltEqual
                                    102. | EltNotEqual
                                    103. | EltLess
                                    104. | EltGreater
                                    105. | EltLessEqual
                                    106. | EltGreaterEqual
                                    107. | EltEqualScalar
                                    108. | EltNotEqualScalar
                                    109. | EltLessScalar
                                    110. | EltGreaterScalar
                                    111. | EltLessEqualScalar
                                    112. | EltGreaterEqualScalar
                                    113. | Conv1d of Owl_types_common.padding * int array
                                    114. | Conv2d of Owl_types_common.padding * int array
                                    115. | Conv3d of Owl_types_common.padding * int array
                                    116. | TransposeConv1d of Owl_types_common.padding * int array
                                    117. | TransposeConv2d of Owl_types_common.padding * int array
                                    118. | TransposeConv3d of Owl_types_common.padding * int array
                                    119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                    120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                    121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                    122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                    123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                    124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                    125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                    126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                    127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                    128. | UpSampling2d of int array
                                    129. | Conv1dBackwardInput of int array
                                    130. | Conv1dBackwardKernel of int array
                                    131. | Conv2dBackwardInput of int array
                                    132. | Conv2dBackwardKernel of int array
                                    133. | Conv3dBackwardInput of int array
                                    134. | Conv3dBackwardKernel of int array
                                    135. | TransposeConv1dBackwardInput of int array
                                    136. | TransposeConv1dBackwardKernel of int array
                                    137. | TransposeConv2dBackwardInput of int array
                                    138. | TransposeConv2dBackwardKernel of int array
                                    139. | TransposeConv3dBackwardInput of int array
                                    140. | TransposeConv3dBackwardKernel of int array
                                    141. | DilatedConv1dBackwardInput of int array * int array
                                    142. | DilatedConv1dBackwardKernel of int array * int array
                                    143. | DilatedConv2dBackwardInput of int array * int array
                                    144. | DilatedConv2dBackwardKernel of int array * int array
                                    145. | DilatedConv3dBackwardInput of int array * int array
                                    146. | DilatedConv3dBackwardKernel of int array * int array
                                    147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                    148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                    149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                    150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                    151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                    152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                    153. | UpSampling2dBackward of int array
                                    154. | RowNum
                                    155. | ColNum
                                    156. | Row
                                    157. | Rows of int array
                                    158. | CopyRowTo
                                    159. | CopyColTo
                                    160. | Dot of bool * bool * elt * elt
                                    161. | Inv
                                    162. | Trace
                                    163. | Transpose of int array
                                    164. | ToRows
                                    165. | OfRows
                                    166. | Scalar_Add
                                    167. | Scalar_Sub
                                    168. | Scalar_Mul
                                    169. | Scalar_Div
                                    170. | Scalar_Pow
                                    171. | Scalar_Atan2
                                    172. | Scalar_Abs
                                    173. | Scalar_Neg
                                    174. | Scalar_Sqr
                                    175. | Scalar_Sqrt
                                    176. | Scalar_Exp
                                    177. | Scalar_Log
                                    178. | Scalar_Log2
                                    179. | Scalar_Log10
                                    180. | Scalar_Signum
                                    181. | Scalar_Floor
                                    182. | Scalar_Ceil
                                    183. | Scalar_Round
                                    184. | Scalar_Sin
                                    185. | Scalar_Cos
                                    186. | Scalar_Tan
                                    187. | Scalar_Sinh
                                    188. | Scalar_Cosh
                                    189. | Scalar_Tanh
                                    190. | Scalar_Asin
                                    191. | Scalar_Acos
                                    192. | Scalar_Atan
                                    193. | Scalar_Asinh
                                    194. | Scalar_Acosh
                                    195. | Scalar_Atanh
                                    196. | Scalar_Relu
                                    197. | Scalar_Dawsn
                                    198. | Scalar_Sigmoid
                                    199. | Fused_Adagrad of float * float
                                      (*

                                      TODO

                                      *)
                                    diff --git a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/index.html b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/index.html index a5b038ebc..a821b4da5 100644 --- a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_operator.Make.Symbol.Shape)

                                    Module Symbol.Shape

                                    Core functions
                                    val infer_shape : +Shape (owl-base.Owl_computation_operator.Make.Symbol.Shape)

                                    Module Symbol.Shape

                                    Core functions
                                    val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                    TODO

                                    diff --git a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/index.html b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/index.html index 3fb55fca0..5ba99e85a 100644 --- a/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/argument-1-Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_operator.Make.Symbol)

                                    Parameter Make.Symbol

                                    Core functions
                                    val op_to_str : Shape.Type.op -> string

                                    TODO

                                    val is_random_variable : Shape.Type.op -> bool

                                    TODO

                                    val refnum : 'a Owl_graph.node -> int

                                    TODO

                                    val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                    TODO

                                    val node_numel : Shape.Type.attr Owl_graph.node -> int

                                    TODO

                                    val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                    TODO

                                    val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                    TODO

                                    val shape_to_str : int array option array -> string

                                    TODO

                                    val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                    TODO

                                    val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                    TODO

                                    val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                    TODO

                                    val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                    TODO

                                    val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                    TODO

                                    val make_node : +Symbol (owl-base.Owl_computation_operator.Make.Symbol)

                                    Parameter Make.Symbol

                                    Core functions
                                    val op_to_str : Shape.Type.op -> string

                                    TODO

                                    val is_random_variable : Shape.Type.op -> bool

                                    TODO

                                    val refnum : 'a Owl_graph.node -> int

                                    TODO

                                    val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                    TODO

                                    val node_numel : Shape.Type.attr Owl_graph.node -> int

                                    TODO

                                    val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                    TODO

                                    val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                    TODO

                                    val shape_to_str : int array option array -> string

                                    TODO

                                    val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                    TODO

                                    val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                    TODO

                                    val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                    TODO

                                    val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                    TODO

                                    val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                    TODO

                                    val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_operator/Make/index.html b/docs/owl-base/Owl_computation_operator/Make/index.html index ff2ee8d43..d523af66a 100644 --- a/docs/owl-base/Owl_computation_operator/Make/index.html +++ b/docs/owl-base/Owl_computation_operator/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_computation_operator.Make)

                                    Module Owl_computation_operator.Make

                                    Parameters

                                    Signature

                                    module Symbol = Symbol
                                    val empty : int array -> Symbol.Shape.Type.arr
                                    val zeros : int array -> Symbol.Shape.Type.arr
                                    val ones : int array -> Symbol.Shape.Type.arr
                                    val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr
                                    val sequential : +Make (owl-base.Owl_computation_operator.Make)

                                    Module Owl_computation_operator.Make

                                    Parameters

                                    Signature

                                    module Symbol = Symbol
                                    val empty : int array -> Symbol.Shape.Type.arr
                                    val zeros : int array -> Symbol.Shape.Type.arr
                                    val ones : int array -> Symbol.Shape.Type.arr
                                    val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr
                                    val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> diff --git a/docs/owl-base/Owl_computation_operator/index.html b/docs/owl-base/Owl_computation_operator/index.html index 75c462d04..eed8daa2a 100644 --- a/docs/owl-base/Owl_computation_operator/index.html +++ b/docs/owl-base/Owl_computation_operator/index.html @@ -1,2 +1,2 @@ -Owl_computation_operator (owl-base.Owl_computation_operator)

                                    Module Owl_computation_operator

                                    module Make (Symbol : Owl_computation_symbol_sig.Sig) : sig ... end
                                    +Owl_computation_operator (owl-base.Owl_computation_operator)

                                    Module Owl_computation_operator

                                    module Make (Symbol : Owl_computation_symbol_sig.Sig) : sig ... end
                                    diff --git a/docs/owl-base/Owl_computation_operator_sig/index.html b/docs/owl-base/Owl_computation_operator_sig/index.html index 434b83de1..163457f12 100644 --- a/docs/owl-base/Owl_computation_operator_sig/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/index.html @@ -1,2 +1,2 @@ -Owl_computation_operator_sig (owl-base.Owl_computation_operator_sig)

                                    Module Owl_computation_operator_sig

                                    module type Sig = sig ... end
                                    +Owl_computation_operator_sig (owl-base.Owl_computation_operator_sig)

                                    Module Owl_computation_operator_sig

                                    module type Sig = sig ... end
                                    diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Linalg/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Linalg/index.html index 6be771ca2..914bee5ff 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Linalg/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_operator_sig.Sig.Linalg)

                                    Module Sig.Linalg

                                    val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                    TODO

                                    val svd : +Linalg (owl-base.Owl_computation_operator_sig.Sig.Linalg)

                                    Module Sig.Linalg

                                    inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

                                    logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

                                    val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                    chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

                                    • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

                                    qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

                                    lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

                                    svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

                                    • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
                                    val lyapunov : + Symbol.Shape.Type.arr

                                    sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

                                    val discrete_lyapunov : + Symbol.Shape.Type.arr

                                    lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

                                    val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                    TODO

                                    val linsolve : + Symbol.Shape.Type.arr

                                    discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

                                    • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
                                    val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                    TODO

                                    linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

                                    • trans specifies whether to transpose the matrix A.
                                    • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

                                    care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

                                    • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                    + Symbol.Shape.Type.arr

                                    dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

                                    • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                    diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Mat/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Mat/index.html index b1658e401..466015153 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Mat/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_operator_sig.Sig.Mat)

                                    Module Sig.Mat

                                    val eye : int -> Symbol.Shape.Type.arr

                                    TODO

                                    TODO

                                    TODO

                                    TODO

                                    +Mat (owl-base.Owl_computation_operator_sig.Sig.Mat)

                                    Module Sig.Mat

                                    val eye : int -> Symbol.Shape.Type.arr

                                    eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

                                    diagm ?k v creates a diagonal matrix from the array v.

                                    • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

                                    triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

                                    tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

                                    diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Scalar/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Scalar/index.html index c7b7d0cb0..e37d0b802 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Scalar/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_operator_sig.Sig.Scalar)

                                    Module Sig.Scalar

                                    val add : +Scalar (owl-base.Owl_computation_operator_sig.Sig.Scalar)

                                    Module Sig.Scalar

                                    add a b returns the sum of the scalars a and b.

                                    sub a b returns the difference of the scalars a and b.

                                    mul a b returns the product of the scalars a and b.

                                    div a b returns the quotient of the scalars a and b.

                                    val atan2 : + Symbol.Shape.Type.elt

                                    pow a b returns the scalar a raised to the power of b.

                                    + Symbol.Shape.Type.elt

                                    atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                                    abs a returns the absolute value of the scalar a.

                                    neg a returns the negation of the scalar a.

                                    sqr a returns the square of the scalar a.

                                    sqrt a returns the square root of the scalar a.

                                    exp a returns the exponential of the scalar a.

                                    log a returns the natural logarithm of the scalar a.

                                    log2 a returns the base-2 logarithm of the scalar a.

                                    log10 a returns the base-10 logarithm of the scalar a.

                                    signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                                    floor a returns the greatest integer less than or equal to the scalar a.

                                    ceil a returns the smallest integer greater than or equal to the scalar a.

                                    round a returns the nearest integer to the scalar a.

                                    sin a returns the sine of the scalar a.

                                    cos a returns the cosine of the scalar a.

                                    tan a returns the tangent of the scalar a.

                                    sinh a returns the hyperbolic sine of the scalar a.

                                    cosh a returns the hyperbolic cosine of the scalar a.

                                    tanh a returns the hyperbolic tangent of the scalar a.

                                    asin a returns the arcsine of the scalar a.

                                    acos a returns the arccosine of the scalar a.

                                    atan a returns the arctangent of the scalar a.

                                    asinh a returns the inverse hyperbolic sine of the scalar a.

                                    acosh a returns the inverse hyperbolic cosine of the scalar a.

                                    atanh a returns the inverse hyperbolic tangent of the scalar a.

                                    relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                                    dawsn a returns Dawson's function of the scalar a.

                                    sigmoid a returns the sigmoid function of the scalar a.

                                    diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Linalg/index.html index a19c34fd4..dfeee80c3 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device.A.Linalg)

                                    Module A.Linalg

                                    val inv : arr -> arr
                                    val logdet : arr -> elt
                                    val chol : ?upper:bool -> arr -> arr
                                    val svd : ?thin:bool -> arr -> arr * arr * arr
                                    val qr : arr -> arr * arr
                                    val lq : arr -> arr * arr
                                    val sylvester : arr -> arr -> arr -> arr
                                    val lyapunov : arr -> arr -> arr
                                    val discrete_lyapunov : +Linalg (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device.A.Linalg)

                                    Module A.Linalg

                                    val inv : arr -> arr
                                    val logdet : arr -> elt
                                    val chol : ?upper:bool -> arr -> arr
                                    val svd : ?thin:bool -> arr -> arr * arr * arr
                                    val qr : arr -> arr * arr
                                    val lq : arr -> arr * arr
                                    val sylvester : arr -> arr -> arr -> arr
                                    val lyapunov : arr -> arr -> arr
                                    val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Mat/index.html index 1ffc4de80..3e44b437d 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device.A.Mat)

                                    Module A.Mat

                                    val diagm : ?k:int -> arr -> arr
                                    val triu : ?k:int -> arr -> arr
                                    val tril : ?k:int -> arr -> arr
                                    val eye : int -> arr
                                    +Mat (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device.A.Mat)

                                    Module A.Mat

                                    val diagm : ?k:int -> arr -> arr
                                    val triu : ?k:int -> arr -> arr
                                    val tril : ?k:int -> arr -> arr
                                    val eye : int -> arr
                                    diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Scalar/index.html index a6469d6d9..f6f2c42ae 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device.A.Scalar)

                                    Module A.Scalar

                                    val add : elt -> elt -> elt
                                    val sub : elt -> elt -> elt
                                    val mul : elt -> elt -> elt
                                    val div : elt -> elt -> elt
                                    val pow : elt -> elt -> elt
                                    val atan2 : elt -> elt -> elt
                                    val abs : elt -> elt
                                    val neg : elt -> elt
                                    val sqr : elt -> elt
                                    val sqrt : elt -> elt
                                    val exp : elt -> elt
                                    val log : elt -> elt
                                    val log2 : elt -> elt
                                    val log10 : elt -> elt
                                    val signum : elt -> elt
                                    val floor : elt -> elt
                                    val ceil : elt -> elt
                                    val round : elt -> elt
                                    val sin : elt -> elt
                                    val cos : elt -> elt
                                    val tan : elt -> elt
                                    val sinh : elt -> elt
                                    val cosh : elt -> elt
                                    val tanh : elt -> elt
                                    val asin : elt -> elt
                                    val acos : elt -> elt
                                    val atan : elt -> elt
                                    val asinh : elt -> elt
                                    val acosh : elt -> elt
                                    val atanh : elt -> elt
                                    val relu : elt -> elt
                                    val dawsn : elt -> elt
                                    val sigmoid : elt -> elt
                                    +Scalar (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device.A.Scalar)

                                    Module A.Scalar

                                    val add : elt -> elt -> elt
                                    val sub : elt -> elt -> elt
                                    val mul : elt -> elt -> elt
                                    val div : elt -> elt -> elt
                                    val pow : elt -> elt -> elt
                                    val atan2 : elt -> elt -> elt
                                    val abs : elt -> elt
                                    val neg : elt -> elt
                                    val sqr : elt -> elt
                                    val sqrt : elt -> elt
                                    val exp : elt -> elt
                                    val log : elt -> elt
                                    val log2 : elt -> elt
                                    val log10 : elt -> elt
                                    val signum : elt -> elt
                                    val floor : elt -> elt
                                    val ceil : elt -> elt
                                    val round : elt -> elt
                                    val sin : elt -> elt
                                    val cos : elt -> elt
                                    val tan : elt -> elt
                                    val sinh : elt -> elt
                                    val cosh : elt -> elt
                                    val tanh : elt -> elt
                                    val asin : elt -> elt
                                    val acos : elt -> elt
                                    val atan : elt -> elt
                                    val asinh : elt -> elt
                                    val acosh : elt -> elt
                                    val atanh : elt -> elt
                                    val relu : elt -> elt
                                    val dawsn : elt -> elt
                                    val sigmoid : elt -> elt
                                    diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/index.html index 39bbf872a..b3cdb432e 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device.A)

                                    Module Device.A

                                    include Owl_types_ndarray_algodiff.Sig
                                    include Owl_types_ndarray_eltcmp.Sig
                                    include Owl_types_ndarray_basic.Sig
                                    type arr
                                    type elt
                                    val empty : int array -> arr
                                    val zeros : int array -> arr
                                    val ones : int array -> arr
                                    val create : int array -> elt -> arr
                                    val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                    val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                    val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                    val bernoulli : ?p:elt -> int array -> arr
                                    val init : int array -> (int -> elt) -> arr
                                    val init_nd : int array -> (int array -> elt) -> arr
                                    val shape : arr -> int array
                                    val numel : arr -> int
                                    val get : arr -> int array -> elt
                                    val set : arr -> int array -> elt -> unit
                                    val get_slice : int list list -> arr -> arr
                                    val set_slice : int list list -> arr -> arr -> unit
                                    val get_fancy : Owl_types_common.index list -> arr -> arr
                                    val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                    val copy : arr -> arr
                                    val copy_ : out:arr -> arr -> unit
                                    val reset : arr -> unit
                                    val reshape : arr -> int array -> arr
                                    val reverse : arr -> arr
                                    val tile : arr -> int array -> arr
                                    val repeat : arr -> int array -> arr
                                    val concatenate : ?axis:int -> arr array -> arr
                                    val stack : ?axis:int -> arr array -> arr
                                    val split : ?axis:int -> int array -> arr -> arr array
                                    val expand : ?hi:bool -> arr -> int -> arr
                                    val squeeze : ?axis:int array -> arr -> arr
                                    val draw : ?axis:int -> arr -> int -> arr * int array
                                    val map : (elt -> elt) -> arr -> arr
                                    val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                    val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                    val one_hot : int -> arr -> arr
                                    val pad : ?v:elt -> int list list -> arr -> arr
                                    val print : +A (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device.A)

                                    Module Device.A

                                    include Owl_types_ndarray_algodiff.Sig
                                    include Owl_types_ndarray_eltcmp.Sig
                                    include Owl_types_ndarray_basic.Sig
                                    type arr
                                    type elt
                                    val empty : int array -> arr
                                    val zeros : int array -> arr
                                    val ones : int array -> arr
                                    val create : int array -> elt -> arr
                                    val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                    val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                    val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                    val bernoulli : ?p:elt -> int array -> arr
                                    val init : int array -> (int -> elt) -> arr
                                    val init_nd : int array -> (int array -> elt) -> arr
                                    val shape : arr -> int array
                                    val numel : arr -> int
                                    val get : arr -> int array -> elt
                                    val set : arr -> int array -> elt -> unit
                                    val get_slice : int list list -> arr -> arr
                                    val set_slice : int list list -> arr -> arr -> unit
                                    val get_fancy : Owl_types_common.index list -> arr -> arr
                                    val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                    val copy : arr -> arr
                                    val copy_ : out:arr -> arr -> unit
                                    val reset : arr -> unit
                                    val reshape : arr -> int array -> arr
                                    val reverse : arr -> arr
                                    val tile : arr -> int array -> arr
                                    val repeat : arr -> int array -> arr
                                    val concatenate : ?axis:int -> arr array -> arr
                                    val stack : ?axis:int -> arr array -> arr
                                    val split : ?axis:int -> int array -> arr -> arr array
                                    val expand : ?hi:bool -> arr -> int -> arr
                                    val squeeze : ?axis:int array -> arr -> arr
                                    val draw : ?axis:int -> arr -> int -> arr * int array
                                    val map : (elt -> elt) -> arr -> arr
                                    val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                    val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                    val one_hot : int -> arr -> arr
                                    val pad : ?v:elt -> int list list -> arr -> arr
                                    val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/index.html index dc5656152..1e80f1fa6 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device)

                                    Module Type.Device

                                    Type definition
                                    type device

                                    TODO

                                    type value

                                    TODO

                                    Core functions
                                    val make_device : unit -> device

                                    TODO

                                    val arr_to_value : A.arr -> value

                                    TODO

                                    val value_to_arr : value -> A.arr

                                    TODO

                                    val elt_to_value : A.elt -> value

                                    TODO

                                    val value_to_elt : value -> A.elt

                                    TODO

                                    val value_to_float : value -> float

                                    TODO

                                    val is_arr : value -> bool

                                    TODO

                                    val is_elt : value -> bool

                                    TODO

                                    +Device (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type.Device)

                                    Module Type.Device

                                    Type definition
                                    type device

                                    TODO

                                    type value

                                    TODO

                                    Core functions
                                    val make_device : unit -> device

                                    TODO

                                    val arr_to_value : A.arr -> value

                                    TODO

                                    val value_to_arr : value -> A.arr

                                    TODO

                                    val elt_to_value : A.elt -> value

                                    TODO

                                    val value_to_elt : value -> A.elt

                                    TODO

                                    val value_to_float : value -> float

                                    TODO

                                    val is_arr : value -> bool

                                    TODO

                                    val is_elt : value -> bool

                                    TODO

                                    diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/index.html index 948745469..893d8d070 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type)

                                    Module Shape.Type

                                    Type definition
                                    type state =
                                    1. | Valid
                                    2. | Invalid
                                      (*

                                      TODO

                                      *)

                                    TODO

                                    and block = {
                                    1. size : int;
                                    2. block_id : int;
                                    3. mutable active : t option;
                                    4. mutable memory : Device.value;
                                    5. mutable nodes : t list;
                                    }

                                    block type keeps a reference to a block of memory and to the nodes sharing that block.

                                    and attr = {
                                    1. mutable op : op;
                                    2. mutable freeze : bool;
                                    3. mutable reuse : bool;
                                    4. mutable state : state;
                                    5. mutable shape : int array option array;
                                    6. mutable value : Device.value array;
                                    7. mutable block : block array option;
                                    }

                                    TODO

                                    and arr =
                                    1. | Arr of t
                                    and elt =
                                    1. | Elt of t
                                    and op =
                                    1. | Noop
                                    2. | Var
                                    3. | Const
                                    4. | Empty of int array
                                    5. | Zeros of int array
                                    6. | Ones of int array
                                    7. | Create of int array
                                    8. | Sequential of int array
                                    9. | Uniform of int array
                                    10. | Gaussian of int array
                                    11. | Bernoulli of int array
                                    12. | Init of int array * int -> elt
                                    13. | Get of int array
                                    14. | Set of int array
                                    15. | GetSlice of int list list
                                    16. | SetSlice of int list list
                                    17. | GetFancy of Owl_types_common.index list
                                    18. | SetFancy of Owl_types_common.index list
                                    19. | Copy
                                    20. | Reset
                                    21. | Reshape of int array
                                    22. | Reverse
                                    23. | Tile of int array
                                    24. | Repeat of int array
                                    25. | Pad of elt * int list list
                                    26. | Concatenate of int
                                    27. | Stack of int
                                    28. | Split of int * int array
                                    29. | Draw of int * int
                                    30. | Map of elt -> elt
                                    31. | Fold of int * elt -> elt -> elt
                                    32. | Scan of int * elt -> elt -> elt
                                    33. | OneHot of int
                                    34. | OfArray of int array
                                    35. | Delay of Device.A.arr -> Device.A.arr
                                    36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                    37. | LazyPrint of int option +Type (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape.Type)

                                      Module Shape.Type

                                      Type definition
                                      type state =
                                      1. | Valid
                                      2. | Invalid
                                        (*

                                        TODO

                                        *)

                                      TODO

                                      and block = {
                                      1. size : int;
                                      2. block_id : int;
                                      3. mutable active : t option;
                                      4. mutable memory : Device.value;
                                      5. mutable nodes : t list;
                                      }

                                      block type keeps a reference to a block of memory and to the nodes sharing that block.

                                      and attr = {
                                      1. mutable op : op;
                                      2. mutable freeze : bool;
                                      3. mutable reuse : bool;
                                      4. mutable state : state;
                                      5. mutable shape : int array option array;
                                      6. mutable value : Device.value array;
                                      7. mutable block : block array option;
                                      }

                                      TODO

                                      and arr =
                                      1. | Arr of t
                                      and elt =
                                      1. | Elt of t
                                      and op =
                                      1. | Noop
                                      2. | Var
                                      3. | Const
                                      4. | Empty of int array
                                      5. | Zeros of int array
                                      6. | Ones of int array
                                      7. | Create of int array
                                      8. | Sequential of int array
                                      9. | Uniform of int array
                                      10. | Gaussian of int array
                                      11. | Bernoulli of int array
                                      12. | Init of int array * int -> elt
                                      13. | Get of int array
                                      14. | Set of int array
                                      15. | GetSlice of int list list
                                      16. | SetSlice of int list list
                                      17. | GetFancy of Owl_types_common.index list
                                      18. | SetFancy of Owl_types_common.index list
                                      19. | Copy
                                      20. | Reset
                                      21. | Reshape of int array
                                      22. | Reverse
                                      23. | Tile of int array
                                      24. | Repeat of int array
                                      25. | Pad of elt * int list list
                                      26. | Concatenate of int
                                      27. | Stack of int
                                      28. | Split of int * int array
                                      29. | Draw of int * int
                                      30. | Map of elt -> elt
                                      31. | Fold of int * elt -> elt -> elt
                                      32. | Scan of int * elt -> elt -> elt
                                      33. | OneHot of int
                                      34. | OfArray of int array
                                      35. | Delay of Device.A.arr -> Device.A.arr
                                      36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                      37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                      38. | Abs
                                      39. | Neg
                                      40. | Floor
                                      41. | Ceil
                                      42. | Round
                                      43. | Sqr
                                      44. | Sqrt
                                      45. | Log
                                      46. | Log2
                                      47. | Log10
                                      48. | Exp
                                      49. | Sin
                                      50. | Cos
                                      51. | Tan
                                      52. | Sinh
                                      53. | Cosh
                                      54. | Tanh
                                      55. | Asin
                                      56. | Acos
                                      57. | Atan
                                      58. | Asinh
                                      59. | Acosh
                                      60. | Atanh
                                      61. | Min of bool * int
                                      62. | Max of bool * int
                                      63. | Sum of bool * int
                                      64. | SumReduce of int array
                                      65. | Signum
                                      66. | Sigmoid
                                      67. | Relu
                                      68. | Dawsn
                                      69. | Min'
                                      70. | Max'
                                      71. | Sum'
                                      72. | LogSumExp'
                                      73. | LogSumExp of bool * int
                                      74. | L1norm'
                                      75. | L2norm'
                                      76. | L2NormSqr'
                                      77. | ClipByValue
                                      78. | ClipByL2norm
                                      79. | Pow
                                      80. | ScalarPow
                                      81. | PowScalar
                                      82. | Atan2
                                      83. | ScalarAtan2
                                      84. | Atan2Scalar
                                      85. | Hypot
                                      86. | Min2
                                      87. | Max2
                                      88. | Add
                                      89. | Sub
                                      90. | Mul
                                      91. | Div
                                      92. | AddScalar
                                      93. | SubScalar
                                      94. | MulScalar
                                      95. | DivScalar
                                      96. | ScalarAdd
                                      97. | ScalarSub
                                      98. | ScalarMul
                                      99. | ScalarDiv
                                      100. | FMA
                                      101. | EltEqual
                                      102. | EltNotEqual
                                      103. | EltLess
                                      104. | EltGreater
                                      105. | EltLessEqual
                                      106. | EltGreaterEqual
                                      107. | EltEqualScalar
                                      108. | EltNotEqualScalar
                                      109. | EltLessScalar
                                      110. | EltGreaterScalar
                                      111. | EltLessEqualScalar
                                      112. | EltGreaterEqualScalar
                                      113. | Conv1d of Owl_types_common.padding * int array
                                      114. | Conv2d of Owl_types_common.padding * int array
                                      115. | Conv3d of Owl_types_common.padding * int array
                                      116. | TransposeConv1d of Owl_types_common.padding * int array
                                      117. | TransposeConv2d of Owl_types_common.padding * int array
                                      118. | TransposeConv3d of Owl_types_common.padding * int array
                                      119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                      120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                      121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                      122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                      123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                      124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                      125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                      126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                      127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                      128. | UpSampling2d of int array
                                      129. | Conv1dBackwardInput of int array
                                      130. | Conv1dBackwardKernel of int array
                                      131. | Conv2dBackwardInput of int array
                                      132. | Conv2dBackwardKernel of int array
                                      133. | Conv3dBackwardInput of int array
                                      134. | Conv3dBackwardKernel of int array
                                      135. | TransposeConv1dBackwardInput of int array
                                      136. | TransposeConv1dBackwardKernel of int array
                                      137. | TransposeConv2dBackwardInput of int array
                                      138. | TransposeConv2dBackwardKernel of int array
                                      139. | TransposeConv3dBackwardInput of int array
                                      140. | TransposeConv3dBackwardKernel of int array
                                      141. | DilatedConv1dBackwardInput of int array * int array
                                      142. | DilatedConv1dBackwardKernel of int array * int array
                                      143. | DilatedConv2dBackwardInput of int array * int array
                                      144. | DilatedConv2dBackwardKernel of int array * int array
                                      145. | DilatedConv3dBackwardInput of int array * int array
                                      146. | DilatedConv3dBackwardKernel of int array * int array
                                      147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                      148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                      149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                      150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                      151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                      152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                      153. | UpSampling2dBackward of int array
                                      154. | RowNum
                                      155. | ColNum
                                      156. | Row
                                      157. | Rows of int array
                                      158. | CopyRowTo
                                      159. | CopyColTo
                                      160. | Dot of bool * bool * elt * elt
                                      161. | Inv
                                      162. | Trace
                                      163. | Transpose of int array
                                      164. | ToRows
                                      165. | OfRows
                                      166. | Scalar_Add
                                      167. | Scalar_Sub
                                      168. | Scalar_Mul
                                      169. | Scalar_Div
                                      170. | Scalar_Pow
                                      171. | Scalar_Atan2
                                      172. | Scalar_Abs
                                      173. | Scalar_Neg
                                      174. | Scalar_Sqr
                                      175. | Scalar_Sqrt
                                      176. | Scalar_Exp
                                      177. | Scalar_Log
                                      178. | Scalar_Log2
                                      179. | Scalar_Log10
                                      180. | Scalar_Signum
                                      181. | Scalar_Floor
                                      182. | Scalar_Ceil
                                      183. | Scalar_Round
                                      184. | Scalar_Sin
                                      185. | Scalar_Cos
                                      186. | Scalar_Tan
                                      187. | Scalar_Sinh
                                      188. | Scalar_Cosh
                                      189. | Scalar_Tanh
                                      190. | Scalar_Asin
                                      191. | Scalar_Acos
                                      192. | Scalar_Atan
                                      193. | Scalar_Asinh
                                      194. | Scalar_Acosh
                                      195. | Scalar_Atanh
                                      196. | Scalar_Relu
                                      197. | Scalar_Dawsn
                                      198. | Scalar_Sigmoid
                                      199. | Fused_Adagrad of float * float
                                        (*

                                        TODO

                                        *)
                                      diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/index.html index 2a9e6a439..dd945076f 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape)

                                      Module Symbol.Shape

                                      Core functions
                                      val infer_shape : +Shape (owl-base.Owl_computation_operator_sig.Sig.Symbol.Shape)

                                      Module Symbol.Shape

                                      Core functions
                                      val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                      TODO

                                      diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/index.html index 7f5f85199..e0e2d61b9 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_operator_sig.Sig.Symbol)

                                      Module Sig.Symbol

                                      Core functions
                                      val op_to_str : Shape.Type.op -> string

                                      TODO

                                      val is_random_variable : Shape.Type.op -> bool

                                      TODO

                                      val refnum : 'a Owl_graph.node -> int

                                      TODO

                                      val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                      TODO

                                      val node_numel : Shape.Type.attr Owl_graph.node -> int

                                      TODO

                                      val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                      TODO

                                      val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                      TODO

                                      val shape_to_str : int array option array -> string

                                      TODO

                                      val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                      TODO

                                      val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                      TODO

                                      val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                      TODO

                                      val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                      TODO

                                      val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                      TODO

                                      val make_node : +Symbol (owl-base.Owl_computation_operator_sig.Sig.Symbol)

                                      Module Sig.Symbol

                                      Core functions
                                      val op_to_str : Shape.Type.op -> string

                                      TODO

                                      val is_random_variable : Shape.Type.op -> bool

                                      TODO

                                      val refnum : 'a Owl_graph.node -> int

                                      TODO

                                      val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                      TODO

                                      val node_numel : Shape.Type.attr Owl_graph.node -> int

                                      TODO

                                      val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                      TODO

                                      val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                      TODO

                                      val shape_to_str : int array option array -> string

                                      TODO

                                      val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                      TODO

                                      val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                      TODO

                                      val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                      TODO

                                      val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                      TODO

                                      val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                      TODO

                                      val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/index.html b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/index.html index 2088de9d3..2ff5a80ce 100644 --- a/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_computation_operator_sig/module-type-Sig/index.html @@ -1,58 +1,58 @@ -Sig (owl-base.Owl_computation_operator_sig.Sig)

                                      Module type Owl_computation_operator_sig.Sig

                                      Vectorised functions
                                      val empty : int array -> Symbol.Shape.Type.arr

                                      TODO

                                      val zeros : int array -> Symbol.Shape.Type.arr

                                      TODO

                                      val ones : int array -> Symbol.Shape.Type.arr

                                      TODO

                                      val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                      TODO

                                      val sequential : +Sig (owl-base.Owl_computation_operator_sig.Sig)

                                      Module type Owl_computation_operator_sig.Sig

                                      Vectorised functions

                                      noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                                      val empty : int array -> Symbol.Shape.Type.arr

                                      empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                                      val zeros : int array -> Symbol.Shape.Type.arr

                                      zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                                      val ones : int array -> Symbol.Shape.Type.arr

                                      ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                                      val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                      create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                                      val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val uniform : + Symbol.Shape.Type.arr

                                      sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                                      val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val gaussian : + Symbol.Shape.Type.arr

                                      uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                                      val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                      TODO

                                      val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                      TODO

                                      val init_nd : + Symbol.Shape.Type.arr

                                      gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                                      val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                      bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                                      val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                      init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                                      val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                                      TODO

                                      val shape : Symbol.Shape.Type.arr -> int array

                                      TODO

                                      val numel : Symbol.Shape.Type.arr -> int

                                      TODO

                                      TODO

                                      val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                      TODO

                                      val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                      TODO

                                      val set_slice : + Symbol.Shape.Type.arr

                                      init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                                      val shape : Symbol.Shape.Type.arr -> int array

                                      shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                                      val numel : Symbol.Shape.Type.arr -> int

                                      numel arr returns the total number of elements in the array arr.

                                      get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                                      val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                      set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                                      val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                      get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                                      val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                      TODO

                                      val get_fancy : + unit

                                      set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                                      val set_fancy : + Symbol.Shape.Type.arr

                                      get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                                      val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                      TODO

                                      val copy_ : out:'a -> 'b -> 'c

                                      TODO

                                      val reset : Symbol.Shape.Type.arr -> unit

                                      TODO

                                      val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      TODO

                                      val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      TODO

                                      val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      TODO

                                      val pad : + unit

                                      set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                                      copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                                      val copy_ : out:'a -> 'b -> 'c

                                      copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                                      val reset : Symbol.Shape.Type.arr -> unit

                                      reset arr sets all elements of the array arr to zero.

                                      val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                                      reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                                      val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                                      val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                                      TODO

                                      val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                      TODO

                                      val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                      TODO

                                      val concatenate : + Symbol.Shape.Type.arr

                                      pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                                      val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                      expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                                      val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                      squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                                      val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                      TODO

                                      val concat : + Symbol.Shape.Type.arr

                                      concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                                      val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                      stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                                      val split : ?axis:int -> 'a -> 'b -> 'c

                                      TODO

                                      concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                                      val split : ?axis:int -> 'a -> 'b -> 'c

                                      split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                                      • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                                      val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                                      TODO

                                      val map : + Symbol.Shape.Type.arr * 'a array

                                      draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                                      map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                                      fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                                      TODO

                                      val delay : + Symbol.Shape.Type.arr

                                      scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                                      one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                                      delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                                      val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                      val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                      TODO

                                      lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                      val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                      print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                                      • max_row is an optional parameter specifying the maximum number of rows to print.
                                      • max_col is an optional parameter specifying the maximum number of columns to print.
                                      • header is an optional parameter to include a header in the output.
                                      • fmt is an optional parameter to specify the format of the output.

                                      abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                                      neg arr negates each element in the array arr. Returns a new array with each element negated.

                                      floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                                      ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                                      round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                                      sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                                      sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                                      log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                                      log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                                      log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                                      exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                                      sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                                      cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                                      tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                                      sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                                      cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                                      tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                                      asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                                      acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                                      atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                                      asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                                      acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                                      atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                                      val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                                      • axis specifies the axis along which to compute the minimum.
                                      • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                                      val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                                      • axis specifies the axis along which to compute the maximum.
                                      • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                                      val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val sum_reduce : + Symbol.Shape.Type.arr

                                      sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                                      • axis specifies the axis along which to compute the sum.
                                      • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                                      val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val log_sum_exp : + Symbol.Shape.Type.arr

                                      sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                                      • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                                      signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                                      sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                                      relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                                      dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                                      min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                                      max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                                      sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                                      log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                                      val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val clip_by_value : + Symbol.Shape.Type.arr

                                      log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                                      • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                                      • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                                      l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                                      l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                                      l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                                      val clip_by_l2norm : + Symbol.Shape.Type.arr

                                      clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                                      • amin specifies the minimum value to clip to.
                                      • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                                      clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                                      val scalar_pow : + Symbol.Shape.Type.arr

                                      pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                                      val pow_scalar : + Symbol.Shape.Type.arr

                                      scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                                      val atan2 : + Symbol.Shape.Type.arr

                                      pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                                      val scalar_atan2 : + Symbol.Shape.Type.arr

                                      atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                                      val atan2_scalar : + Symbol.Shape.Type.arr

                                      scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                                      val hypot : + Symbol.Shape.Type.arr

                                      atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                                      hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                                      min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                                      max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                                      add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                                      sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                                      mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                                      val add_scalar : + Symbol.Shape.Type.arr

                                      div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                                      val sub_scalar : + Symbol.Shape.Type.arr

                                      add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                      val mul_scalar : + Symbol.Shape.Type.arr

                                      sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                                      val div_scalar : + Symbol.Shape.Type.arr

                                      mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                      val scalar_add : + Symbol.Shape.Type.arr

                                      div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                      val scalar_sub : + Symbol.Shape.Type.arr

                                      scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                      val scalar_mul : + Symbol.Shape.Type.arr

                                      scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                                      val scalar_div : + Symbol.Shape.Type.arr

                                      scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                      scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                                      val elt_equal : + Symbol.Shape.Type.arr

                                      fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                                      val elt_not_equal : + Symbol.Shape.Type.arr

                                      elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                                      val elt_less : + Symbol.Shape.Type.arr

                                      elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                                      val elt_greater : + Symbol.Shape.Type.arr

                                      elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                                      val elt_less_equal : + Symbol.Shape.Type.arr

                                      elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                                      val elt_greater_equal : + Symbol.Shape.Type.arr

                                      elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                                      val elt_equal_scalar : + Symbol.Shape.Type.arr

                                      elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                                      val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                                      elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                                      val elt_less_scalar : + Symbol.Shape.Type.arr

                                      elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                                      val elt_greater_scalar : + Symbol.Shape.Type.arr

                                      elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                                      val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                                      elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                                      TODO

                                      val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                                      elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                                      TODO

                                      val conv1d : + Symbol.Shape.Type.arr

                                      elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                                      val conv2d : + Symbol.Shape.Type.arr

                                      conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                                      • padding specifies the padding strategy (default is "valid").
                                      • strides specifies the stride length. Returns a new array with the result of the convolution.
                                      val conv3d : + Symbol.Shape.Type.arr

                                      conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                                      • padding specifies the padding strategy (default is "valid").
                                      • strides specifies the stride length. Returns a new array with the result of the convolution.
                                      val transpose_conv1d : + Symbol.Shape.Type.arr

                                      conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                                      • padding specifies the padding strategy (default is "valid").
                                      • strides specifies the stride length. Returns a new array with the result of the convolution.
                                      val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val transpose_conv2d : + Symbol.Shape.Type.arr

                                      transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                      • padding specifies the padding strategy (default is "valid").
                                      • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                      val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val transpose_conv3d : + Symbol.Shape.Type.arr

                                      transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                      • padding specifies the padding strategy (default is "valid").
                                      • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                      val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val dilated_conv1d : + Symbol.Shape.Type.arr

                                      transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                      • padding specifies the padding strategy (default is "valid").
                                      • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                      val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val dilated_conv2d : + Symbol.Shape.Type.arr

                                      dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                                      • padding specifies the padding strategy (default is "valid").
                                      • strides specifies the stride length.
                                      • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                      val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val dilated_conv3d : + Symbol.Shape.Type.arr

                                      dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                                      • padding specifies the padding strategy (default is "valid").
                                      • strides specifies the stride length.
                                      • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                      val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val max_pool1d : + Symbol.Shape.Type.arr

                                      dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                                      • padding specifies the padding strategy (default is "valid").
                                      • strides specifies the stride length.
                                      • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                      val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val max_pool2d : + Symbol.Shape.Type.arr

                                      max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                                      • padding specifies the padding strategy (default is "valid").
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                      val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val max_pool3d : + Symbol.Shape.Type.arr

                                      max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                                      • padding specifies the padding strategy (default is "valid").
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                      val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val avg_pool1d : + Symbol.Shape.Type.arr

                                      max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                                      • padding specifies the padding strategy (default is "valid").
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                      val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val avg_pool2d : + Symbol.Shape.Type.arr

                                      avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                                      • padding specifies the padding strategy (default is "valid").
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                      val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val avg_pool3d : + Symbol.Shape.Type.arr

                                      avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                                      • padding specifies the padding strategy (default is "valid").
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                      val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      TODO

                                      val conv1d_backward_input : + Symbol.Shape.Type.arr

                                      avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                                      • padding specifies the padding strategy (default is "valid").
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                      val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      upsampling2d input size performs a 2-dimensional upsampling on the input array.

                                      • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                                      TODO

                                      val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                      conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                                      • input is the original input array.
                                      • kernel is the convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                      val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val conv2d_backward_input : + Symbol.Shape.Type.arr

                                      conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                                      • input is the original input array.
                                      • kernel is the convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                      TODO

                                      val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                      conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                                      • input is the original input array.
                                      • kernel is the convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                      val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val conv3d_backward_input : + Symbol.Shape.Type.arr

                                      conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                                      • input is the original input array.
                                      • kernel is the convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                      TODO

                                      val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                      conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                                      • input is the original input array.
                                      • kernel is the convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                      val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                                      conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                                      • input is the original input array.
                                      • kernel is the convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                                      val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                      transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                                      • input is the original input array.
                                      • kernel is the transposed convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                      val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                                      transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                                      • input is the original input array.
                                      • kernel is the transposed convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                      val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                      transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                                      • input is the original input array.
                                      • kernel is the transposed convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                      val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                                      transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                                      • input is the original input array.
                                      • kernel is the transposed convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                      val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                      transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                                      • input is the original input array.
                                      • kernel is the transposed convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                      val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                                      transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                                      • input is the original input array.
                                      • kernel is the transposed convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                      val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                      dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                                      • input is the original input array.
                                      • kernel is the dilated convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • dilations specifies the dilation rate.
                                      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                      val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                                      dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                                      • input is the original input array.
                                      • kernel is the dilated convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • dilations specifies the dilation rate.
                                      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                      val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                      dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                                      • input is the original input array.
                                      • kernel is the dilated convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • dilations specifies the dilation rate.
                                      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                      val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                                      dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                                      • input is the original input array.
                                      • kernel is the dilated convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • dilations specifies the dilation rate.
                                      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                      val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                      dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                                      • input is the original input array.
                                      • kernel is the dilated convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • dilations specifies the dilation rate.
                                      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                      val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val max_pool1d_backward : + Symbol.Shape.Type.arr

                                      dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                                      • input is the original input array.
                                      • kernel is the dilated convolutional kernel used during the forward pass.
                                      • strides specifies the stride length.
                                      • dilations specifies the dilation rate.
                                      • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                      val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val max_pool2d_backward : + Symbol.Shape.Type.arr

                                      max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                                      • padding specifies the padding strategy used during the forward pass.
                                      • input is the original input array.
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                      val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val max_pool3d_backward : + Symbol.Shape.Type.arr

                                      max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                                      • padding specifies the padding strategy used during the forward pass.
                                      • input is the original input array.
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                      val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val avg_pool1d_backward : + Symbol.Shape.Type.arr

                                      max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                                      • padding specifies the padding strategy used during the forward pass.
                                      • input is the original input array.
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                      val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val avg_pool2d_backward : + Symbol.Shape.Type.arr

                                      avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                                      • padding specifies the padding strategy used during the forward pass.
                                      • input is the original input array.
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                      val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val avg_pool3d_backward : + Symbol.Shape.Type.arr

                                      avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                                      • padding specifies the padding strategy used during the forward pass.
                                      • input is the original input array.
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                      val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val upsampling2d_backward : + Symbol.Shape.Type.arr

                                      avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                                      • padding specifies the padding strategy used during the forward pass.
                                      • input is the original input array.
                                      • pool_size specifies the size of the pooling window.
                                      • strides specifies the stride length.
                                      • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                      val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val row_num : Symbol.Shape.Type.arr -> int

                                      TODO

                                      val col_num : Symbol.Shape.Type.arr -> int

                                      TODO

                                      val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      TODO

                                      val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                      TODO

                                      val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                      TODO

                                      TODO

                                      upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                                      • input is the original input array.
                                      • size specifies the upsampling factors for each dimension.
                                      • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                                      val row_num : Symbol.Shape.Type.arr -> int

                                      row_num arr returns the number of rows in the array arr.

                                      val col_num : Symbol.Shape.Type.arr -> int

                                      col_num arr returns the number of columns in the array arr.

                                      row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                                      val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                      rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                                      val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                      copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                                      val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                      copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                                      diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                                      trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                                      val transpose : + Symbol.Shape.Type.arr

                                      dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                                      val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val to_rows : Symbol.Shape.Type.arr -> 'a array

                                      TODO

                                      TODO

                                      val to_cols : Symbol.Shape.Type.arr -> 'a array

                                      TODO

                                      TODO

                                      val of_array : + Symbol.Shape.Type.arr

                                      transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                                      val to_rows : Symbol.Shape.Type.arr -> 'a array

                                      to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                                      of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                                      val to_cols : Symbol.Shape.Type.arr -> 'a array

                                      to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                                      of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                                      val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                                      TODO

                                      val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                      TODO

                                      val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                      TODO

                                      Scalar functions
                                      module Scalar : sig ... end
                                      module Mat : sig ... end
                                      module Linalg : sig ... end
                                      + Symbol.Shape.Type.arr

                                      of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                                      val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                      of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                                      val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                      to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                                      Scalar functions
                                      module Scalar : sig ... end
                                      module Mat : sig ... end
                                      module Linalg : sig ... end
                                      diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Linalg/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Linalg/index.html index ca8e7d2bc..a547573d4 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_optimiser.Make.Operator.Linalg)

                                      Module Operator.Linalg

                                      val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                      TODO

                                      val svd : +Linalg (owl-base.Owl_computation_optimiser.Make.Operator.Linalg)

                                      Module Operator.Linalg

                                      inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

                                      logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

                                      val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                      chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

                                      • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

                                      qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

                                      lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

                                      svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

                                      • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
                                      val lyapunov : + Symbol.Shape.Type.arr

                                      sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

                                      val discrete_lyapunov : + Symbol.Shape.Type.arr

                                      lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

                                      val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      val linsolve : + Symbol.Shape.Type.arr

                                      discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

                                      • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
                                      val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                      TODO

                                      linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

                                      • trans specifies whether to transpose the matrix A.
                                      • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

                                      care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

                                      • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                      + Symbol.Shape.Type.arr

                                      dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

                                      • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                      diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Mat/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Mat/index.html index 543c439f6..33f50b15d 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_optimiser.Make.Operator.Mat)

                                      Module Operator.Mat

                                      val eye : int -> Symbol.Shape.Type.arr

                                      TODO

                                      TODO

                                      TODO

                                      TODO

                                      +Mat (owl-base.Owl_computation_optimiser.Make.Operator.Mat)

                                      Module Operator.Mat

                                      val eye : int -> Symbol.Shape.Type.arr

                                      eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

                                      diagm ?k v creates a diagonal matrix from the array v.

                                      • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

                                      triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

                                      tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

                                      diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Scalar/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Scalar/index.html index f110d53bb..358aa934e 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_optimiser.Make.Operator.Scalar)

                                      Module Operator.Scalar

                                      val add : +Scalar (owl-base.Owl_computation_optimiser.Make.Operator.Scalar)

                                      Module Operator.Scalar

                                      add a b returns the sum of the scalars a and b.

                                      sub a b returns the difference of the scalars a and b.

                                      mul a b returns the product of the scalars a and b.

                                      div a b returns the quotient of the scalars a and b.

                                      val atan2 : + Symbol.Shape.Type.elt

                                      pow a b returns the scalar a raised to the power of b.

                                      + Symbol.Shape.Type.elt

                                      atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                                      abs a returns the absolute value of the scalar a.

                                      neg a returns the negation of the scalar a.

                                      sqr a returns the square of the scalar a.

                                      sqrt a returns the square root of the scalar a.

                                      exp a returns the exponential of the scalar a.

                                      log a returns the natural logarithm of the scalar a.

                                      log2 a returns the base-2 logarithm of the scalar a.

                                      log10 a returns the base-10 logarithm of the scalar a.

                                      signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                                      floor a returns the greatest integer less than or equal to the scalar a.

                                      ceil a returns the smallest integer greater than or equal to the scalar a.

                                      round a returns the nearest integer to the scalar a.

                                      sin a returns the sine of the scalar a.

                                      cos a returns the cosine of the scalar a.

                                      tan a returns the tangent of the scalar a.

                                      sinh a returns the hyperbolic sine of the scalar a.

                                      cosh a returns the hyperbolic cosine of the scalar a.

                                      tanh a returns the hyperbolic tangent of the scalar a.

                                      asin a returns the arcsine of the scalar a.

                                      acos a returns the arccosine of the scalar a.

                                      atan a returns the arctangent of the scalar a.

                                      asinh a returns the inverse hyperbolic sine of the scalar a.

                                      acosh a returns the inverse hyperbolic cosine of the scalar a.

                                      atanh a returns the inverse hyperbolic tangent of the scalar a.

                                      relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                                      dawsn a returns Dawson's function of the scalar a.

                                      sigmoid a returns the sigmoid function of the scalar a.

                                      diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 1afb65454..2e775fe2c 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                      Module A.Linalg

                                      val inv : arr -> arr
                                      val logdet : arr -> elt
                                      val chol : ?upper:bool -> arr -> arr
                                      val svd : ?thin:bool -> arr -> arr * arr * arr
                                      val qr : arr -> arr * arr
                                      val lq : arr -> arr * arr
                                      val sylvester : arr -> arr -> arr -> arr
                                      val lyapunov : arr -> arr -> arr
                                      val discrete_lyapunov : +Linalg (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                      Module A.Linalg

                                      val inv : arr -> arr
                                      val logdet : arr -> elt
                                      val chol : ?upper:bool -> arr -> arr
                                      val svd : ?thin:bool -> arr -> arr * arr * arr
                                      val qr : arr -> arr * arr
                                      val lq : arr -> arr * arr
                                      val sylvester : arr -> arr -> arr -> arr
                                      val lyapunov : arr -> arr -> arr
                                      val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 5b10c1379..64593c0e0 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device.A.Mat)

                                      Module A.Mat

                                      val diagm : ?k:int -> arr -> arr
                                      val triu : ?k:int -> arr -> arr
                                      val tril : ?k:int -> arr -> arr
                                      val eye : int -> arr
                                      +Mat (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device.A.Mat)

                                      Module A.Mat

                                      val diagm : ?k:int -> arr -> arr
                                      val triu : ?k:int -> arr -> arr
                                      val tril : ?k:int -> arr -> arr
                                      val eye : int -> arr
                                      diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index 716e9d21d..99965a3bf 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                      Module A.Scalar

                                      val add : elt -> elt -> elt
                                      val sub : elt -> elt -> elt
                                      val mul : elt -> elt -> elt
                                      val div : elt -> elt -> elt
                                      val pow : elt -> elt -> elt
                                      val atan2 : elt -> elt -> elt
                                      val abs : elt -> elt
                                      val neg : elt -> elt
                                      val sqr : elt -> elt
                                      val sqrt : elt -> elt
                                      val exp : elt -> elt
                                      val log : elt -> elt
                                      val log2 : elt -> elt
                                      val log10 : elt -> elt
                                      val signum : elt -> elt
                                      val floor : elt -> elt
                                      val ceil : elt -> elt
                                      val round : elt -> elt
                                      val sin : elt -> elt
                                      val cos : elt -> elt
                                      val tan : elt -> elt
                                      val sinh : elt -> elt
                                      val cosh : elt -> elt
                                      val tanh : elt -> elt
                                      val asin : elt -> elt
                                      val acos : elt -> elt
                                      val atan : elt -> elt
                                      val asinh : elt -> elt
                                      val acosh : elt -> elt
                                      val atanh : elt -> elt
                                      val relu : elt -> elt
                                      val dawsn : elt -> elt
                                      val sigmoid : elt -> elt
                                      +Scalar (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                      Module A.Scalar

                                      val add : elt -> elt -> elt
                                      val sub : elt -> elt -> elt
                                      val mul : elt -> elt -> elt
                                      val div : elt -> elt -> elt
                                      val pow : elt -> elt -> elt
                                      val atan2 : elt -> elt -> elt
                                      val abs : elt -> elt
                                      val neg : elt -> elt
                                      val sqr : elt -> elt
                                      val sqrt : elt -> elt
                                      val exp : elt -> elt
                                      val log : elt -> elt
                                      val log2 : elt -> elt
                                      val log10 : elt -> elt
                                      val signum : elt -> elt
                                      val floor : elt -> elt
                                      val ceil : elt -> elt
                                      val round : elt -> elt
                                      val sin : elt -> elt
                                      val cos : elt -> elt
                                      val tan : elt -> elt
                                      val sinh : elt -> elt
                                      val cosh : elt -> elt
                                      val tanh : elt -> elt
                                      val asin : elt -> elt
                                      val acos : elt -> elt
                                      val atan : elt -> elt
                                      val asinh : elt -> elt
                                      val acosh : elt -> elt
                                      val atanh : elt -> elt
                                      val relu : elt -> elt
                                      val dawsn : elt -> elt
                                      val sigmoid : elt -> elt
                                      diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/index.html index 7d76dd8f2..8ce61a756 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device.A)

                                      Module Device.A

                                      include Owl_types_ndarray_algodiff.Sig
                                      include Owl_types_ndarray_eltcmp.Sig
                                      include Owl_types_ndarray_basic.Sig
                                      type arr
                                      type elt
                                      val empty : int array -> arr
                                      val zeros : int array -> arr
                                      val ones : int array -> arr
                                      val create : int array -> elt -> arr
                                      val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                      val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                      val bernoulli : ?p:elt -> int array -> arr
                                      val init : int array -> (int -> elt) -> arr
                                      val init_nd : int array -> (int array -> elt) -> arr
                                      val shape : arr -> int array
                                      val numel : arr -> int
                                      val get : arr -> int array -> elt
                                      val set : arr -> int array -> elt -> unit
                                      val get_slice : int list list -> arr -> arr
                                      val set_slice : int list list -> arr -> arr -> unit
                                      val get_fancy : Owl_types_common.index list -> arr -> arr
                                      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                      val copy : arr -> arr
                                      val copy_ : out:arr -> arr -> unit
                                      val reset : arr -> unit
                                      val reshape : arr -> int array -> arr
                                      val reverse : arr -> arr
                                      val tile : arr -> int array -> arr
                                      val repeat : arr -> int array -> arr
                                      val concatenate : ?axis:int -> arr array -> arr
                                      val stack : ?axis:int -> arr array -> arr
                                      val split : ?axis:int -> int array -> arr -> arr array
                                      val expand : ?hi:bool -> arr -> int -> arr
                                      val squeeze : ?axis:int array -> arr -> arr
                                      val draw : ?axis:int -> arr -> int -> arr * int array
                                      val map : (elt -> elt) -> arr -> arr
                                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                      val one_hot : int -> arr -> arr
                                      val pad : ?v:elt -> int list list -> arr -> arr
                                      val print : +A (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device.A)

                                      Module Device.A

                                      include Owl_types_ndarray_algodiff.Sig
                                      include Owl_types_ndarray_eltcmp.Sig
                                      include Owl_types_ndarray_basic.Sig
                                      type arr
                                      type elt
                                      val empty : int array -> arr
                                      val zeros : int array -> arr
                                      val ones : int array -> arr
                                      val create : int array -> elt -> arr
                                      val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                      val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                      val bernoulli : ?p:elt -> int array -> arr
                                      val init : int array -> (int -> elt) -> arr
                                      val init_nd : int array -> (int array -> elt) -> arr
                                      val shape : arr -> int array
                                      val numel : arr -> int
                                      val get : arr -> int array -> elt
                                      val set : arr -> int array -> elt -> unit
                                      val get_slice : int list list -> arr -> arr
                                      val set_slice : int list list -> arr -> arr -> unit
                                      val get_fancy : Owl_types_common.index list -> arr -> arr
                                      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                      val copy : arr -> arr
                                      val copy_ : out:arr -> arr -> unit
                                      val reset : arr -> unit
                                      val reshape : arr -> int array -> arr
                                      val reverse : arr -> arr
                                      val tile : arr -> int array -> arr
                                      val repeat : arr -> int array -> arr
                                      val concatenate : ?axis:int -> arr array -> arr
                                      val stack : ?axis:int -> arr array -> arr
                                      val split : ?axis:int -> int array -> arr -> arr array
                                      val expand : ?hi:bool -> arr -> int -> arr
                                      val squeeze : ?axis:int array -> arr -> arr
                                      val draw : ?axis:int -> arr -> int -> arr * int array
                                      val map : (elt -> elt) -> arr -> arr
                                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                      val one_hot : int -> arr -> arr
                                      val pad : ?v:elt -> int list list -> arr -> arr
                                      val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/index.html index 65bfe4c7d..8edff486c 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device)

                                      Module Type.Device

                                      Type definition
                                      type device

                                      TODO

                                      type value

                                      TODO

                                      Core functions
                                      val make_device : unit -> device

                                      TODO

                                      val arr_to_value : A.arr -> value

                                      TODO

                                      val value_to_arr : value -> A.arr

                                      TODO

                                      val elt_to_value : A.elt -> value

                                      TODO

                                      val value_to_elt : value -> A.elt

                                      TODO

                                      val value_to_float : value -> float

                                      TODO

                                      val is_arr : value -> bool

                                      TODO

                                      val is_elt : value -> bool

                                      TODO

                                      +Device (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type.Device)

                                      Module Type.Device

                                      Type definition
                                      type device

                                      TODO

                                      type value

                                      TODO

                                      Core functions
                                      val make_device : unit -> device

                                      TODO

                                      val arr_to_value : A.arr -> value

                                      TODO

                                      val value_to_arr : value -> A.arr

                                      TODO

                                      val elt_to_value : A.elt -> value

                                      TODO

                                      val value_to_elt : value -> A.elt

                                      TODO

                                      val value_to_float : value -> float

                                      TODO

                                      val is_arr : value -> bool

                                      TODO

                                      val is_elt : value -> bool

                                      TODO

                                      diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/index.html index 07e43fff4..8c0c39ce7 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type)

                                      Module Shape.Type

                                      Type definition
                                      type state =
                                      1. | Valid
                                      2. | Invalid
                                        (*

                                        TODO

                                        *)

                                      TODO

                                      and block = {
                                      1. size : int;
                                      2. block_id : int;
                                      3. mutable active : t option;
                                      4. mutable memory : Device.value;
                                      5. mutable nodes : t list;
                                      }

                                      block type keeps a reference to a block of memory and to the nodes sharing that block.

                                      and attr = {
                                      1. mutable op : op;
                                      2. mutable freeze : bool;
                                      3. mutable reuse : bool;
                                      4. mutable state : state;
                                      5. mutable shape : int array option array;
                                      6. mutable value : Device.value array;
                                      7. mutable block : block array option;
                                      }

                                      TODO

                                      and arr =
                                      1. | Arr of t
                                      and elt =
                                      1. | Elt of t
                                      and op =
                                      1. | Noop
                                      2. | Var
                                      3. | Const
                                      4. | Empty of int array
                                      5. | Zeros of int array
                                      6. | Ones of int array
                                      7. | Create of int array
                                      8. | Sequential of int array
                                      9. | Uniform of int array
                                      10. | Gaussian of int array
                                      11. | Bernoulli of int array
                                      12. | Init of int array * int -> elt
                                      13. | Get of int array
                                      14. | Set of int array
                                      15. | GetSlice of int list list
                                      16. | SetSlice of int list list
                                      17. | GetFancy of Owl_types_common.index list
                                      18. | SetFancy of Owl_types_common.index list
                                      19. | Copy
                                      20. | Reset
                                      21. | Reshape of int array
                                      22. | Reverse
                                      23. | Tile of int array
                                      24. | Repeat of int array
                                      25. | Pad of elt * int list list
                                      26. | Concatenate of int
                                      27. | Stack of int
                                      28. | Split of int * int array
                                      29. | Draw of int * int
                                      30. | Map of elt -> elt
                                      31. | Fold of int * elt -> elt -> elt
                                      32. | Scan of int * elt -> elt -> elt
                                      33. | OneHot of int
                                      34. | OfArray of int array
                                      35. | Delay of Device.A.arr -> Device.A.arr
                                      36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                      37. | LazyPrint of int option +Type (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape.Type)

                                        Module Shape.Type

                                        Type definition
                                        type state =
                                        1. | Valid
                                        2. | Invalid
                                          (*

                                          TODO

                                          *)

                                        TODO

                                        and block = {
                                        1. size : int;
                                        2. block_id : int;
                                        3. mutable active : t option;
                                        4. mutable memory : Device.value;
                                        5. mutable nodes : t list;
                                        }

                                        block type keeps a reference to a block of memory and to the nodes sharing that block.

                                        and attr = {
                                        1. mutable op : op;
                                        2. mutable freeze : bool;
                                        3. mutable reuse : bool;
                                        4. mutable state : state;
                                        5. mutable shape : int array option array;
                                        6. mutable value : Device.value array;
                                        7. mutable block : block array option;
                                        }

                                        TODO

                                        and arr =
                                        1. | Arr of t
                                        and elt =
                                        1. | Elt of t
                                        and op =
                                        1. | Noop
                                        2. | Var
                                        3. | Const
                                        4. | Empty of int array
                                        5. | Zeros of int array
                                        6. | Ones of int array
                                        7. | Create of int array
                                        8. | Sequential of int array
                                        9. | Uniform of int array
                                        10. | Gaussian of int array
                                        11. | Bernoulli of int array
                                        12. | Init of int array * int -> elt
                                        13. | Get of int array
                                        14. | Set of int array
                                        15. | GetSlice of int list list
                                        16. | SetSlice of int list list
                                        17. | GetFancy of Owl_types_common.index list
                                        18. | SetFancy of Owl_types_common.index list
                                        19. | Copy
                                        20. | Reset
                                        21. | Reshape of int array
                                        22. | Reverse
                                        23. | Tile of int array
                                        24. | Repeat of int array
                                        25. | Pad of elt * int list list
                                        26. | Concatenate of int
                                        27. | Stack of int
                                        28. | Split of int * int array
                                        29. | Draw of int * int
                                        30. | Map of elt -> elt
                                        31. | Fold of int * elt -> elt -> elt
                                        32. | Scan of int * elt -> elt -> elt
                                        33. | OneHot of int
                                        34. | OfArray of int array
                                        35. | Delay of Device.A.arr -> Device.A.arr
                                        36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                        37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                        38. | Abs
                                        39. | Neg
                                        40. | Floor
                                        41. | Ceil
                                        42. | Round
                                        43. | Sqr
                                        44. | Sqrt
                                        45. | Log
                                        46. | Log2
                                        47. | Log10
                                        48. | Exp
                                        49. | Sin
                                        50. | Cos
                                        51. | Tan
                                        52. | Sinh
                                        53. | Cosh
                                        54. | Tanh
                                        55. | Asin
                                        56. | Acos
                                        57. | Atan
                                        58. | Asinh
                                        59. | Acosh
                                        60. | Atanh
                                        61. | Min of bool * int
                                        62. | Max of bool * int
                                        63. | Sum of bool * int
                                        64. | SumReduce of int array
                                        65. | Signum
                                        66. | Sigmoid
                                        67. | Relu
                                        68. | Dawsn
                                        69. | Min'
                                        70. | Max'
                                        71. | Sum'
                                        72. | LogSumExp'
                                        73. | LogSumExp of bool * int
                                        74. | L1norm'
                                        75. | L2norm'
                                        76. | L2NormSqr'
                                        77. | ClipByValue
                                        78. | ClipByL2norm
                                        79. | Pow
                                        80. | ScalarPow
                                        81. | PowScalar
                                        82. | Atan2
                                        83. | ScalarAtan2
                                        84. | Atan2Scalar
                                        85. | Hypot
                                        86. | Min2
                                        87. | Max2
                                        88. | Add
                                        89. | Sub
                                        90. | Mul
                                        91. | Div
                                        92. | AddScalar
                                        93. | SubScalar
                                        94. | MulScalar
                                        95. | DivScalar
                                        96. | ScalarAdd
                                        97. | ScalarSub
                                        98. | ScalarMul
                                        99. | ScalarDiv
                                        100. | FMA
                                        101. | EltEqual
                                        102. | EltNotEqual
                                        103. | EltLess
                                        104. | EltGreater
                                        105. | EltLessEqual
                                        106. | EltGreaterEqual
                                        107. | EltEqualScalar
                                        108. | EltNotEqualScalar
                                        109. | EltLessScalar
                                        110. | EltGreaterScalar
                                        111. | EltLessEqualScalar
                                        112. | EltGreaterEqualScalar
                                        113. | Conv1d of Owl_types_common.padding * int array
                                        114. | Conv2d of Owl_types_common.padding * int array
                                        115. | Conv3d of Owl_types_common.padding * int array
                                        116. | TransposeConv1d of Owl_types_common.padding * int array
                                        117. | TransposeConv2d of Owl_types_common.padding * int array
                                        118. | TransposeConv3d of Owl_types_common.padding * int array
                                        119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                        120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                        121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                        122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                        123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                        124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                        125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                        126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                        127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                        128. | UpSampling2d of int array
                                        129. | Conv1dBackwardInput of int array
                                        130. | Conv1dBackwardKernel of int array
                                        131. | Conv2dBackwardInput of int array
                                        132. | Conv2dBackwardKernel of int array
                                        133. | Conv3dBackwardInput of int array
                                        134. | Conv3dBackwardKernel of int array
                                        135. | TransposeConv1dBackwardInput of int array
                                        136. | TransposeConv1dBackwardKernel of int array
                                        137. | TransposeConv2dBackwardInput of int array
                                        138. | TransposeConv2dBackwardKernel of int array
                                        139. | TransposeConv3dBackwardInput of int array
                                        140. | TransposeConv3dBackwardKernel of int array
                                        141. | DilatedConv1dBackwardInput of int array * int array
                                        142. | DilatedConv1dBackwardKernel of int array * int array
                                        143. | DilatedConv2dBackwardInput of int array * int array
                                        144. | DilatedConv2dBackwardKernel of int array * int array
                                        145. | DilatedConv3dBackwardInput of int array * int array
                                        146. | DilatedConv3dBackwardKernel of int array * int array
                                        147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                        148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                        149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                        150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                        151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                        152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                        153. | UpSampling2dBackward of int array
                                        154. | RowNum
                                        155. | ColNum
                                        156. | Row
                                        157. | Rows of int array
                                        158. | CopyRowTo
                                        159. | CopyColTo
                                        160. | Dot of bool * bool * elt * elt
                                        161. | Inv
                                        162. | Trace
                                        163. | Transpose of int array
                                        164. | ToRows
                                        165. | OfRows
                                        166. | Scalar_Add
                                        167. | Scalar_Sub
                                        168. | Scalar_Mul
                                        169. | Scalar_Div
                                        170. | Scalar_Pow
                                        171. | Scalar_Atan2
                                        172. | Scalar_Abs
                                        173. | Scalar_Neg
                                        174. | Scalar_Sqr
                                        175. | Scalar_Sqrt
                                        176. | Scalar_Exp
                                        177. | Scalar_Log
                                        178. | Scalar_Log2
                                        179. | Scalar_Log10
                                        180. | Scalar_Signum
                                        181. | Scalar_Floor
                                        182. | Scalar_Ceil
                                        183. | Scalar_Round
                                        184. | Scalar_Sin
                                        185. | Scalar_Cos
                                        186. | Scalar_Tan
                                        187. | Scalar_Sinh
                                        188. | Scalar_Cosh
                                        189. | Scalar_Tanh
                                        190. | Scalar_Asin
                                        191. | Scalar_Acos
                                        192. | Scalar_Atan
                                        193. | Scalar_Asinh
                                        194. | Scalar_Acosh
                                        195. | Scalar_Atanh
                                        196. | Scalar_Relu
                                        197. | Scalar_Dawsn
                                        198. | Scalar_Sigmoid
                                        199. | Fused_Adagrad of float * float
                                          (*

                                          TODO

                                          *)
                                        diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/index.html index 0787effc3..145d3fc07 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape)

                                        Module Symbol.Shape

                                        Core functions
                                        val infer_shape : +Shape (owl-base.Owl_computation_optimiser.Make.Operator.Symbol.Shape)

                                        Module Symbol.Shape

                                        Core functions
                                        val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                        TODO

                                        diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/index.html index 593a67117..00ebdca45 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_optimiser.Make.Operator.Symbol)

                                        Module Operator.Symbol

                                        Core functions
                                        val op_to_str : Shape.Type.op -> string

                                        TODO

                                        val is_random_variable : Shape.Type.op -> bool

                                        TODO

                                        val refnum : 'a Owl_graph.node -> int

                                        TODO

                                        val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                        TODO

                                        val node_numel : Shape.Type.attr Owl_graph.node -> int

                                        TODO

                                        val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                        TODO

                                        val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                        TODO

                                        val shape_to_str : int array option array -> string

                                        TODO

                                        val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                        TODO

                                        val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                        TODO

                                        val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                        TODO

                                        val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                        TODO

                                        val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                        TODO

                                        val make_node : +Symbol (owl-base.Owl_computation_optimiser.Make.Operator.Symbol)

                                        Module Operator.Symbol

                                        Core functions
                                        val op_to_str : Shape.Type.op -> string

                                        TODO

                                        val is_random_variable : Shape.Type.op -> bool

                                        TODO

                                        val refnum : 'a Owl_graph.node -> int

                                        TODO

                                        val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                        TODO

                                        val node_numel : Shape.Type.attr Owl_graph.node -> int

                                        TODO

                                        val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                        TODO

                                        val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                        TODO

                                        val shape_to_str : int array option array -> string

                                        TODO

                                        val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                        TODO

                                        val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                        TODO

                                        val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                        TODO

                                        val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                        TODO

                                        val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                        TODO

                                        val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/index.html b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/index.html index 4bfc270bc..816246f91 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/argument-1-Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_optimiser.Make.Operator)

                                        Parameter Make.Operator

                                        Vectorised functions
                                        val empty : int array -> Symbol.Shape.Type.arr

                                        TODO

                                        val zeros : int array -> Symbol.Shape.Type.arr

                                        TODO

                                        val ones : int array -> Symbol.Shape.Type.arr

                                        TODO

                                        val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                        TODO

                                        val sequential : +Operator (owl-base.Owl_computation_optimiser.Make.Operator)

                                        Parameter Make.Operator

                                        Vectorised functions

                                        noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                                        val empty : int array -> Symbol.Shape.Type.arr

                                        empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                                        val zeros : int array -> Symbol.Shape.Type.arr

                                        zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                                        val ones : int array -> Symbol.Shape.Type.arr

                                        ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                                        val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                        create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                                        val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val uniform : + Symbol.Shape.Type.arr

                                        sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                                        val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val gaussian : + Symbol.Shape.Type.arr

                                        uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                                        val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                        TODO

                                        val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                        TODO

                                        val init_nd : + Symbol.Shape.Type.arr

                                        gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                                        val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                        bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                                        val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                        init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                                        val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                                        TODO

                                        val shape : Symbol.Shape.Type.arr -> int array

                                        TODO

                                        val numel : Symbol.Shape.Type.arr -> int

                                        TODO

                                        TODO

                                        val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                        TODO

                                        val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                        TODO

                                        val set_slice : + Symbol.Shape.Type.arr

                                        init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                                        val shape : Symbol.Shape.Type.arr -> int array

                                        shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                                        val numel : Symbol.Shape.Type.arr -> int

                                        numel arr returns the total number of elements in the array arr.

                                        get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                                        val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                        set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                                        val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                        get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                                        val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                        TODO

                                        val get_fancy : + unit

                                        set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                                        val set_fancy : + Symbol.Shape.Type.arr

                                        get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                                        val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                        TODO

                                        val copy_ : out:'a -> 'b -> 'c

                                        TODO

                                        val reset : Symbol.Shape.Type.arr -> unit

                                        TODO

                                        val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        TODO

                                        val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        TODO

                                        val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        TODO

                                        val pad : + unit

                                        set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                                        copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                                        val copy_ : out:'a -> 'b -> 'c

                                        copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                                        val reset : Symbol.Shape.Type.arr -> unit

                                        reset arr sets all elements of the array arr to zero.

                                        val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                                        reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                                        val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                                        val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                                        TODO

                                        val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                        TODO

                                        val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                        TODO

                                        val concatenate : + Symbol.Shape.Type.arr

                                        pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                                        val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                        expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                                        val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                        squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                                        val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                        TODO

                                        val concat : + Symbol.Shape.Type.arr

                                        concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                                        val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                        stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                                        val split : ?axis:int -> 'a -> 'b -> 'c

                                        TODO

                                        concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                                        val split : ?axis:int -> 'a -> 'b -> 'c

                                        split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                                        • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                                        val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                                        TODO

                                        val map : + Symbol.Shape.Type.arr * 'a array

                                        draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                                        map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                                        fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                                        TODO

                                        val delay : + Symbol.Shape.Type.arr

                                        scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                                        one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                                        delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                                        val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                        val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                        TODO

                                        lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                        val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                        print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                                        • max_row is an optional parameter specifying the maximum number of rows to print.
                                        • max_col is an optional parameter specifying the maximum number of columns to print.
                                        • header is an optional parameter to include a header in the output.
                                        • fmt is an optional parameter to specify the format of the output.

                                        abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                                        neg arr negates each element in the array arr. Returns a new array with each element negated.

                                        floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                                        ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                                        round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                                        sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                                        sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                                        log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                                        log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                                        log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                                        exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                                        sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                                        cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                                        tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                                        sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                                        cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                                        tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                                        asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                                        acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                                        atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                                        asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                                        acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                                        atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                                        val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                                        • axis specifies the axis along which to compute the minimum.
                                        • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                                        val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                                        • axis specifies the axis along which to compute the maximum.
                                        • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                                        val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val sum_reduce : + Symbol.Shape.Type.arr

                                        sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                                        • axis specifies the axis along which to compute the sum.
                                        • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                                        val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val log_sum_exp : + Symbol.Shape.Type.arr

                                        sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                                        • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                                        signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                                        sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                                        relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                                        dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                                        min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                                        max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                                        sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                                        log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                                        val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val clip_by_value : + Symbol.Shape.Type.arr

                                        log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                                        • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                                        • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                                        l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                                        l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                                        l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                                        val clip_by_l2norm : + Symbol.Shape.Type.arr

                                        clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                                        • amin specifies the minimum value to clip to.
                                        • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                                        clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                                        val scalar_pow : + Symbol.Shape.Type.arr

                                        pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                                        val pow_scalar : + Symbol.Shape.Type.arr

                                        scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                                        val atan2 : + Symbol.Shape.Type.arr

                                        pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                                        val scalar_atan2 : + Symbol.Shape.Type.arr

                                        atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                                        val atan2_scalar : + Symbol.Shape.Type.arr

                                        scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                                        val hypot : + Symbol.Shape.Type.arr

                                        atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                                        hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                                        min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                                        max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                                        add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                                        sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                                        mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                                        val add_scalar : + Symbol.Shape.Type.arr

                                        div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                                        val sub_scalar : + Symbol.Shape.Type.arr

                                        add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                        val mul_scalar : + Symbol.Shape.Type.arr

                                        sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                                        val div_scalar : + Symbol.Shape.Type.arr

                                        mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                        val scalar_add : + Symbol.Shape.Type.arr

                                        div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                        val scalar_sub : + Symbol.Shape.Type.arr

                                        scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                        val scalar_mul : + Symbol.Shape.Type.arr

                                        scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                                        val scalar_div : + Symbol.Shape.Type.arr

                                        scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                        scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                                        val elt_equal : + Symbol.Shape.Type.arr

                                        fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                                        val elt_not_equal : + Symbol.Shape.Type.arr

                                        elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                                        val elt_less : + Symbol.Shape.Type.arr

                                        elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                                        val elt_greater : + Symbol.Shape.Type.arr

                                        elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                                        val elt_less_equal : + Symbol.Shape.Type.arr

                                        elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                                        val elt_greater_equal : + Symbol.Shape.Type.arr

                                        elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                                        val elt_equal_scalar : + Symbol.Shape.Type.arr

                                        elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                                        val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                                        elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                                        val elt_less_scalar : + Symbol.Shape.Type.arr

                                        elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                                        val elt_greater_scalar : + Symbol.Shape.Type.arr

                                        elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                                        val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                                        elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                                        TODO

                                        val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                                        elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                                        TODO

                                        val conv1d : + Symbol.Shape.Type.arr

                                        elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                                        val conv2d : + Symbol.Shape.Type.arr

                                        conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                                        • padding specifies the padding strategy (default is "valid").
                                        • strides specifies the stride length. Returns a new array with the result of the convolution.
                                        val conv3d : + Symbol.Shape.Type.arr

                                        conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                                        • padding specifies the padding strategy (default is "valid").
                                        • strides specifies the stride length. Returns a new array with the result of the convolution.
                                        val transpose_conv1d : + Symbol.Shape.Type.arr

                                        conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                                        • padding specifies the padding strategy (default is "valid").
                                        • strides specifies the stride length. Returns a new array with the result of the convolution.
                                        val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val transpose_conv2d : + Symbol.Shape.Type.arr

                                        transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                        • padding specifies the padding strategy (default is "valid").
                                        • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                        val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val transpose_conv3d : + Symbol.Shape.Type.arr

                                        transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                        • padding specifies the padding strategy (default is "valid").
                                        • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                        val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val dilated_conv1d : + Symbol.Shape.Type.arr

                                        transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                        • padding specifies the padding strategy (default is "valid").
                                        • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                        val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val dilated_conv2d : + Symbol.Shape.Type.arr

                                        dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                                        • padding specifies the padding strategy (default is "valid").
                                        • strides specifies the stride length.
                                        • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                        val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val dilated_conv3d : + Symbol.Shape.Type.arr

                                        dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                                        • padding specifies the padding strategy (default is "valid").
                                        • strides specifies the stride length.
                                        • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                        val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val max_pool1d : + Symbol.Shape.Type.arr

                                        dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                                        • padding specifies the padding strategy (default is "valid").
                                        • strides specifies the stride length.
                                        • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                        val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val max_pool2d : + Symbol.Shape.Type.arr

                                        max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                                        • padding specifies the padding strategy (default is "valid").
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                        val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val max_pool3d : + Symbol.Shape.Type.arr

                                        max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                                        • padding specifies the padding strategy (default is "valid").
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                        val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val avg_pool1d : + Symbol.Shape.Type.arr

                                        max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                                        • padding specifies the padding strategy (default is "valid").
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                        val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val avg_pool2d : + Symbol.Shape.Type.arr

                                        avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                                        • padding specifies the padding strategy (default is "valid").
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                        val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val avg_pool3d : + Symbol.Shape.Type.arr

                                        avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                                        • padding specifies the padding strategy (default is "valid").
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                        val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        TODO

                                        val conv1d_backward_input : + Symbol.Shape.Type.arr

                                        avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                                        • padding specifies the padding strategy (default is "valid").
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                        val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        upsampling2d input size performs a 2-dimensional upsampling on the input array.

                                        • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                                        TODO

                                        val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                        conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                                        • input is the original input array.
                                        • kernel is the convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                        val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val conv2d_backward_input : + Symbol.Shape.Type.arr

                                        conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                                        • input is the original input array.
                                        • kernel is the convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                        TODO

                                        val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                        conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                                        • input is the original input array.
                                        • kernel is the convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                        val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val conv3d_backward_input : + Symbol.Shape.Type.arr

                                        conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                                        • input is the original input array.
                                        • kernel is the convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                        TODO

                                        val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                        conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                                        • input is the original input array.
                                        • kernel is the convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                        val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                                        conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                                        • input is the original input array.
                                        • kernel is the convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                                        val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                        transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                                        • input is the original input array.
                                        • kernel is the transposed convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                        val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                                        transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                                        • input is the original input array.
                                        • kernel is the transposed convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                        val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                        transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                                        • input is the original input array.
                                        • kernel is the transposed convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                        val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                                        transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                                        • input is the original input array.
                                        • kernel is the transposed convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                        val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                        transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                                        • input is the original input array.
                                        • kernel is the transposed convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                        val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                                        transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                                        • input is the original input array.
                                        • kernel is the transposed convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                        val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                        dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                                        • input is the original input array.
                                        • kernel is the dilated convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • dilations specifies the dilation rate.
                                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                        val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                                        dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                                        • input is the original input array.
                                        • kernel is the dilated convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • dilations specifies the dilation rate.
                                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                        val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                        dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                                        • input is the original input array.
                                        • kernel is the dilated convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • dilations specifies the dilation rate.
                                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                        val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                                        dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                                        • input is the original input array.
                                        • kernel is the dilated convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • dilations specifies the dilation rate.
                                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                        val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                        dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                                        • input is the original input array.
                                        • kernel is the dilated convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • dilations specifies the dilation rate.
                                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                        val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val max_pool1d_backward : + Symbol.Shape.Type.arr

                                        dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                                        • input is the original input array.
                                        • kernel is the dilated convolutional kernel used during the forward pass.
                                        • strides specifies the stride length.
                                        • dilations specifies the dilation rate.
                                        • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                        val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val max_pool2d_backward : + Symbol.Shape.Type.arr

                                        max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                                        • padding specifies the padding strategy used during the forward pass.
                                        • input is the original input array.
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                        val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val max_pool3d_backward : + Symbol.Shape.Type.arr

                                        max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                                        • padding specifies the padding strategy used during the forward pass.
                                        • input is the original input array.
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                        val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val avg_pool1d_backward : + Symbol.Shape.Type.arr

                                        max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                                        • padding specifies the padding strategy used during the forward pass.
                                        • input is the original input array.
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                        val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val avg_pool2d_backward : + Symbol.Shape.Type.arr

                                        avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                                        • padding specifies the padding strategy used during the forward pass.
                                        • input is the original input array.
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                        val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val avg_pool3d_backward : + Symbol.Shape.Type.arr

                                        avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                                        • padding specifies the padding strategy used during the forward pass.
                                        • input is the original input array.
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                        val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val upsampling2d_backward : + Symbol.Shape.Type.arr

                                        avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                                        • padding specifies the padding strategy used during the forward pass.
                                        • input is the original input array.
                                        • pool_size specifies the size of the pooling window.
                                        • strides specifies the stride length.
                                        • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                        val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val row_num : Symbol.Shape.Type.arr -> int

                                        TODO

                                        val col_num : Symbol.Shape.Type.arr -> int

                                        TODO

                                        val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        TODO

                                        val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                        TODO

                                        val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                        TODO

                                        TODO

                                        upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                                        • input is the original input array.
                                        • size specifies the upsampling factors for each dimension.
                                        • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                                        val row_num : Symbol.Shape.Type.arr -> int

                                        row_num arr returns the number of rows in the array arr.

                                        val col_num : Symbol.Shape.Type.arr -> int

                                        col_num arr returns the number of columns in the array arr.

                                        row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                                        val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                        rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                                        val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                        copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                                        val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                        copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                                        diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                                        trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                                        val transpose : + Symbol.Shape.Type.arr

                                        dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                                        val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val to_rows : Symbol.Shape.Type.arr -> 'a array

                                        TODO

                                        TODO

                                        val to_cols : Symbol.Shape.Type.arr -> 'a array

                                        TODO

                                        TODO

                                        val of_array : + Symbol.Shape.Type.arr

                                        transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                                        val to_rows : Symbol.Shape.Type.arr -> 'a array

                                        to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                                        of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                                        val to_cols : Symbol.Shape.Type.arr -> 'a array

                                        to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                                        of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                                        val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                                        TODO

                                        val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                        TODO

                                        val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                        TODO

                                        Scalar functions
                                        module Scalar : sig ... end
                                        module Mat : sig ... end
                                        module Linalg : sig ... end
                                        + Symbol.Shape.Type.arr

                                        of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                                        val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                        of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                                        val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                        to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                                        Scalar functions
                                        module Scalar : sig ... end
                                        module Mat : sig ... end
                                        module Linalg : sig ... end
                                        diff --git a/docs/owl-base/Owl_computation_optimiser/Make/index.html b/docs/owl-base/Owl_computation_optimiser/Make/index.html index 6bebf6703..5e30d1d77 100644 --- a/docs/owl-base/Owl_computation_optimiser/Make/index.html +++ b/docs/owl-base/Owl_computation_optimiser/Make/index.html @@ -1,4 +1,4 @@ -Make (owl-base.Owl_computation_optimiser.Make)

                                        Module Owl_computation_optimiser.Make

                                        Parameters

                                        Signature

                                        module Operator = Operator
                                        val _optimise_term : Operator.Symbol.Shape.Type.attr Owl_graph.node -> unit
                                        val pattern_011 : Operator.Symbol.Shape.Type.op -> float -> float -> float
                                        val pattern_013 : Operator.Symbol.Shape.Type.op -> float -> float
                                        val pattern_021 : 'a -> 'b
                                        val estimate_complexity : 'a Owl_graph.node array -> int * int
                                        val optimise_nodes : +Make (owl-base.Owl_computation_optimiser.Make)

                                        Module Owl_computation_optimiser.Make

                                        Parameters

                                        Signature

                                        module Operator = Operator
                                        val _optimise_term : Operator.Symbol.Shape.Type.attr Owl_graph.node -> unit
                                        val pattern_011 : Operator.Symbol.Shape.Type.op -> float -> float -> float
                                        val pattern_013 : Operator.Symbol.Shape.Type.op -> float -> float
                                        val pattern_021 : 'a -> 'b
                                        val estimate_complexity : 'a Owl_graph.node array -> int * int
                                        val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit
                                        diff --git a/docs/owl-base/Owl_computation_optimiser/index.html b/docs/owl-base/Owl_computation_optimiser/index.html index 73f42309e..9ac5234e4 100644 --- a/docs/owl-base/Owl_computation_optimiser/index.html +++ b/docs/owl-base/Owl_computation_optimiser/index.html @@ -1,2 +1,2 @@ -Owl_computation_optimiser (owl-base.Owl_computation_optimiser)

                                        Module Owl_computation_optimiser

                                        +Owl_computation_optimiser (owl-base.Owl_computation_optimiser)

                                        Module Owl_computation_optimiser

                                        diff --git a/docs/owl-base/Owl_computation_optimiser_sig/index.html b/docs/owl-base/Owl_computation_optimiser_sig/index.html index 50beb39ba..dfb6649b8 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/index.html @@ -1,2 +1,2 @@ -Owl_computation_optimiser_sig (owl-base.Owl_computation_optimiser_sig)

                                        Module Owl_computation_optimiser_sig

                                        module type Sig = sig ... end
                                        +Owl_computation_optimiser_sig (owl-base.Owl_computation_optimiser_sig)

                                        Module Owl_computation_optimiser_sig

                                        module type Sig = sig ... end
                                        diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Linalg/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Linalg/index.html index 5673decf2..897dd2341 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Linalg)

                                        Module Operator.Linalg

                                        val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                        TODO

                                        val svd : +Linalg (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Linalg)

                                        Module Operator.Linalg

                                        inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

                                        logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

                                        val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                        chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

                                        • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

                                        qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

                                        lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

                                        svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

                                        • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
                                        val lyapunov : + Symbol.Shape.Type.arr

                                        sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

                                        val discrete_lyapunov : + Symbol.Shape.Type.arr

                                        lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

                                        val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        val linsolve : + Symbol.Shape.Type.arr

                                        discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

                                        • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
                                        val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                        TODO

                                        linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

                                        • trans specifies whether to transpose the matrix A.
                                        • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

                                        care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

                                        • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                        + Symbol.Shape.Type.arr

                                        dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

                                        • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                        diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Mat/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Mat/index.html index 0d0dad7ce..958800dc8 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Mat/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Mat)

                                        Module Operator.Mat

                                        val eye : int -> Symbol.Shape.Type.arr

                                        TODO

                                        TODO

                                        TODO

                                        TODO

                                        +Mat (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Mat)

                                        Module Operator.Mat

                                        val eye : int -> Symbol.Shape.Type.arr

                                        eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

                                        diagm ?k v creates a diagonal matrix from the array v.

                                        • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

                                        triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

                                        tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

                                        diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Scalar/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Scalar/index.html index 5e970bff0..e9b3ed5b1 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Scalar)

                                        Module Operator.Scalar

                                        val add : +Scalar (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Scalar)

                                        Module Operator.Scalar

                                        add a b returns the sum of the scalars a and b.

                                        sub a b returns the difference of the scalars a and b.

                                        mul a b returns the product of the scalars a and b.

                                        div a b returns the quotient of the scalars a and b.

                                        val atan2 : + Symbol.Shape.Type.elt

                                        pow a b returns the scalar a raised to the power of b.

                                        + Symbol.Shape.Type.elt

                                        atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                                        abs a returns the absolute value of the scalar a.

                                        neg a returns the negation of the scalar a.

                                        sqr a returns the square of the scalar a.

                                        sqrt a returns the square root of the scalar a.

                                        exp a returns the exponential of the scalar a.

                                        log a returns the natural logarithm of the scalar a.

                                        log2 a returns the base-2 logarithm of the scalar a.

                                        log10 a returns the base-10 logarithm of the scalar a.

                                        signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                                        floor a returns the greatest integer less than or equal to the scalar a.

                                        ceil a returns the smallest integer greater than or equal to the scalar a.

                                        round a returns the nearest integer to the scalar a.

                                        sin a returns the sine of the scalar a.

                                        cos a returns the cosine of the scalar a.

                                        tan a returns the tangent of the scalar a.

                                        sinh a returns the hyperbolic sine of the scalar a.

                                        cosh a returns the hyperbolic cosine of the scalar a.

                                        tanh a returns the hyperbolic tangent of the scalar a.

                                        asin a returns the arcsine of the scalar a.

                                        acos a returns the arccosine of the scalar a.

                                        atan a returns the arctangent of the scalar a.

                                        asinh a returns the inverse hyperbolic sine of the scalar a.

                                        acosh a returns the inverse hyperbolic cosine of the scalar a.

                                        atanh a returns the inverse hyperbolic tangent of the scalar a.

                                        relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                                        dawsn a returns Dawson's function of the scalar a.

                                        sigmoid a returns the sigmoid function of the scalar a.

                                        diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 61282f3ea..0f7b840c9 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                        Module A.Linalg

                                        val inv : arr -> arr
                                        val logdet : arr -> elt
                                        val chol : ?upper:bool -> arr -> arr
                                        val svd : ?thin:bool -> arr -> arr * arr * arr
                                        val qr : arr -> arr * arr
                                        val lq : arr -> arr * arr
                                        val sylvester : arr -> arr -> arr -> arr
                                        val lyapunov : arr -> arr -> arr
                                        val discrete_lyapunov : +Linalg (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                        Module A.Linalg

                                        val inv : arr -> arr
                                        val logdet : arr -> elt
                                        val chol : ?upper:bool -> arr -> arr
                                        val svd : ?thin:bool -> arr -> arr * arr * arr
                                        val qr : arr -> arr * arr
                                        val lq : arr -> arr * arr
                                        val sylvester : arr -> arr -> arr -> arr
                                        val lyapunov : arr -> arr -> arr
                                        val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 2ee08df3e..b5c64766a 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device.A.Mat)

                                        Module A.Mat

                                        val diagm : ?k:int -> arr -> arr
                                        val triu : ?k:int -> arr -> arr
                                        val tril : ?k:int -> arr -> arr
                                        val eye : int -> arr
                                        +Mat (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device.A.Mat)

                                        Module A.Mat

                                        val diagm : ?k:int -> arr -> arr
                                        val triu : ?k:int -> arr -> arr
                                        val tril : ?k:int -> arr -> arr
                                        val eye : int -> arr
                                        diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index b3e9e2273..54f505600 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                        Module A.Scalar

                                        val add : elt -> elt -> elt
                                        val sub : elt -> elt -> elt
                                        val mul : elt -> elt -> elt
                                        val div : elt -> elt -> elt
                                        val pow : elt -> elt -> elt
                                        val atan2 : elt -> elt -> elt
                                        val abs : elt -> elt
                                        val neg : elt -> elt
                                        val sqr : elt -> elt
                                        val sqrt : elt -> elt
                                        val exp : elt -> elt
                                        val log : elt -> elt
                                        val log2 : elt -> elt
                                        val log10 : elt -> elt
                                        val signum : elt -> elt
                                        val floor : elt -> elt
                                        val ceil : elt -> elt
                                        val round : elt -> elt
                                        val sin : elt -> elt
                                        val cos : elt -> elt
                                        val tan : elt -> elt
                                        val sinh : elt -> elt
                                        val cosh : elt -> elt
                                        val tanh : elt -> elt
                                        val asin : elt -> elt
                                        val acos : elt -> elt
                                        val atan : elt -> elt
                                        val asinh : elt -> elt
                                        val acosh : elt -> elt
                                        val atanh : elt -> elt
                                        val relu : elt -> elt
                                        val dawsn : elt -> elt
                                        val sigmoid : elt -> elt
                                        +Scalar (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                        Module A.Scalar

                                        val add : elt -> elt -> elt
                                        val sub : elt -> elt -> elt
                                        val mul : elt -> elt -> elt
                                        val div : elt -> elt -> elt
                                        val pow : elt -> elt -> elt
                                        val atan2 : elt -> elt -> elt
                                        val abs : elt -> elt
                                        val neg : elt -> elt
                                        val sqr : elt -> elt
                                        val sqrt : elt -> elt
                                        val exp : elt -> elt
                                        val log : elt -> elt
                                        val log2 : elt -> elt
                                        val log10 : elt -> elt
                                        val signum : elt -> elt
                                        val floor : elt -> elt
                                        val ceil : elt -> elt
                                        val round : elt -> elt
                                        val sin : elt -> elt
                                        val cos : elt -> elt
                                        val tan : elt -> elt
                                        val sinh : elt -> elt
                                        val cosh : elt -> elt
                                        val tanh : elt -> elt
                                        val asin : elt -> elt
                                        val acos : elt -> elt
                                        val atan : elt -> elt
                                        val asinh : elt -> elt
                                        val acosh : elt -> elt
                                        val atanh : elt -> elt
                                        val relu : elt -> elt
                                        val dawsn : elt -> elt
                                        val sigmoid : elt -> elt
                                        diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/index.html index b088e6810..45cb39a52 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device.A)

                                        Module Device.A

                                        include Owl_types_ndarray_algodiff.Sig
                                        include Owl_types_ndarray_eltcmp.Sig
                                        include Owl_types_ndarray_basic.Sig
                                        type arr
                                        type elt
                                        val empty : int array -> arr
                                        val zeros : int array -> arr
                                        val ones : int array -> arr
                                        val create : int array -> elt -> arr
                                        val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                        val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                        val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                        val bernoulli : ?p:elt -> int array -> arr
                                        val init : int array -> (int -> elt) -> arr
                                        val init_nd : int array -> (int array -> elt) -> arr
                                        val shape : arr -> int array
                                        val numel : arr -> int
                                        val get : arr -> int array -> elt
                                        val set : arr -> int array -> elt -> unit
                                        val get_slice : int list list -> arr -> arr
                                        val set_slice : int list list -> arr -> arr -> unit
                                        val get_fancy : Owl_types_common.index list -> arr -> arr
                                        val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                        val copy : arr -> arr
                                        val copy_ : out:arr -> arr -> unit
                                        val reset : arr -> unit
                                        val reshape : arr -> int array -> arr
                                        val reverse : arr -> arr
                                        val tile : arr -> int array -> arr
                                        val repeat : arr -> int array -> arr
                                        val concatenate : ?axis:int -> arr array -> arr
                                        val stack : ?axis:int -> arr array -> arr
                                        val split : ?axis:int -> int array -> arr -> arr array
                                        val expand : ?hi:bool -> arr -> int -> arr
                                        val squeeze : ?axis:int array -> arr -> arr
                                        val draw : ?axis:int -> arr -> int -> arr * int array
                                        val map : (elt -> elt) -> arr -> arr
                                        val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                        val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                        val one_hot : int -> arr -> arr
                                        val pad : ?v:elt -> int list list -> arr -> arr
                                        val print : +A (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device.A)

                                        Module Device.A

                                        include Owl_types_ndarray_algodiff.Sig
                                        include Owl_types_ndarray_eltcmp.Sig
                                        include Owl_types_ndarray_basic.Sig
                                        type arr
                                        type elt
                                        val empty : int array -> arr
                                        val zeros : int array -> arr
                                        val ones : int array -> arr
                                        val create : int array -> elt -> arr
                                        val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                        val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                        val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                        val bernoulli : ?p:elt -> int array -> arr
                                        val init : int array -> (int -> elt) -> arr
                                        val init_nd : int array -> (int array -> elt) -> arr
                                        val shape : arr -> int array
                                        val numel : arr -> int
                                        val get : arr -> int array -> elt
                                        val set : arr -> int array -> elt -> unit
                                        val get_slice : int list list -> arr -> arr
                                        val set_slice : int list list -> arr -> arr -> unit
                                        val get_fancy : Owl_types_common.index list -> arr -> arr
                                        val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                        val copy : arr -> arr
                                        val copy_ : out:arr -> arr -> unit
                                        val reset : arr -> unit
                                        val reshape : arr -> int array -> arr
                                        val reverse : arr -> arr
                                        val tile : arr -> int array -> arr
                                        val repeat : arr -> int array -> arr
                                        val concatenate : ?axis:int -> arr array -> arr
                                        val stack : ?axis:int -> arr array -> arr
                                        val split : ?axis:int -> int array -> arr -> arr array
                                        val expand : ?hi:bool -> arr -> int -> arr
                                        val squeeze : ?axis:int array -> arr -> arr
                                        val draw : ?axis:int -> arr -> int -> arr * int array
                                        val map : (elt -> elt) -> arr -> arr
                                        val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                        val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                        val one_hot : int -> arr -> arr
                                        val pad : ?v:elt -> int list list -> arr -> arr
                                        val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/index.html index 2b653ca3e..d8b6a5edd 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device)

                                        Module Type.Device

                                        Type definition
                                        type device

                                        TODO

                                        type value

                                        TODO

                                        Core functions
                                        val make_device : unit -> device

                                        TODO

                                        val arr_to_value : A.arr -> value

                                        TODO

                                        val value_to_arr : value -> A.arr

                                        TODO

                                        val elt_to_value : A.elt -> value

                                        TODO

                                        val value_to_elt : value -> A.elt

                                        TODO

                                        val value_to_float : value -> float

                                        TODO

                                        val is_arr : value -> bool

                                        TODO

                                        val is_elt : value -> bool

                                        TODO

                                        +Device (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type.Device)

                                        Module Type.Device

                                        Type definition
                                        type device

                                        TODO

                                        type value

                                        TODO

                                        Core functions
                                        val make_device : unit -> device

                                        TODO

                                        val arr_to_value : A.arr -> value

                                        TODO

                                        val value_to_arr : value -> A.arr

                                        TODO

                                        val elt_to_value : A.elt -> value

                                        TODO

                                        val value_to_elt : value -> A.elt

                                        TODO

                                        val value_to_float : value -> float

                                        TODO

                                        val is_arr : value -> bool

                                        TODO

                                        val is_elt : value -> bool

                                        TODO

                                        diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/index.html index b859eda6a..9b4cd981a 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type)

                                        Module Shape.Type

                                        Type definition
                                        type state =
                                        1. | Valid
                                        2. | Invalid
                                          (*

                                          TODO

                                          *)

                                        TODO

                                        and block = {
                                        1. size : int;
                                        2. block_id : int;
                                        3. mutable active : t option;
                                        4. mutable memory : Device.value;
                                        5. mutable nodes : t list;
                                        }

                                        block type keeps a reference to a block of memory and to the nodes sharing that block.

                                        and attr = {
                                        1. mutable op : op;
                                        2. mutable freeze : bool;
                                        3. mutable reuse : bool;
                                        4. mutable state : state;
                                        5. mutable shape : int array option array;
                                        6. mutable value : Device.value array;
                                        7. mutable block : block array option;
                                        }

                                        TODO

                                        and arr =
                                        1. | Arr of t
                                        and elt =
                                        1. | Elt of t
                                        and op =
                                        1. | Noop
                                        2. | Var
                                        3. | Const
                                        4. | Empty of int array
                                        5. | Zeros of int array
                                        6. | Ones of int array
                                        7. | Create of int array
                                        8. | Sequential of int array
                                        9. | Uniform of int array
                                        10. | Gaussian of int array
                                        11. | Bernoulli of int array
                                        12. | Init of int array * int -> elt
                                        13. | Get of int array
                                        14. | Set of int array
                                        15. | GetSlice of int list list
                                        16. | SetSlice of int list list
                                        17. | GetFancy of Owl_types_common.index list
                                        18. | SetFancy of Owl_types_common.index list
                                        19. | Copy
                                        20. | Reset
                                        21. | Reshape of int array
                                        22. | Reverse
                                        23. | Tile of int array
                                        24. | Repeat of int array
                                        25. | Pad of elt * int list list
                                        26. | Concatenate of int
                                        27. | Stack of int
                                        28. | Split of int * int array
                                        29. | Draw of int * int
                                        30. | Map of elt -> elt
                                        31. | Fold of int * elt -> elt -> elt
                                        32. | Scan of int * elt -> elt -> elt
                                        33. | OneHot of int
                                        34. | OfArray of int array
                                        35. | Delay of Device.A.arr -> Device.A.arr
                                        36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                        37. | LazyPrint of int option +Type (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape.Type)

                                          Module Shape.Type

                                          Type definition
                                          type state =
                                          1. | Valid
                                          2. | Invalid
                                            (*

                                            TODO

                                            *)

                                          TODO

                                          and block = {
                                          1. size : int;
                                          2. block_id : int;
                                          3. mutable active : t option;
                                          4. mutable memory : Device.value;
                                          5. mutable nodes : t list;
                                          }

                                          block type keeps a reference to a block of memory and to the nodes sharing that block.

                                          and attr = {
                                          1. mutable op : op;
                                          2. mutable freeze : bool;
                                          3. mutable reuse : bool;
                                          4. mutable state : state;
                                          5. mutable shape : int array option array;
                                          6. mutable value : Device.value array;
                                          7. mutable block : block array option;
                                          }

                                          TODO

                                          and arr =
                                          1. | Arr of t
                                          and elt =
                                          1. | Elt of t
                                          and op =
                                          1. | Noop
                                          2. | Var
                                          3. | Const
                                          4. | Empty of int array
                                          5. | Zeros of int array
                                          6. | Ones of int array
                                          7. | Create of int array
                                          8. | Sequential of int array
                                          9. | Uniform of int array
                                          10. | Gaussian of int array
                                          11. | Bernoulli of int array
                                          12. | Init of int array * int -> elt
                                          13. | Get of int array
                                          14. | Set of int array
                                          15. | GetSlice of int list list
                                          16. | SetSlice of int list list
                                          17. | GetFancy of Owl_types_common.index list
                                          18. | SetFancy of Owl_types_common.index list
                                          19. | Copy
                                          20. | Reset
                                          21. | Reshape of int array
                                          22. | Reverse
                                          23. | Tile of int array
                                          24. | Repeat of int array
                                          25. | Pad of elt * int list list
                                          26. | Concatenate of int
                                          27. | Stack of int
                                          28. | Split of int * int array
                                          29. | Draw of int * int
                                          30. | Map of elt -> elt
                                          31. | Fold of int * elt -> elt -> elt
                                          32. | Scan of int * elt -> elt -> elt
                                          33. | OneHot of int
                                          34. | OfArray of int array
                                          35. | Delay of Device.A.arr -> Device.A.arr
                                          36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                          37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                          38. | Abs
                                          39. | Neg
                                          40. | Floor
                                          41. | Ceil
                                          42. | Round
                                          43. | Sqr
                                          44. | Sqrt
                                          45. | Log
                                          46. | Log2
                                          47. | Log10
                                          48. | Exp
                                          49. | Sin
                                          50. | Cos
                                          51. | Tan
                                          52. | Sinh
                                          53. | Cosh
                                          54. | Tanh
                                          55. | Asin
                                          56. | Acos
                                          57. | Atan
                                          58. | Asinh
                                          59. | Acosh
                                          60. | Atanh
                                          61. | Min of bool * int
                                          62. | Max of bool * int
                                          63. | Sum of bool * int
                                          64. | SumReduce of int array
                                          65. | Signum
                                          66. | Sigmoid
                                          67. | Relu
                                          68. | Dawsn
                                          69. | Min'
                                          70. | Max'
                                          71. | Sum'
                                          72. | LogSumExp'
                                          73. | LogSumExp of bool * int
                                          74. | L1norm'
                                          75. | L2norm'
                                          76. | L2NormSqr'
                                          77. | ClipByValue
                                          78. | ClipByL2norm
                                          79. | Pow
                                          80. | ScalarPow
                                          81. | PowScalar
                                          82. | Atan2
                                          83. | ScalarAtan2
                                          84. | Atan2Scalar
                                          85. | Hypot
                                          86. | Min2
                                          87. | Max2
                                          88. | Add
                                          89. | Sub
                                          90. | Mul
                                          91. | Div
                                          92. | AddScalar
                                          93. | SubScalar
                                          94. | MulScalar
                                          95. | DivScalar
                                          96. | ScalarAdd
                                          97. | ScalarSub
                                          98. | ScalarMul
                                          99. | ScalarDiv
                                          100. | FMA
                                          101. | EltEqual
                                          102. | EltNotEqual
                                          103. | EltLess
                                          104. | EltGreater
                                          105. | EltLessEqual
                                          106. | EltGreaterEqual
                                          107. | EltEqualScalar
                                          108. | EltNotEqualScalar
                                          109. | EltLessScalar
                                          110. | EltGreaterScalar
                                          111. | EltLessEqualScalar
                                          112. | EltGreaterEqualScalar
                                          113. | Conv1d of Owl_types_common.padding * int array
                                          114. | Conv2d of Owl_types_common.padding * int array
                                          115. | Conv3d of Owl_types_common.padding * int array
                                          116. | TransposeConv1d of Owl_types_common.padding * int array
                                          117. | TransposeConv2d of Owl_types_common.padding * int array
                                          118. | TransposeConv3d of Owl_types_common.padding * int array
                                          119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                          120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                          121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                          122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                          123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                          124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                          125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                          126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                          127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                          128. | UpSampling2d of int array
                                          129. | Conv1dBackwardInput of int array
                                          130. | Conv1dBackwardKernel of int array
                                          131. | Conv2dBackwardInput of int array
                                          132. | Conv2dBackwardKernel of int array
                                          133. | Conv3dBackwardInput of int array
                                          134. | Conv3dBackwardKernel of int array
                                          135. | TransposeConv1dBackwardInput of int array
                                          136. | TransposeConv1dBackwardKernel of int array
                                          137. | TransposeConv2dBackwardInput of int array
                                          138. | TransposeConv2dBackwardKernel of int array
                                          139. | TransposeConv3dBackwardInput of int array
                                          140. | TransposeConv3dBackwardKernel of int array
                                          141. | DilatedConv1dBackwardInput of int array * int array
                                          142. | DilatedConv1dBackwardKernel of int array * int array
                                          143. | DilatedConv2dBackwardInput of int array * int array
                                          144. | DilatedConv2dBackwardKernel of int array * int array
                                          145. | DilatedConv3dBackwardInput of int array * int array
                                          146. | DilatedConv3dBackwardKernel of int array * int array
                                          147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                          148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                          149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                          150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                          151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                          152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                          153. | UpSampling2dBackward of int array
                                          154. | RowNum
                                          155. | ColNum
                                          156. | Row
                                          157. | Rows of int array
                                          158. | CopyRowTo
                                          159. | CopyColTo
                                          160. | Dot of bool * bool * elt * elt
                                          161. | Inv
                                          162. | Trace
                                          163. | Transpose of int array
                                          164. | ToRows
                                          165. | OfRows
                                          166. | Scalar_Add
                                          167. | Scalar_Sub
                                          168. | Scalar_Mul
                                          169. | Scalar_Div
                                          170. | Scalar_Pow
                                          171. | Scalar_Atan2
                                          172. | Scalar_Abs
                                          173. | Scalar_Neg
                                          174. | Scalar_Sqr
                                          175. | Scalar_Sqrt
                                          176. | Scalar_Exp
                                          177. | Scalar_Log
                                          178. | Scalar_Log2
                                          179. | Scalar_Log10
                                          180. | Scalar_Signum
                                          181. | Scalar_Floor
                                          182. | Scalar_Ceil
                                          183. | Scalar_Round
                                          184. | Scalar_Sin
                                          185. | Scalar_Cos
                                          186. | Scalar_Tan
                                          187. | Scalar_Sinh
                                          188. | Scalar_Cosh
                                          189. | Scalar_Tanh
                                          190. | Scalar_Asin
                                          191. | Scalar_Acos
                                          192. | Scalar_Atan
                                          193. | Scalar_Asinh
                                          194. | Scalar_Acosh
                                          195. | Scalar_Atanh
                                          196. | Scalar_Relu
                                          197. | Scalar_Dawsn
                                          198. | Scalar_Sigmoid
                                          199. | Fused_Adagrad of float * float
                                            (*

                                            TODO

                                            *)
                                          diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/index.html index f1dabb1a2..96d25527a 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape)

                                          Module Symbol.Shape

                                          Core functions
                                          val infer_shape : +Shape (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol.Shape)

                                          Module Symbol.Shape

                                          Core functions
                                          val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                          TODO

                                          diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/index.html index 174ed17a1..17c4a6aa8 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol)

                                          Module Operator.Symbol

                                          Core functions
                                          val op_to_str : Shape.Type.op -> string

                                          TODO

                                          val is_random_variable : Shape.Type.op -> bool

                                          TODO

                                          val refnum : 'a Owl_graph.node -> int

                                          TODO

                                          val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                          TODO

                                          val node_numel : Shape.Type.attr Owl_graph.node -> int

                                          TODO

                                          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                          TODO

                                          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                          TODO

                                          val shape_to_str : int array option array -> string

                                          TODO

                                          val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                          TODO

                                          val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                          TODO

                                          val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                          TODO

                                          val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                          TODO

                                          val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                          TODO

                                          val make_node : +Symbol (owl-base.Owl_computation_optimiser_sig.Sig.Operator.Symbol)

                                          Module Operator.Symbol

                                          Core functions
                                          val op_to_str : Shape.Type.op -> string

                                          TODO

                                          val is_random_variable : Shape.Type.op -> bool

                                          TODO

                                          val refnum : 'a Owl_graph.node -> int

                                          TODO

                                          val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                          TODO

                                          val node_numel : Shape.Type.attr Owl_graph.node -> int

                                          TODO

                                          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                          TODO

                                          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                          TODO

                                          val shape_to_str : int array option array -> string

                                          TODO

                                          val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                          TODO

                                          val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                          TODO

                                          val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                          TODO

                                          val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                          TODO

                                          val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                          TODO

                                          val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/index.html index 50ddb9cce..6d1462988 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_computation_optimiser_sig.Sig.Operator)

                                          Module Sig.Operator

                                          Vectorised functions
                                          val empty : int array -> Symbol.Shape.Type.arr

                                          TODO

                                          val zeros : int array -> Symbol.Shape.Type.arr

                                          TODO

                                          val ones : int array -> Symbol.Shape.Type.arr

                                          TODO

                                          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                          TODO

                                          val sequential : +Operator (owl-base.Owl_computation_optimiser_sig.Sig.Operator)

                                          Module Sig.Operator

                                          Vectorised functions

                                          noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                                          val empty : int array -> Symbol.Shape.Type.arr

                                          empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                                          val zeros : int array -> Symbol.Shape.Type.arr

                                          zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                                          val ones : int array -> Symbol.Shape.Type.arr

                                          ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                                          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                          create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                                          val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val uniform : + Symbol.Shape.Type.arr

                                          sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                                          val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val gaussian : + Symbol.Shape.Type.arr

                                          uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                                          val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                          TODO

                                          val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                          TODO

                                          val init_nd : + Symbol.Shape.Type.arr

                                          gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                                          val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                          bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                                          val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                          init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                                          val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                                          TODO

                                          val shape : Symbol.Shape.Type.arr -> int array

                                          TODO

                                          val numel : Symbol.Shape.Type.arr -> int

                                          TODO

                                          TODO

                                          val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                          TODO

                                          val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                          TODO

                                          val set_slice : + Symbol.Shape.Type.arr

                                          init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                                          val shape : Symbol.Shape.Type.arr -> int array

                                          shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                                          val numel : Symbol.Shape.Type.arr -> int

                                          numel arr returns the total number of elements in the array arr.

                                          get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                                          val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                          set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                                          val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                          get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                                          val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                          TODO

                                          val get_fancy : + unit

                                          set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                                          val set_fancy : + Symbol.Shape.Type.arr

                                          get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                                          val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                          TODO

                                          val copy_ : out:'a -> 'b -> 'c

                                          TODO

                                          val reset : Symbol.Shape.Type.arr -> unit

                                          TODO

                                          val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          TODO

                                          val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          TODO

                                          val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          TODO

                                          val pad : + unit

                                          set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                                          copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                                          val copy_ : out:'a -> 'b -> 'c

                                          copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                                          val reset : Symbol.Shape.Type.arr -> unit

                                          reset arr sets all elements of the array arr to zero.

                                          val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                                          reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                                          val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                                          val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                                          TODO

                                          val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                          TODO

                                          val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                          TODO

                                          val concatenate : + Symbol.Shape.Type.arr

                                          pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                                          val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                          expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                                          val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                          squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                                          val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                          TODO

                                          val concat : + Symbol.Shape.Type.arr

                                          concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                                          val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                          stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                                          val split : ?axis:int -> 'a -> 'b -> 'c

                                          TODO

                                          concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                                          val split : ?axis:int -> 'a -> 'b -> 'c

                                          split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                                          • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                                          val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                                          TODO

                                          val map : + Symbol.Shape.Type.arr * 'a array

                                          draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                                          map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                                          fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                                          TODO

                                          val delay : + Symbol.Shape.Type.arr

                                          scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                                          one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                                          delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                                          val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                          val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                          TODO

                                          lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                          val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                          print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                                          • max_row is an optional parameter specifying the maximum number of rows to print.
                                          • max_col is an optional parameter specifying the maximum number of columns to print.
                                          • header is an optional parameter to include a header in the output.
                                          • fmt is an optional parameter to specify the format of the output.

                                          abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                                          neg arr negates each element in the array arr. Returns a new array with each element negated.

                                          floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                                          ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                                          round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                                          sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                                          sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                                          log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                                          log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                                          log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                                          exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                                          sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                                          cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                                          tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                                          sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                                          cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                                          tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                                          asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                                          acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                                          atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                                          asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                                          acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                                          atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                                          val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                                          • axis specifies the axis along which to compute the minimum.
                                          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                                          val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                                          • axis specifies the axis along which to compute the maximum.
                                          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                                          val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val sum_reduce : + Symbol.Shape.Type.arr

                                          sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                                          • axis specifies the axis along which to compute the sum.
                                          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                                          val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val log_sum_exp : + Symbol.Shape.Type.arr

                                          sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                                          • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                                          signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                                          sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                                          relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                                          dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                                          min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                                          max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                                          sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                                          log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val clip_by_value : + Symbol.Shape.Type.arr

                                          log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                                          • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                                          • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                                          l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                                          l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                                          l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                                          val clip_by_l2norm : + Symbol.Shape.Type.arr

                                          clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                                          • amin specifies the minimum value to clip to.
                                          • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                                          clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                                          val scalar_pow : + Symbol.Shape.Type.arr

                                          pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                                          val pow_scalar : + Symbol.Shape.Type.arr

                                          scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                                          val atan2 : + Symbol.Shape.Type.arr

                                          pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                                          val scalar_atan2 : + Symbol.Shape.Type.arr

                                          atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                                          val atan2_scalar : + Symbol.Shape.Type.arr

                                          scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                                          val hypot : + Symbol.Shape.Type.arr

                                          atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                                          hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                                          min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                                          max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                                          add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                                          sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                                          mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                                          val add_scalar : + Symbol.Shape.Type.arr

                                          div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                                          val sub_scalar : + Symbol.Shape.Type.arr

                                          add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                          val mul_scalar : + Symbol.Shape.Type.arr

                                          sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                                          val div_scalar : + Symbol.Shape.Type.arr

                                          mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                          val scalar_add : + Symbol.Shape.Type.arr

                                          div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                          val scalar_sub : + Symbol.Shape.Type.arr

                                          scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                          val scalar_mul : + Symbol.Shape.Type.arr

                                          scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                                          val scalar_div : + Symbol.Shape.Type.arr

                                          scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                          scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                                          val elt_equal : + Symbol.Shape.Type.arr

                                          fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                                          val elt_not_equal : + Symbol.Shape.Type.arr

                                          elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                                          val elt_less : + Symbol.Shape.Type.arr

                                          elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                                          val elt_greater : + Symbol.Shape.Type.arr

                                          elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                                          val elt_less_equal : + Symbol.Shape.Type.arr

                                          elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                                          val elt_greater_equal : + Symbol.Shape.Type.arr

                                          elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                                          val elt_equal_scalar : + Symbol.Shape.Type.arr

                                          elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                                          val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                                          elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                                          val elt_less_scalar : + Symbol.Shape.Type.arr

                                          elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                                          val elt_greater_scalar : + Symbol.Shape.Type.arr

                                          elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                                          val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                                          elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                                          TODO

                                          val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                                          elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                                          TODO

                                          val conv1d : + Symbol.Shape.Type.arr

                                          elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                                          val conv2d : + Symbol.Shape.Type.arr

                                          conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                                          • padding specifies the padding strategy (default is "valid").
                                          • strides specifies the stride length. Returns a new array with the result of the convolution.
                                          val conv3d : + Symbol.Shape.Type.arr

                                          conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                                          • padding specifies the padding strategy (default is "valid").
                                          • strides specifies the stride length. Returns a new array with the result of the convolution.
                                          val transpose_conv1d : + Symbol.Shape.Type.arr

                                          conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                                          • padding specifies the padding strategy (default is "valid").
                                          • strides specifies the stride length. Returns a new array with the result of the convolution.
                                          val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val transpose_conv2d : + Symbol.Shape.Type.arr

                                          transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                          • padding specifies the padding strategy (default is "valid").
                                          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                          val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val transpose_conv3d : + Symbol.Shape.Type.arr

                                          transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                          • padding specifies the padding strategy (default is "valid").
                                          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                          val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val dilated_conv1d : + Symbol.Shape.Type.arr

                                          transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                          • padding specifies the padding strategy (default is "valid").
                                          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                          val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val dilated_conv2d : + Symbol.Shape.Type.arr

                                          dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                                          • padding specifies the padding strategy (default is "valid").
                                          • strides specifies the stride length.
                                          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                          val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val dilated_conv3d : + Symbol.Shape.Type.arr

                                          dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                                          • padding specifies the padding strategy (default is "valid").
                                          • strides specifies the stride length.
                                          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                          val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val max_pool1d : + Symbol.Shape.Type.arr

                                          dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                                          • padding specifies the padding strategy (default is "valid").
                                          • strides specifies the stride length.
                                          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                          val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val max_pool2d : + Symbol.Shape.Type.arr

                                          max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                                          • padding specifies the padding strategy (default is "valid").
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                          val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val max_pool3d : + Symbol.Shape.Type.arr

                                          max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                                          • padding specifies the padding strategy (default is "valid").
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                          val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val avg_pool1d : + Symbol.Shape.Type.arr

                                          max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                                          • padding specifies the padding strategy (default is "valid").
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                          val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val avg_pool2d : + Symbol.Shape.Type.arr

                                          avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                                          • padding specifies the padding strategy (default is "valid").
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                          val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val avg_pool3d : + Symbol.Shape.Type.arr

                                          avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                                          • padding specifies the padding strategy (default is "valid").
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                          val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          TODO

                                          val conv1d_backward_input : + Symbol.Shape.Type.arr

                                          avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                                          • padding specifies the padding strategy (default is "valid").
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                          val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          upsampling2d input size performs a 2-dimensional upsampling on the input array.

                                          • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                                          TODO

                                          val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                          conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                                          • input is the original input array.
                                          • kernel is the convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                          val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val conv2d_backward_input : + Symbol.Shape.Type.arr

                                          conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                                          • input is the original input array.
                                          • kernel is the convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                          TODO

                                          val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                          conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                                          • input is the original input array.
                                          • kernel is the convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                          val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val conv3d_backward_input : + Symbol.Shape.Type.arr

                                          conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                                          • input is the original input array.
                                          • kernel is the convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                          TODO

                                          val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                          conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                                          • input is the original input array.
                                          • kernel is the convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                          val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                                          conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                                          • input is the original input array.
                                          • kernel is the convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                                          val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                          transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                                          • input is the original input array.
                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                          val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                                          transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                                          • input is the original input array.
                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                          val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                          transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                                          • input is the original input array.
                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                          val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                                          transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                                          • input is the original input array.
                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                          val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                          transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                                          • input is the original input array.
                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                          val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                                          transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                                          • input is the original input array.
                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                          val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                          dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                                          • input is the original input array.
                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • dilations specifies the dilation rate.
                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                          val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                                          dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                                          • input is the original input array.
                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • dilations specifies the dilation rate.
                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                          val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                          dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                                          • input is the original input array.
                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • dilations specifies the dilation rate.
                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                          val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                                          dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                                          • input is the original input array.
                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • dilations specifies the dilation rate.
                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                          val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                          dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                                          • input is the original input array.
                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • dilations specifies the dilation rate.
                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                          val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val max_pool1d_backward : + Symbol.Shape.Type.arr

                                          dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                                          • input is the original input array.
                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                          • strides specifies the stride length.
                                          • dilations specifies the dilation rate.
                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                          val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val max_pool2d_backward : + Symbol.Shape.Type.arr

                                          max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                                          • padding specifies the padding strategy used during the forward pass.
                                          • input is the original input array.
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                          val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val max_pool3d_backward : + Symbol.Shape.Type.arr

                                          max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                                          • padding specifies the padding strategy used during the forward pass.
                                          • input is the original input array.
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                          val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val avg_pool1d_backward : + Symbol.Shape.Type.arr

                                          max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                                          • padding specifies the padding strategy used during the forward pass.
                                          • input is the original input array.
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                          val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val avg_pool2d_backward : + Symbol.Shape.Type.arr

                                          avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                                          • padding specifies the padding strategy used during the forward pass.
                                          • input is the original input array.
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                          val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val avg_pool3d_backward : + Symbol.Shape.Type.arr

                                          avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                                          • padding specifies the padding strategy used during the forward pass.
                                          • input is the original input array.
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                          val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val upsampling2d_backward : + Symbol.Shape.Type.arr

                                          avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                                          • padding specifies the padding strategy used during the forward pass.
                                          • input is the original input array.
                                          • pool_size specifies the size of the pooling window.
                                          • strides specifies the stride length.
                                          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                          val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val row_num : Symbol.Shape.Type.arr -> int

                                          TODO

                                          val col_num : Symbol.Shape.Type.arr -> int

                                          TODO

                                          val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          TODO

                                          val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                          TODO

                                          val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                          TODO

                                          TODO

                                          upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                                          • input is the original input array.
                                          • size specifies the upsampling factors for each dimension.
                                          • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                                          val row_num : Symbol.Shape.Type.arr -> int

                                          row_num arr returns the number of rows in the array arr.

                                          val col_num : Symbol.Shape.Type.arr -> int

                                          col_num arr returns the number of columns in the array arr.

                                          row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                                          val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                          rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                                          val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                          copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                                          val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                          copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                                          diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                                          trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                                          val transpose : + Symbol.Shape.Type.arr

                                          dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                                          val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                          TODO

                                          val to_rows : Symbol.Shape.Type.arr -> 'a array

                                          TODO

                                          TODO

                                          val to_cols : Symbol.Shape.Type.arr -> 'a array

                                          TODO

                                          TODO

                                          val of_array : + Symbol.Shape.Type.arr

                                          transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                                          val to_rows : Symbol.Shape.Type.arr -> 'a array

                                          to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                                          of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                                          val to_cols : Symbol.Shape.Type.arr -> 'a array

                                          to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                                          of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                                          val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                                          TODO

                                          val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                          TODO

                                          val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                          TODO

                                          Scalar functions
                                          module Scalar : sig ... end
                                          module Mat : sig ... end
                                          module Linalg : sig ... end
                                          + Symbol.Shape.Type.arr

                                          of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                                          val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                          of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                                          val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                          to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                                          Scalar functions
                                          module Scalar : sig ... end
                                          module Mat : sig ... end
                                          module Linalg : sig ... end
                                          diff --git a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/index.html b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/index.html index 2676f3eaf..1c8f7dda6 100644 --- a/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_computation_optimiser_sig/module-type-Sig/index.html @@ -1,4 +1,4 @@ -Sig (owl-base.Owl_computation_optimiser_sig.Sig)

                                          Module type Owl_computation_optimiser_sig.Sig

                                          Core functions
                                          val estimate_complexity : 'a Owl_graph.node array -> int * int

                                          TODO

                                          val optimise_nodes : +Sig (owl-base.Owl_computation_optimiser_sig.Sig)

                                          Module type Owl_computation_optimiser_sig.Sig

                                          Core functions
                                          val estimate_complexity : 'a Owl_graph.node array -> int * int

                                          TODO

                                          val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

                                          TODO

                                          diff --git a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Linalg/index.html index a882c402f..52a8f0910 100644 --- a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_shape.Make.Type.Device.A.Linalg)

                                          Module A.Linalg

                                          val inv : arr -> arr
                                          val logdet : arr -> elt
                                          val chol : ?upper:bool -> arr -> arr
                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                          val qr : arr -> arr * arr
                                          val lq : arr -> arr * arr
                                          val sylvester : arr -> arr -> arr -> arr
                                          val lyapunov : arr -> arr -> arr
                                          val discrete_lyapunov : +Linalg (owl-base.Owl_computation_shape.Make.Type.Device.A.Linalg)

                                          Module A.Linalg

                                          val inv : arr -> arr
                                          val logdet : arr -> elt
                                          val chol : ?upper:bool -> arr -> arr
                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                          val qr : arr -> arr * arr
                                          val lq : arr -> arr * arr
                                          val sylvester : arr -> arr -> arr -> arr
                                          val lyapunov : arr -> arr -> arr
                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Mat/index.html index 8a75f143f..11e9125f8 100644 --- a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_shape.Make.Type.Device.A.Mat)

                                          Module A.Mat

                                          val diagm : ?k:int -> arr -> arr
                                          val triu : ?k:int -> arr -> arr
                                          val tril : ?k:int -> arr -> arr
                                          val eye : int -> arr
                                          +Mat (owl-base.Owl_computation_shape.Make.Type.Device.A.Mat)

                                          Module A.Mat

                                          val diagm : ?k:int -> arr -> arr
                                          val triu : ?k:int -> arr -> arr
                                          val tril : ?k:int -> arr -> arr
                                          val eye : int -> arr
                                          diff --git a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Scalar/index.html index f46d5bbb6..e303e8059 100644 --- a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_shape.Make.Type.Device.A.Scalar)

                                          Module A.Scalar

                                          val add : elt -> elt -> elt
                                          val sub : elt -> elt -> elt
                                          val mul : elt -> elt -> elt
                                          val div : elt -> elt -> elt
                                          val pow : elt -> elt -> elt
                                          val atan2 : elt -> elt -> elt
                                          val abs : elt -> elt
                                          val neg : elt -> elt
                                          val sqr : elt -> elt
                                          val sqrt : elt -> elt
                                          val exp : elt -> elt
                                          val log : elt -> elt
                                          val log2 : elt -> elt
                                          val log10 : elt -> elt
                                          val signum : elt -> elt
                                          val floor : elt -> elt
                                          val ceil : elt -> elt
                                          val round : elt -> elt
                                          val sin : elt -> elt
                                          val cos : elt -> elt
                                          val tan : elt -> elt
                                          val sinh : elt -> elt
                                          val cosh : elt -> elt
                                          val tanh : elt -> elt
                                          val asin : elt -> elt
                                          val acos : elt -> elt
                                          val atan : elt -> elt
                                          val asinh : elt -> elt
                                          val acosh : elt -> elt
                                          val atanh : elt -> elt
                                          val relu : elt -> elt
                                          val dawsn : elt -> elt
                                          val sigmoid : elt -> elt
                                          +Scalar (owl-base.Owl_computation_shape.Make.Type.Device.A.Scalar)

                                          Module A.Scalar

                                          val add : elt -> elt -> elt
                                          val sub : elt -> elt -> elt
                                          val mul : elt -> elt -> elt
                                          val div : elt -> elt -> elt
                                          val pow : elt -> elt -> elt
                                          val atan2 : elt -> elt -> elt
                                          val abs : elt -> elt
                                          val neg : elt -> elt
                                          val sqr : elt -> elt
                                          val sqrt : elt -> elt
                                          val exp : elt -> elt
                                          val log : elt -> elt
                                          val log2 : elt -> elt
                                          val log10 : elt -> elt
                                          val signum : elt -> elt
                                          val floor : elt -> elt
                                          val ceil : elt -> elt
                                          val round : elt -> elt
                                          val sin : elt -> elt
                                          val cos : elt -> elt
                                          val tan : elt -> elt
                                          val sinh : elt -> elt
                                          val cosh : elt -> elt
                                          val tanh : elt -> elt
                                          val asin : elt -> elt
                                          val acos : elt -> elt
                                          val atan : elt -> elt
                                          val asinh : elt -> elt
                                          val acosh : elt -> elt
                                          val atanh : elt -> elt
                                          val relu : elt -> elt
                                          val dawsn : elt -> elt
                                          val sigmoid : elt -> elt
                                          diff --git a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/index.html b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/index.html index b602d5f85..8e5a24230 100644 --- a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_shape.Make.Type.Device.A)

                                          Module Device.A

                                          include Owl_types_ndarray_algodiff.Sig
                                          include Owl_types_ndarray_eltcmp.Sig
                                          include Owl_types_ndarray_basic.Sig
                                          type arr
                                          type elt
                                          val empty : int array -> arr
                                          val zeros : int array -> arr
                                          val ones : int array -> arr
                                          val create : int array -> elt -> arr
                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                          val bernoulli : ?p:elt -> int array -> arr
                                          val init : int array -> (int -> elt) -> arr
                                          val init_nd : int array -> (int array -> elt) -> arr
                                          val shape : arr -> int array
                                          val numel : arr -> int
                                          val get : arr -> int array -> elt
                                          val set : arr -> int array -> elt -> unit
                                          val get_slice : int list list -> arr -> arr
                                          val set_slice : int list list -> arr -> arr -> unit
                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                          val copy : arr -> arr
                                          val copy_ : out:arr -> arr -> unit
                                          val reset : arr -> unit
                                          val reshape : arr -> int array -> arr
                                          val reverse : arr -> arr
                                          val tile : arr -> int array -> arr
                                          val repeat : arr -> int array -> arr
                                          val concatenate : ?axis:int -> arr array -> arr
                                          val stack : ?axis:int -> arr array -> arr
                                          val split : ?axis:int -> int array -> arr -> arr array
                                          val expand : ?hi:bool -> arr -> int -> arr
                                          val squeeze : ?axis:int array -> arr -> arr
                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                          val map : (elt -> elt) -> arr -> arr
                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                          val one_hot : int -> arr -> arr
                                          val pad : ?v:elt -> int list list -> arr -> arr
                                          val print : +A (owl-base.Owl_computation_shape.Make.Type.Device.A)

                                          Module Device.A

                                          include Owl_types_ndarray_algodiff.Sig
                                          include Owl_types_ndarray_eltcmp.Sig
                                          include Owl_types_ndarray_basic.Sig
                                          type arr
                                          type elt
                                          val empty : int array -> arr
                                          val zeros : int array -> arr
                                          val ones : int array -> arr
                                          val create : int array -> elt -> arr
                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                          val bernoulli : ?p:elt -> int array -> arr
                                          val init : int array -> (int -> elt) -> arr
                                          val init_nd : int array -> (int array -> elt) -> arr
                                          val shape : arr -> int array
                                          val numel : arr -> int
                                          val get : arr -> int array -> elt
                                          val set : arr -> int array -> elt -> unit
                                          val get_slice : int list list -> arr -> arr
                                          val set_slice : int list list -> arr -> arr -> unit
                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                          val copy : arr -> arr
                                          val copy_ : out:arr -> arr -> unit
                                          val reset : arr -> unit
                                          val reshape : arr -> int array -> arr
                                          val reverse : arr -> arr
                                          val tile : arr -> int array -> arr
                                          val repeat : arr -> int array -> arr
                                          val concatenate : ?axis:int -> arr array -> arr
                                          val stack : ?axis:int -> arr array -> arr
                                          val split : ?axis:int -> int array -> arr -> arr array
                                          val expand : ?hi:bool -> arr -> int -> arr
                                          val squeeze : ?axis:int array -> arr -> arr
                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                          val map : (elt -> elt) -> arr -> arr
                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                          val one_hot : int -> arr -> arr
                                          val pad : ?v:elt -> int list list -> arr -> arr
                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/index.html b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/index.html index f5ee27dcf..3ded8638b 100644 --- a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/index.html +++ b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_shape.Make.Type.Device)

                                          Module Type.Device

                                          Type definition
                                          type device

                                          TODO

                                          type value

                                          TODO

                                          Core functions
                                          val make_device : unit -> device

                                          TODO

                                          val arr_to_value : A.arr -> value

                                          TODO

                                          val value_to_arr : value -> A.arr

                                          TODO

                                          val elt_to_value : A.elt -> value

                                          TODO

                                          val value_to_elt : value -> A.elt

                                          TODO

                                          val value_to_float : value -> float

                                          TODO

                                          val is_arr : value -> bool

                                          TODO

                                          val is_elt : value -> bool

                                          TODO

                                          +Device (owl-base.Owl_computation_shape.Make.Type.Device)

                                          Module Type.Device

                                          Type definition
                                          type device

                                          TODO

                                          type value

                                          TODO

                                          Core functions
                                          val make_device : unit -> device

                                          TODO

                                          val arr_to_value : A.arr -> value

                                          TODO

                                          val value_to_arr : value -> A.arr

                                          TODO

                                          val elt_to_value : A.elt -> value

                                          TODO

                                          val value_to_elt : value -> A.elt

                                          TODO

                                          val value_to_float : value -> float

                                          TODO

                                          val is_arr : value -> bool

                                          TODO

                                          val is_elt : value -> bool

                                          TODO

                                          diff --git a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/index.html b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/index.html index 5ca6facfb..d8b69dc30 100644 --- a/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/index.html +++ b/docs/owl-base/Owl_computation_shape/Make/argument-1-Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_shape.Make.Type)

                                          Parameter Make.Type

                                          Type definition
                                          type state =
                                          1. | Valid
                                          2. | Invalid
                                            (*

                                            TODO

                                            *)

                                          TODO

                                          and block = {
                                          1. size : int;
                                          2. block_id : int;
                                          3. mutable active : t option;
                                          4. mutable memory : Device.value;
                                          5. mutable nodes : t list;
                                          }

                                          block type keeps a reference to a block of memory and to the nodes sharing that block.

                                          and attr = {
                                          1. mutable op : op;
                                          2. mutable freeze : bool;
                                          3. mutable reuse : bool;
                                          4. mutable state : state;
                                          5. mutable shape : int array option array;
                                          6. mutable value : Device.value array;
                                          7. mutable block : block array option;
                                          }

                                          TODO

                                          and arr =
                                          1. | Arr of t
                                          and elt =
                                          1. | Elt of t
                                          and op =
                                          1. | Noop
                                          2. | Var
                                          3. | Const
                                          4. | Empty of int array
                                          5. | Zeros of int array
                                          6. | Ones of int array
                                          7. | Create of int array
                                          8. | Sequential of int array
                                          9. | Uniform of int array
                                          10. | Gaussian of int array
                                          11. | Bernoulli of int array
                                          12. | Init of int array * int -> elt
                                          13. | Get of int array
                                          14. | Set of int array
                                          15. | GetSlice of int list list
                                          16. | SetSlice of int list list
                                          17. | GetFancy of Owl_types_common.index list
                                          18. | SetFancy of Owl_types_common.index list
                                          19. | Copy
                                          20. | Reset
                                          21. | Reshape of int array
                                          22. | Reverse
                                          23. | Tile of int array
                                          24. | Repeat of int array
                                          25. | Pad of elt * int list list
                                          26. | Concatenate of int
                                          27. | Stack of int
                                          28. | Split of int * int array
                                          29. | Draw of int * int
                                          30. | Map of elt -> elt
                                          31. | Fold of int * elt -> elt -> elt
                                          32. | Scan of int * elt -> elt -> elt
                                          33. | OneHot of int
                                          34. | OfArray of int array
                                          35. | Delay of Device.A.arr -> Device.A.arr
                                          36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                          37. | LazyPrint of int option +Type (owl-base.Owl_computation_shape.Make.Type)

                                            Parameter Make.Type

                                            Type definition
                                            type state =
                                            1. | Valid
                                            2. | Invalid
                                              (*

                                              TODO

                                              *)

                                            TODO

                                            and block = {
                                            1. size : int;
                                            2. block_id : int;
                                            3. mutable active : t option;
                                            4. mutable memory : Device.value;
                                            5. mutable nodes : t list;
                                            }

                                            block type keeps a reference to a block of memory and to the nodes sharing that block.

                                            and attr = {
                                            1. mutable op : op;
                                            2. mutable freeze : bool;
                                            3. mutable reuse : bool;
                                            4. mutable state : state;
                                            5. mutable shape : int array option array;
                                            6. mutable value : Device.value array;
                                            7. mutable block : block array option;
                                            }

                                            TODO

                                            and arr =
                                            1. | Arr of t
                                            and elt =
                                            1. | Elt of t
                                            and op =
                                            1. | Noop
                                            2. | Var
                                            3. | Const
                                            4. | Empty of int array
                                            5. | Zeros of int array
                                            6. | Ones of int array
                                            7. | Create of int array
                                            8. | Sequential of int array
                                            9. | Uniform of int array
                                            10. | Gaussian of int array
                                            11. | Bernoulli of int array
                                            12. | Init of int array * int -> elt
                                            13. | Get of int array
                                            14. | Set of int array
                                            15. | GetSlice of int list list
                                            16. | SetSlice of int list list
                                            17. | GetFancy of Owl_types_common.index list
                                            18. | SetFancy of Owl_types_common.index list
                                            19. | Copy
                                            20. | Reset
                                            21. | Reshape of int array
                                            22. | Reverse
                                            23. | Tile of int array
                                            24. | Repeat of int array
                                            25. | Pad of elt * int list list
                                            26. | Concatenate of int
                                            27. | Stack of int
                                            28. | Split of int * int array
                                            29. | Draw of int * int
                                            30. | Map of elt -> elt
                                            31. | Fold of int * elt -> elt -> elt
                                            32. | Scan of int * elt -> elt -> elt
                                            33. | OneHot of int
                                            34. | OfArray of int array
                                            35. | Delay of Device.A.arr -> Device.A.arr
                                            36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                            37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                            38. | Abs
                                            39. | Neg
                                            40. | Floor
                                            41. | Ceil
                                            42. | Round
                                            43. | Sqr
                                            44. | Sqrt
                                            45. | Log
                                            46. | Log2
                                            47. | Log10
                                            48. | Exp
                                            49. | Sin
                                            50. | Cos
                                            51. | Tan
                                            52. | Sinh
                                            53. | Cosh
                                            54. | Tanh
                                            55. | Asin
                                            56. | Acos
                                            57. | Atan
                                            58. | Asinh
                                            59. | Acosh
                                            60. | Atanh
                                            61. | Min of bool * int
                                            62. | Max of bool * int
                                            63. | Sum of bool * int
                                            64. | SumReduce of int array
                                            65. | Signum
                                            66. | Sigmoid
                                            67. | Relu
                                            68. | Dawsn
                                            69. | Min'
                                            70. | Max'
                                            71. | Sum'
                                            72. | LogSumExp'
                                            73. | LogSumExp of bool * int
                                            74. | L1norm'
                                            75. | L2norm'
                                            76. | L2NormSqr'
                                            77. | ClipByValue
                                            78. | ClipByL2norm
                                            79. | Pow
                                            80. | ScalarPow
                                            81. | PowScalar
                                            82. | Atan2
                                            83. | ScalarAtan2
                                            84. | Atan2Scalar
                                            85. | Hypot
                                            86. | Min2
                                            87. | Max2
                                            88. | Add
                                            89. | Sub
                                            90. | Mul
                                            91. | Div
                                            92. | AddScalar
                                            93. | SubScalar
                                            94. | MulScalar
                                            95. | DivScalar
                                            96. | ScalarAdd
                                            97. | ScalarSub
                                            98. | ScalarMul
                                            99. | ScalarDiv
                                            100. | FMA
                                            101. | EltEqual
                                            102. | EltNotEqual
                                            103. | EltLess
                                            104. | EltGreater
                                            105. | EltLessEqual
                                            106. | EltGreaterEqual
                                            107. | EltEqualScalar
                                            108. | EltNotEqualScalar
                                            109. | EltLessScalar
                                            110. | EltGreaterScalar
                                            111. | EltLessEqualScalar
                                            112. | EltGreaterEqualScalar
                                            113. | Conv1d of Owl_types_common.padding * int array
                                            114. | Conv2d of Owl_types_common.padding * int array
                                            115. | Conv3d of Owl_types_common.padding * int array
                                            116. | TransposeConv1d of Owl_types_common.padding * int array
                                            117. | TransposeConv2d of Owl_types_common.padding * int array
                                            118. | TransposeConv3d of Owl_types_common.padding * int array
                                            119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                            120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                            121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                            122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                            123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                            124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                            125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                            126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                            127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                            128. | UpSampling2d of int array
                                            129. | Conv1dBackwardInput of int array
                                            130. | Conv1dBackwardKernel of int array
                                            131. | Conv2dBackwardInput of int array
                                            132. | Conv2dBackwardKernel of int array
                                            133. | Conv3dBackwardInput of int array
                                            134. | Conv3dBackwardKernel of int array
                                            135. | TransposeConv1dBackwardInput of int array
                                            136. | TransposeConv1dBackwardKernel of int array
                                            137. | TransposeConv2dBackwardInput of int array
                                            138. | TransposeConv2dBackwardKernel of int array
                                            139. | TransposeConv3dBackwardInput of int array
                                            140. | TransposeConv3dBackwardKernel of int array
                                            141. | DilatedConv1dBackwardInput of int array * int array
                                            142. | DilatedConv1dBackwardKernel of int array * int array
                                            143. | DilatedConv2dBackwardInput of int array * int array
                                            144. | DilatedConv2dBackwardKernel of int array * int array
                                            145. | DilatedConv3dBackwardInput of int array * int array
                                            146. | DilatedConv3dBackwardKernel of int array * int array
                                            147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                            148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                            149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                            150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                            151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                            152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                            153. | UpSampling2dBackward of int array
                                            154. | RowNum
                                            155. | ColNum
                                            156. | Row
                                            157. | Rows of int array
                                            158. | CopyRowTo
                                            159. | CopyColTo
                                            160. | Dot of bool * bool * elt * elt
                                            161. | Inv
                                            162. | Trace
                                            163. | Transpose of int array
                                            164. | ToRows
                                            165. | OfRows
                                            166. | Scalar_Add
                                            167. | Scalar_Sub
                                            168. | Scalar_Mul
                                            169. | Scalar_Div
                                            170. | Scalar_Pow
                                            171. | Scalar_Atan2
                                            172. | Scalar_Abs
                                            173. | Scalar_Neg
                                            174. | Scalar_Sqr
                                            175. | Scalar_Sqrt
                                            176. | Scalar_Exp
                                            177. | Scalar_Log
                                            178. | Scalar_Log2
                                            179. | Scalar_Log10
                                            180. | Scalar_Signum
                                            181. | Scalar_Floor
                                            182. | Scalar_Ceil
                                            183. | Scalar_Round
                                            184. | Scalar_Sin
                                            185. | Scalar_Cos
                                            186. | Scalar_Tan
                                            187. | Scalar_Sinh
                                            188. | Scalar_Cosh
                                            189. | Scalar_Tanh
                                            190. | Scalar_Asin
                                            191. | Scalar_Acos
                                            192. | Scalar_Atan
                                            193. | Scalar_Asinh
                                            194. | Scalar_Acosh
                                            195. | Scalar_Atanh
                                            196. | Scalar_Relu
                                            197. | Scalar_Dawsn
                                            198. | Scalar_Sigmoid
                                            199. | Fused_Adagrad of float * float
                                              (*

                                              TODO

                                              *)
                                            diff --git a/docs/owl-base/Owl_computation_shape/Make/index.html b/docs/owl-base/Owl_computation_shape/Make/index.html index c88b123b9..98843dcf6 100644 --- a/docs/owl-base/Owl_computation_shape/Make/index.html +++ b/docs/owl-base/Owl_computation_shape/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_computation_shape.Make)

                                            Module Owl_computation_shape.Make

                                            Parameters

                                            Signature

                                            module Type = Type
                                            val _infer_shape_00 : 'a -> 'b array option array
                                            val _infer_shape_01 : 'a array option array array -> 'a array option array
                                            val _infer_shape_02 : 'a array option array array -> 'a array option array
                                            val _infer_shape_03 : int array option array array -> int array option array
                                            val _infer_shape_04 : +Make (owl-base.Owl_computation_shape.Make)

                                            Module Owl_computation_shape.Make

                                            Parameters

                                            Signature

                                            module Type = Type
                                            val _infer_shape_00 : 'a -> 'b array option array
                                            val _infer_shape_01 : 'a array option array array -> 'a array option array
                                            val _infer_shape_02 : 'a array option array array -> 'a array option array
                                            val _infer_shape_03 : int array option array array -> int array option array
                                            val _infer_shape_04 : int array option array array -> int -> int array option array
                                            val _infer_shape_05 : diff --git a/docs/owl-base/Owl_computation_shape/index.html b/docs/owl-base/Owl_computation_shape/index.html index 9d58d9f76..ce07dcbdd 100644 --- a/docs/owl-base/Owl_computation_shape/index.html +++ b/docs/owl-base/Owl_computation_shape/index.html @@ -1,2 +1,2 @@ -Owl_computation_shape (owl-base.Owl_computation_shape)

                                            Module Owl_computation_shape

                                            module Make (Type : Owl_computation_type_sig.Sig) : sig ... end
                                            +Owl_computation_shape (owl-base.Owl_computation_shape)

                                            Module Owl_computation_shape

                                            module Make (Type : Owl_computation_type_sig.Sig) : sig ... end
                                            diff --git a/docs/owl-base/Owl_computation_shape_sig/index.html b/docs/owl-base/Owl_computation_shape_sig/index.html index df7cbd21e..6fcfaab29 100644 --- a/docs/owl-base/Owl_computation_shape_sig/index.html +++ b/docs/owl-base/Owl_computation_shape_sig/index.html @@ -1,2 +1,2 @@ -Owl_computation_shape_sig (owl-base.Owl_computation_shape_sig)

                                            Module Owl_computation_shape_sig

                                            module type Sig = sig ... end
                                            +Owl_computation_shape_sig (owl-base.Owl_computation_shape_sig)

                                            Module Owl_computation_shape_sig

                                            module type Sig = sig ... end
                                            diff --git a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Linalg/index.html index 8ed5e898b..a5d7e5fd3 100644 --- a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_shape_sig.Sig.Type.Device.A.Linalg)

                                            Module A.Linalg

                                            val inv : arr -> arr
                                            val logdet : arr -> elt
                                            val chol : ?upper:bool -> arr -> arr
                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                            val qr : arr -> arr * arr
                                            val lq : arr -> arr * arr
                                            val sylvester : arr -> arr -> arr -> arr
                                            val lyapunov : arr -> arr -> arr
                                            val discrete_lyapunov : +Linalg (owl-base.Owl_computation_shape_sig.Sig.Type.Device.A.Linalg)

                                            Module A.Linalg

                                            val inv : arr -> arr
                                            val logdet : arr -> elt
                                            val chol : ?upper:bool -> arr -> arr
                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                            val qr : arr -> arr * arr
                                            val lq : arr -> arr * arr
                                            val sylvester : arr -> arr -> arr -> arr
                                            val lyapunov : arr -> arr -> arr
                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Mat/index.html index a5c006690..c8f63a1e7 100644 --- a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_shape_sig.Sig.Type.Device.A.Mat)

                                            Module A.Mat

                                            val diagm : ?k:int -> arr -> arr
                                            val triu : ?k:int -> arr -> arr
                                            val tril : ?k:int -> arr -> arr
                                            val eye : int -> arr
                                            +Mat (owl-base.Owl_computation_shape_sig.Sig.Type.Device.A.Mat)

                                            Module A.Mat

                                            val diagm : ?k:int -> arr -> arr
                                            val triu : ?k:int -> arr -> arr
                                            val tril : ?k:int -> arr -> arr
                                            val eye : int -> arr
                                            diff --git a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Scalar/index.html index 1c8b47ef8..cc035a331 100644 --- a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_shape_sig.Sig.Type.Device.A.Scalar)

                                            Module A.Scalar

                                            val add : elt -> elt -> elt
                                            val sub : elt -> elt -> elt
                                            val mul : elt -> elt -> elt
                                            val div : elt -> elt -> elt
                                            val pow : elt -> elt -> elt
                                            val atan2 : elt -> elt -> elt
                                            val abs : elt -> elt
                                            val neg : elt -> elt
                                            val sqr : elt -> elt
                                            val sqrt : elt -> elt
                                            val exp : elt -> elt
                                            val log : elt -> elt
                                            val log2 : elt -> elt
                                            val log10 : elt -> elt
                                            val signum : elt -> elt
                                            val floor : elt -> elt
                                            val ceil : elt -> elt
                                            val round : elt -> elt
                                            val sin : elt -> elt
                                            val cos : elt -> elt
                                            val tan : elt -> elt
                                            val sinh : elt -> elt
                                            val cosh : elt -> elt
                                            val tanh : elt -> elt
                                            val asin : elt -> elt
                                            val acos : elt -> elt
                                            val atan : elt -> elt
                                            val asinh : elt -> elt
                                            val acosh : elt -> elt
                                            val atanh : elt -> elt
                                            val relu : elt -> elt
                                            val dawsn : elt -> elt
                                            val sigmoid : elt -> elt
                                            +Scalar (owl-base.Owl_computation_shape_sig.Sig.Type.Device.A.Scalar)

                                            Module A.Scalar

                                            val add : elt -> elt -> elt
                                            val sub : elt -> elt -> elt
                                            val mul : elt -> elt -> elt
                                            val div : elt -> elt -> elt
                                            val pow : elt -> elt -> elt
                                            val atan2 : elt -> elt -> elt
                                            val abs : elt -> elt
                                            val neg : elt -> elt
                                            val sqr : elt -> elt
                                            val sqrt : elt -> elt
                                            val exp : elt -> elt
                                            val log : elt -> elt
                                            val log2 : elt -> elt
                                            val log10 : elt -> elt
                                            val signum : elt -> elt
                                            val floor : elt -> elt
                                            val ceil : elt -> elt
                                            val round : elt -> elt
                                            val sin : elt -> elt
                                            val cos : elt -> elt
                                            val tan : elt -> elt
                                            val sinh : elt -> elt
                                            val cosh : elt -> elt
                                            val tanh : elt -> elt
                                            val asin : elt -> elt
                                            val acos : elt -> elt
                                            val atan : elt -> elt
                                            val asinh : elt -> elt
                                            val acosh : elt -> elt
                                            val atanh : elt -> elt
                                            val relu : elt -> elt
                                            val dawsn : elt -> elt
                                            val sigmoid : elt -> elt
                                            diff --git a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/index.html b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/index.html index 91a23fa0a..55770d5fc 100644 --- a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_shape_sig.Sig.Type.Device.A)

                                            Module Device.A

                                            include Owl_types_ndarray_algodiff.Sig
                                            include Owl_types_ndarray_eltcmp.Sig
                                            include Owl_types_ndarray_basic.Sig
                                            type arr
                                            type elt
                                            val empty : int array -> arr
                                            val zeros : int array -> arr
                                            val ones : int array -> arr
                                            val create : int array -> elt -> arr
                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                            val bernoulli : ?p:elt -> int array -> arr
                                            val init : int array -> (int -> elt) -> arr
                                            val init_nd : int array -> (int array -> elt) -> arr
                                            val shape : arr -> int array
                                            val numel : arr -> int
                                            val get : arr -> int array -> elt
                                            val set : arr -> int array -> elt -> unit
                                            val get_slice : int list list -> arr -> arr
                                            val set_slice : int list list -> arr -> arr -> unit
                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                            val copy : arr -> arr
                                            val copy_ : out:arr -> arr -> unit
                                            val reset : arr -> unit
                                            val reshape : arr -> int array -> arr
                                            val reverse : arr -> arr
                                            val tile : arr -> int array -> arr
                                            val repeat : arr -> int array -> arr
                                            val concatenate : ?axis:int -> arr array -> arr
                                            val stack : ?axis:int -> arr array -> arr
                                            val split : ?axis:int -> int array -> arr -> arr array
                                            val expand : ?hi:bool -> arr -> int -> arr
                                            val squeeze : ?axis:int array -> arr -> arr
                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                            val map : (elt -> elt) -> arr -> arr
                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                            val one_hot : int -> arr -> arr
                                            val pad : ?v:elt -> int list list -> arr -> arr
                                            val print : +A (owl-base.Owl_computation_shape_sig.Sig.Type.Device.A)

                                            Module Device.A

                                            include Owl_types_ndarray_algodiff.Sig
                                            include Owl_types_ndarray_eltcmp.Sig
                                            include Owl_types_ndarray_basic.Sig
                                            type arr
                                            type elt
                                            val empty : int array -> arr
                                            val zeros : int array -> arr
                                            val ones : int array -> arr
                                            val create : int array -> elt -> arr
                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                            val bernoulli : ?p:elt -> int array -> arr
                                            val init : int array -> (int -> elt) -> arr
                                            val init_nd : int array -> (int array -> elt) -> arr
                                            val shape : arr -> int array
                                            val numel : arr -> int
                                            val get : arr -> int array -> elt
                                            val set : arr -> int array -> elt -> unit
                                            val get_slice : int list list -> arr -> arr
                                            val set_slice : int list list -> arr -> arr -> unit
                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                            val copy : arr -> arr
                                            val copy_ : out:arr -> arr -> unit
                                            val reset : arr -> unit
                                            val reshape : arr -> int array -> arr
                                            val reverse : arr -> arr
                                            val tile : arr -> int array -> arr
                                            val repeat : arr -> int array -> arr
                                            val concatenate : ?axis:int -> arr array -> arr
                                            val stack : ?axis:int -> arr array -> arr
                                            val split : ?axis:int -> int array -> arr -> arr array
                                            val expand : ?hi:bool -> arr -> int -> arr
                                            val squeeze : ?axis:int array -> arr -> arr
                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                            val map : (elt -> elt) -> arr -> arr
                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                            val one_hot : int -> arr -> arr
                                            val pad : ?v:elt -> int list list -> arr -> arr
                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/index.html b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/index.html index ab61afbb6..3aa2e2b28 100644 --- a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_shape_sig.Sig.Type.Device)

                                            Module Type.Device

                                            Type definition
                                            type device

                                            TODO

                                            type value

                                            TODO

                                            Core functions
                                            val make_device : unit -> device

                                            TODO

                                            val arr_to_value : A.arr -> value

                                            TODO

                                            val value_to_arr : value -> A.arr

                                            TODO

                                            val elt_to_value : A.elt -> value

                                            TODO

                                            val value_to_elt : value -> A.elt

                                            TODO

                                            val value_to_float : value -> float

                                            TODO

                                            val is_arr : value -> bool

                                            TODO

                                            val is_elt : value -> bool

                                            TODO

                                            +Device (owl-base.Owl_computation_shape_sig.Sig.Type.Device)

                                            Module Type.Device

                                            Type definition
                                            type device

                                            TODO

                                            type value

                                            TODO

                                            Core functions
                                            val make_device : unit -> device

                                            TODO

                                            val arr_to_value : A.arr -> value

                                            TODO

                                            val value_to_arr : value -> A.arr

                                            TODO

                                            val elt_to_value : A.elt -> value

                                            TODO

                                            val value_to_elt : value -> A.elt

                                            TODO

                                            val value_to_float : value -> float

                                            TODO

                                            val is_arr : value -> bool

                                            TODO

                                            val is_elt : value -> bool

                                            TODO

                                            diff --git a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/index.html b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/index.html index 0cf66e621..77973d5d5 100644 --- a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/index.html +++ b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_shape_sig.Sig.Type)

                                            Module Sig.Type

                                            Type definition
                                            type state =
                                            1. | Valid
                                            2. | Invalid
                                              (*

                                              TODO

                                              *)

                                            TODO

                                            and block = {
                                            1. size : int;
                                            2. block_id : int;
                                            3. mutable active : t option;
                                            4. mutable memory : Device.value;
                                            5. mutable nodes : t list;
                                            }

                                            block type keeps a reference to a block of memory and to the nodes sharing that block.

                                            and attr = {
                                            1. mutable op : op;
                                            2. mutable freeze : bool;
                                            3. mutable reuse : bool;
                                            4. mutable state : state;
                                            5. mutable shape : int array option array;
                                            6. mutable value : Device.value array;
                                            7. mutable block : block array option;
                                            }

                                            TODO

                                            and arr =
                                            1. | Arr of t
                                            and elt =
                                            1. | Elt of t
                                            and op =
                                            1. | Noop
                                            2. | Var
                                            3. | Const
                                            4. | Empty of int array
                                            5. | Zeros of int array
                                            6. | Ones of int array
                                            7. | Create of int array
                                            8. | Sequential of int array
                                            9. | Uniform of int array
                                            10. | Gaussian of int array
                                            11. | Bernoulli of int array
                                            12. | Init of int array * int -> elt
                                            13. | Get of int array
                                            14. | Set of int array
                                            15. | GetSlice of int list list
                                            16. | SetSlice of int list list
                                            17. | GetFancy of Owl_types_common.index list
                                            18. | SetFancy of Owl_types_common.index list
                                            19. | Copy
                                            20. | Reset
                                            21. | Reshape of int array
                                            22. | Reverse
                                            23. | Tile of int array
                                            24. | Repeat of int array
                                            25. | Pad of elt * int list list
                                            26. | Concatenate of int
                                            27. | Stack of int
                                            28. | Split of int * int array
                                            29. | Draw of int * int
                                            30. | Map of elt -> elt
                                            31. | Fold of int * elt -> elt -> elt
                                            32. | Scan of int * elt -> elt -> elt
                                            33. | OneHot of int
                                            34. | OfArray of int array
                                            35. | Delay of Device.A.arr -> Device.A.arr
                                            36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                            37. | LazyPrint of int option +Type (owl-base.Owl_computation_shape_sig.Sig.Type)

                                              Module Sig.Type

                                              Type definition
                                              type state =
                                              1. | Valid
                                              2. | Invalid
                                                (*

                                                TODO

                                                *)

                                              TODO

                                              and block = {
                                              1. size : int;
                                              2. block_id : int;
                                              3. mutable active : t option;
                                              4. mutable memory : Device.value;
                                              5. mutable nodes : t list;
                                              }

                                              block type keeps a reference to a block of memory and to the nodes sharing that block.

                                              and attr = {
                                              1. mutable op : op;
                                              2. mutable freeze : bool;
                                              3. mutable reuse : bool;
                                              4. mutable state : state;
                                              5. mutable shape : int array option array;
                                              6. mutable value : Device.value array;
                                              7. mutable block : block array option;
                                              }

                                              TODO

                                              and arr =
                                              1. | Arr of t
                                              and elt =
                                              1. | Elt of t
                                              and op =
                                              1. | Noop
                                              2. | Var
                                              3. | Const
                                              4. | Empty of int array
                                              5. | Zeros of int array
                                              6. | Ones of int array
                                              7. | Create of int array
                                              8. | Sequential of int array
                                              9. | Uniform of int array
                                              10. | Gaussian of int array
                                              11. | Bernoulli of int array
                                              12. | Init of int array * int -> elt
                                              13. | Get of int array
                                              14. | Set of int array
                                              15. | GetSlice of int list list
                                              16. | SetSlice of int list list
                                              17. | GetFancy of Owl_types_common.index list
                                              18. | SetFancy of Owl_types_common.index list
                                              19. | Copy
                                              20. | Reset
                                              21. | Reshape of int array
                                              22. | Reverse
                                              23. | Tile of int array
                                              24. | Repeat of int array
                                              25. | Pad of elt * int list list
                                              26. | Concatenate of int
                                              27. | Stack of int
                                              28. | Split of int * int array
                                              29. | Draw of int * int
                                              30. | Map of elt -> elt
                                              31. | Fold of int * elt -> elt -> elt
                                              32. | Scan of int * elt -> elt -> elt
                                              33. | OneHot of int
                                              34. | OfArray of int array
                                              35. | Delay of Device.A.arr -> Device.A.arr
                                              36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                              37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                              38. | Abs
                                              39. | Neg
                                              40. | Floor
                                              41. | Ceil
                                              42. | Round
                                              43. | Sqr
                                              44. | Sqrt
                                              45. | Log
                                              46. | Log2
                                              47. | Log10
                                              48. | Exp
                                              49. | Sin
                                              50. | Cos
                                              51. | Tan
                                              52. | Sinh
                                              53. | Cosh
                                              54. | Tanh
                                              55. | Asin
                                              56. | Acos
                                              57. | Atan
                                              58. | Asinh
                                              59. | Acosh
                                              60. | Atanh
                                              61. | Min of bool * int
                                              62. | Max of bool * int
                                              63. | Sum of bool * int
                                              64. | SumReduce of int array
                                              65. | Signum
                                              66. | Sigmoid
                                              67. | Relu
                                              68. | Dawsn
                                              69. | Min'
                                              70. | Max'
                                              71. | Sum'
                                              72. | LogSumExp'
                                              73. | LogSumExp of bool * int
                                              74. | L1norm'
                                              75. | L2norm'
                                              76. | L2NormSqr'
                                              77. | ClipByValue
                                              78. | ClipByL2norm
                                              79. | Pow
                                              80. | ScalarPow
                                              81. | PowScalar
                                              82. | Atan2
                                              83. | ScalarAtan2
                                              84. | Atan2Scalar
                                              85. | Hypot
                                              86. | Min2
                                              87. | Max2
                                              88. | Add
                                              89. | Sub
                                              90. | Mul
                                              91. | Div
                                              92. | AddScalar
                                              93. | SubScalar
                                              94. | MulScalar
                                              95. | DivScalar
                                              96. | ScalarAdd
                                              97. | ScalarSub
                                              98. | ScalarMul
                                              99. | ScalarDiv
                                              100. | FMA
                                              101. | EltEqual
                                              102. | EltNotEqual
                                              103. | EltLess
                                              104. | EltGreater
                                              105. | EltLessEqual
                                              106. | EltGreaterEqual
                                              107. | EltEqualScalar
                                              108. | EltNotEqualScalar
                                              109. | EltLessScalar
                                              110. | EltGreaterScalar
                                              111. | EltLessEqualScalar
                                              112. | EltGreaterEqualScalar
                                              113. | Conv1d of Owl_types_common.padding * int array
                                              114. | Conv2d of Owl_types_common.padding * int array
                                              115. | Conv3d of Owl_types_common.padding * int array
                                              116. | TransposeConv1d of Owl_types_common.padding * int array
                                              117. | TransposeConv2d of Owl_types_common.padding * int array
                                              118. | TransposeConv3d of Owl_types_common.padding * int array
                                              119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                              120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                              121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                              122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                              123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                              124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                              125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                              126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                              127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                              128. | UpSampling2d of int array
                                              129. | Conv1dBackwardInput of int array
                                              130. | Conv1dBackwardKernel of int array
                                              131. | Conv2dBackwardInput of int array
                                              132. | Conv2dBackwardKernel of int array
                                              133. | Conv3dBackwardInput of int array
                                              134. | Conv3dBackwardKernel of int array
                                              135. | TransposeConv1dBackwardInput of int array
                                              136. | TransposeConv1dBackwardKernel of int array
                                              137. | TransposeConv2dBackwardInput of int array
                                              138. | TransposeConv2dBackwardKernel of int array
                                              139. | TransposeConv3dBackwardInput of int array
                                              140. | TransposeConv3dBackwardKernel of int array
                                              141. | DilatedConv1dBackwardInput of int array * int array
                                              142. | DilatedConv1dBackwardKernel of int array * int array
                                              143. | DilatedConv2dBackwardInput of int array * int array
                                              144. | DilatedConv2dBackwardKernel of int array * int array
                                              145. | DilatedConv3dBackwardInput of int array * int array
                                              146. | DilatedConv3dBackwardKernel of int array * int array
                                              147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                              148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                              149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                              150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                              151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                              152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                              153. | UpSampling2dBackward of int array
                                              154. | RowNum
                                              155. | ColNum
                                              156. | Row
                                              157. | Rows of int array
                                              158. | CopyRowTo
                                              159. | CopyColTo
                                              160. | Dot of bool * bool * elt * elt
                                              161. | Inv
                                              162. | Trace
                                              163. | Transpose of int array
                                              164. | ToRows
                                              165. | OfRows
                                              166. | Scalar_Add
                                              167. | Scalar_Sub
                                              168. | Scalar_Mul
                                              169. | Scalar_Div
                                              170. | Scalar_Pow
                                              171. | Scalar_Atan2
                                              172. | Scalar_Abs
                                              173. | Scalar_Neg
                                              174. | Scalar_Sqr
                                              175. | Scalar_Sqrt
                                              176. | Scalar_Exp
                                              177. | Scalar_Log
                                              178. | Scalar_Log2
                                              179. | Scalar_Log10
                                              180. | Scalar_Signum
                                              181. | Scalar_Floor
                                              182. | Scalar_Ceil
                                              183. | Scalar_Round
                                              184. | Scalar_Sin
                                              185. | Scalar_Cos
                                              186. | Scalar_Tan
                                              187. | Scalar_Sinh
                                              188. | Scalar_Cosh
                                              189. | Scalar_Tanh
                                              190. | Scalar_Asin
                                              191. | Scalar_Acos
                                              192. | Scalar_Atan
                                              193. | Scalar_Asinh
                                              194. | Scalar_Acosh
                                              195. | Scalar_Atanh
                                              196. | Scalar_Relu
                                              197. | Scalar_Dawsn
                                              198. | Scalar_Sigmoid
                                              199. | Fused_Adagrad of float * float
                                                (*

                                                TODO

                                                *)
                                              diff --git a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/index.html b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/index.html index 84bf1fdff..005a6c9a1 100644 --- a/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_computation_shape_sig/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_computation_shape_sig.Sig)

                                              Module type Owl_computation_shape_sig.Sig

                                              Core functions
                                              val infer_shape : +Sig (owl-base.Owl_computation_shape_sig.Sig)

                                              Module type Owl_computation_shape_sig.Sig

                                              Core functions
                                              val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                              TODO

                                              diff --git a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Linalg/index.html index 5d2b34999..ad70d7d8d 100644 --- a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_symbol.Make.Shape.Type.Device.A.Linalg)

                                              Module A.Linalg

                                              val inv : arr -> arr
                                              val logdet : arr -> elt
                                              val chol : ?upper:bool -> arr -> arr
                                              val svd : ?thin:bool -> arr -> arr * arr * arr
                                              val qr : arr -> arr * arr
                                              val lq : arr -> arr * arr
                                              val sylvester : arr -> arr -> arr -> arr
                                              val lyapunov : arr -> arr -> arr
                                              val discrete_lyapunov : +Linalg (owl-base.Owl_computation_symbol.Make.Shape.Type.Device.A.Linalg)

                                              Module A.Linalg

                                              val inv : arr -> arr
                                              val logdet : arr -> elt
                                              val chol : ?upper:bool -> arr -> arr
                                              val svd : ?thin:bool -> arr -> arr * arr * arr
                                              val qr : arr -> arr * arr
                                              val lq : arr -> arr * arr
                                              val sylvester : arr -> arr -> arr -> arr
                                              val lyapunov : arr -> arr -> arr
                                              val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Mat/index.html index 056992269..57c18ca63 100644 --- a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_symbol.Make.Shape.Type.Device.A.Mat)

                                              Module A.Mat

                                              val diagm : ?k:int -> arr -> arr
                                              val triu : ?k:int -> arr -> arr
                                              val tril : ?k:int -> arr -> arr
                                              val eye : int -> arr
                                              +Mat (owl-base.Owl_computation_symbol.Make.Shape.Type.Device.A.Mat)

                                              Module A.Mat

                                              val diagm : ?k:int -> arr -> arr
                                              val triu : ?k:int -> arr -> arr
                                              val tril : ?k:int -> arr -> arr
                                              val eye : int -> arr
                                              diff --git a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Scalar/index.html index a2502a630..463c2e3cd 100644 --- a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_symbol.Make.Shape.Type.Device.A.Scalar)

                                              Module A.Scalar

                                              val add : elt -> elt -> elt
                                              val sub : elt -> elt -> elt
                                              val mul : elt -> elt -> elt
                                              val div : elt -> elt -> elt
                                              val pow : elt -> elt -> elt
                                              val atan2 : elt -> elt -> elt
                                              val abs : elt -> elt
                                              val neg : elt -> elt
                                              val sqr : elt -> elt
                                              val sqrt : elt -> elt
                                              val exp : elt -> elt
                                              val log : elt -> elt
                                              val log2 : elt -> elt
                                              val log10 : elt -> elt
                                              val signum : elt -> elt
                                              val floor : elt -> elt
                                              val ceil : elt -> elt
                                              val round : elt -> elt
                                              val sin : elt -> elt
                                              val cos : elt -> elt
                                              val tan : elt -> elt
                                              val sinh : elt -> elt
                                              val cosh : elt -> elt
                                              val tanh : elt -> elt
                                              val asin : elt -> elt
                                              val acos : elt -> elt
                                              val atan : elt -> elt
                                              val asinh : elt -> elt
                                              val acosh : elt -> elt
                                              val atanh : elt -> elt
                                              val relu : elt -> elt
                                              val dawsn : elt -> elt
                                              val sigmoid : elt -> elt
                                              +Scalar (owl-base.Owl_computation_symbol.Make.Shape.Type.Device.A.Scalar)

                                              Module A.Scalar

                                              val add : elt -> elt -> elt
                                              val sub : elt -> elt -> elt
                                              val mul : elt -> elt -> elt
                                              val div : elt -> elt -> elt
                                              val pow : elt -> elt -> elt
                                              val atan2 : elt -> elt -> elt
                                              val abs : elt -> elt
                                              val neg : elt -> elt
                                              val sqr : elt -> elt
                                              val sqrt : elt -> elt
                                              val exp : elt -> elt
                                              val log : elt -> elt
                                              val log2 : elt -> elt
                                              val log10 : elt -> elt
                                              val signum : elt -> elt
                                              val floor : elt -> elt
                                              val ceil : elt -> elt
                                              val round : elt -> elt
                                              val sin : elt -> elt
                                              val cos : elt -> elt
                                              val tan : elt -> elt
                                              val sinh : elt -> elt
                                              val cosh : elt -> elt
                                              val tanh : elt -> elt
                                              val asin : elt -> elt
                                              val acos : elt -> elt
                                              val atan : elt -> elt
                                              val asinh : elt -> elt
                                              val acosh : elt -> elt
                                              val atanh : elt -> elt
                                              val relu : elt -> elt
                                              val dawsn : elt -> elt
                                              val sigmoid : elt -> elt
                                              diff --git a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/index.html index 691714ce5..6171bb906 100644 --- a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_symbol.Make.Shape.Type.Device.A)

                                              Module Device.A

                                              include Owl_types_ndarray_algodiff.Sig
                                              include Owl_types_ndarray_eltcmp.Sig
                                              include Owl_types_ndarray_basic.Sig
                                              type arr
                                              type elt
                                              val empty : int array -> arr
                                              val zeros : int array -> arr
                                              val ones : int array -> arr
                                              val create : int array -> elt -> arr
                                              val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                              val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                              val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                              val bernoulli : ?p:elt -> int array -> arr
                                              val init : int array -> (int -> elt) -> arr
                                              val init_nd : int array -> (int array -> elt) -> arr
                                              val shape : arr -> int array
                                              val numel : arr -> int
                                              val get : arr -> int array -> elt
                                              val set : arr -> int array -> elt -> unit
                                              val get_slice : int list list -> arr -> arr
                                              val set_slice : int list list -> arr -> arr -> unit
                                              val get_fancy : Owl_types_common.index list -> arr -> arr
                                              val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                              val copy : arr -> arr
                                              val copy_ : out:arr -> arr -> unit
                                              val reset : arr -> unit
                                              val reshape : arr -> int array -> arr
                                              val reverse : arr -> arr
                                              val tile : arr -> int array -> arr
                                              val repeat : arr -> int array -> arr
                                              val concatenate : ?axis:int -> arr array -> arr
                                              val stack : ?axis:int -> arr array -> arr
                                              val split : ?axis:int -> int array -> arr -> arr array
                                              val expand : ?hi:bool -> arr -> int -> arr
                                              val squeeze : ?axis:int array -> arr -> arr
                                              val draw : ?axis:int -> arr -> int -> arr * int array
                                              val map : (elt -> elt) -> arr -> arr
                                              val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                              val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                              val one_hot : int -> arr -> arr
                                              val pad : ?v:elt -> int list list -> arr -> arr
                                              val print : +A (owl-base.Owl_computation_symbol.Make.Shape.Type.Device.A)

                                              Module Device.A

                                              include Owl_types_ndarray_algodiff.Sig
                                              include Owl_types_ndarray_eltcmp.Sig
                                              include Owl_types_ndarray_basic.Sig
                                              type arr
                                              type elt
                                              val empty : int array -> arr
                                              val zeros : int array -> arr
                                              val ones : int array -> arr
                                              val create : int array -> elt -> arr
                                              val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                              val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                              val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                              val bernoulli : ?p:elt -> int array -> arr
                                              val init : int array -> (int -> elt) -> arr
                                              val init_nd : int array -> (int array -> elt) -> arr
                                              val shape : arr -> int array
                                              val numel : arr -> int
                                              val get : arr -> int array -> elt
                                              val set : arr -> int array -> elt -> unit
                                              val get_slice : int list list -> arr -> arr
                                              val set_slice : int list list -> arr -> arr -> unit
                                              val get_fancy : Owl_types_common.index list -> arr -> arr
                                              val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                              val copy : arr -> arr
                                              val copy_ : out:arr -> arr -> unit
                                              val reset : arr -> unit
                                              val reshape : arr -> int array -> arr
                                              val reverse : arr -> arr
                                              val tile : arr -> int array -> arr
                                              val repeat : arr -> int array -> arr
                                              val concatenate : ?axis:int -> arr array -> arr
                                              val stack : ?axis:int -> arr array -> arr
                                              val split : ?axis:int -> int array -> arr -> arr array
                                              val expand : ?hi:bool -> arr -> int -> arr
                                              val squeeze : ?axis:int array -> arr -> arr
                                              val draw : ?axis:int -> arr -> int -> arr * int array
                                              val map : (elt -> elt) -> arr -> arr
                                              val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                              val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                              val one_hot : int -> arr -> arr
                                              val pad : ?v:elt -> int list list -> arr -> arr
                                              val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/index.html index 3f0253ada..108fd94c7 100644 --- a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_symbol.Make.Shape.Type.Device)

                                              Module Type.Device

                                              Type definition
                                              type device

                                              TODO

                                              type value

                                              TODO

                                              Core functions
                                              val make_device : unit -> device

                                              TODO

                                              val arr_to_value : A.arr -> value

                                              TODO

                                              val value_to_arr : value -> A.arr

                                              TODO

                                              val elt_to_value : A.elt -> value

                                              TODO

                                              val value_to_elt : value -> A.elt

                                              TODO

                                              val value_to_float : value -> float

                                              TODO

                                              val is_arr : value -> bool

                                              TODO

                                              val is_elt : value -> bool

                                              TODO

                                              +Device (owl-base.Owl_computation_symbol.Make.Shape.Type.Device)

                                              Module Type.Device

                                              Type definition
                                              type device

                                              TODO

                                              type value

                                              TODO

                                              Core functions
                                              val make_device : unit -> device

                                              TODO

                                              val arr_to_value : A.arr -> value

                                              TODO

                                              val value_to_arr : value -> A.arr

                                              TODO

                                              val elt_to_value : A.elt -> value

                                              TODO

                                              val value_to_elt : value -> A.elt

                                              TODO

                                              val value_to_float : value -> float

                                              TODO

                                              val is_arr : value -> bool

                                              TODO

                                              val is_elt : value -> bool

                                              TODO

                                              diff --git a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/index.html b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/index.html index ad6019f8d..439a7f188 100644 --- a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_symbol.Make.Shape.Type)

                                              Module Shape.Type

                                              Type definition
                                              type state =
                                              1. | Valid
                                              2. | Invalid
                                                (*

                                                TODO

                                                *)

                                              TODO

                                              and block = {
                                              1. size : int;
                                              2. block_id : int;
                                              3. mutable active : t option;
                                              4. mutable memory : Device.value;
                                              5. mutable nodes : t list;
                                              }

                                              block type keeps a reference to a block of memory and to the nodes sharing that block.

                                              and attr = {
                                              1. mutable op : op;
                                              2. mutable freeze : bool;
                                              3. mutable reuse : bool;
                                              4. mutable state : state;
                                              5. mutable shape : int array option array;
                                              6. mutable value : Device.value array;
                                              7. mutable block : block array option;
                                              }

                                              TODO

                                              and arr =
                                              1. | Arr of t
                                              and elt =
                                              1. | Elt of t
                                              and op =
                                              1. | Noop
                                              2. | Var
                                              3. | Const
                                              4. | Empty of int array
                                              5. | Zeros of int array
                                              6. | Ones of int array
                                              7. | Create of int array
                                              8. | Sequential of int array
                                              9. | Uniform of int array
                                              10. | Gaussian of int array
                                              11. | Bernoulli of int array
                                              12. | Init of int array * int -> elt
                                              13. | Get of int array
                                              14. | Set of int array
                                              15. | GetSlice of int list list
                                              16. | SetSlice of int list list
                                              17. | GetFancy of Owl_types_common.index list
                                              18. | SetFancy of Owl_types_common.index list
                                              19. | Copy
                                              20. | Reset
                                              21. | Reshape of int array
                                              22. | Reverse
                                              23. | Tile of int array
                                              24. | Repeat of int array
                                              25. | Pad of elt * int list list
                                              26. | Concatenate of int
                                              27. | Stack of int
                                              28. | Split of int * int array
                                              29. | Draw of int * int
                                              30. | Map of elt -> elt
                                              31. | Fold of int * elt -> elt -> elt
                                              32. | Scan of int * elt -> elt -> elt
                                              33. | OneHot of int
                                              34. | OfArray of int array
                                              35. | Delay of Device.A.arr -> Device.A.arr
                                              36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                              37. | LazyPrint of int option +Type (owl-base.Owl_computation_symbol.Make.Shape.Type)

                                                Module Shape.Type

                                                Type definition
                                                type state =
                                                1. | Valid
                                                2. | Invalid
                                                  (*

                                                  TODO

                                                  *)

                                                TODO

                                                and block = {
                                                1. size : int;
                                                2. block_id : int;
                                                3. mutable active : t option;
                                                4. mutable memory : Device.value;
                                                5. mutable nodes : t list;
                                                }

                                                block type keeps a reference to a block of memory and to the nodes sharing that block.

                                                and attr = {
                                                1. mutable op : op;
                                                2. mutable freeze : bool;
                                                3. mutable reuse : bool;
                                                4. mutable state : state;
                                                5. mutable shape : int array option array;
                                                6. mutable value : Device.value array;
                                                7. mutable block : block array option;
                                                }

                                                TODO

                                                and arr =
                                                1. | Arr of t
                                                and elt =
                                                1. | Elt of t
                                                and op =
                                                1. | Noop
                                                2. | Var
                                                3. | Const
                                                4. | Empty of int array
                                                5. | Zeros of int array
                                                6. | Ones of int array
                                                7. | Create of int array
                                                8. | Sequential of int array
                                                9. | Uniform of int array
                                                10. | Gaussian of int array
                                                11. | Bernoulli of int array
                                                12. | Init of int array * int -> elt
                                                13. | Get of int array
                                                14. | Set of int array
                                                15. | GetSlice of int list list
                                                16. | SetSlice of int list list
                                                17. | GetFancy of Owl_types_common.index list
                                                18. | SetFancy of Owl_types_common.index list
                                                19. | Copy
                                                20. | Reset
                                                21. | Reshape of int array
                                                22. | Reverse
                                                23. | Tile of int array
                                                24. | Repeat of int array
                                                25. | Pad of elt * int list list
                                                26. | Concatenate of int
                                                27. | Stack of int
                                                28. | Split of int * int array
                                                29. | Draw of int * int
                                                30. | Map of elt -> elt
                                                31. | Fold of int * elt -> elt -> elt
                                                32. | Scan of int * elt -> elt -> elt
                                                33. | OneHot of int
                                                34. | OfArray of int array
                                                35. | Delay of Device.A.arr -> Device.A.arr
                                                36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                                38. | Abs
                                                39. | Neg
                                                40. | Floor
                                                41. | Ceil
                                                42. | Round
                                                43. | Sqr
                                                44. | Sqrt
                                                45. | Log
                                                46. | Log2
                                                47. | Log10
                                                48. | Exp
                                                49. | Sin
                                                50. | Cos
                                                51. | Tan
                                                52. | Sinh
                                                53. | Cosh
                                                54. | Tanh
                                                55. | Asin
                                                56. | Acos
                                                57. | Atan
                                                58. | Asinh
                                                59. | Acosh
                                                60. | Atanh
                                                61. | Min of bool * int
                                                62. | Max of bool * int
                                                63. | Sum of bool * int
                                                64. | SumReduce of int array
                                                65. | Signum
                                                66. | Sigmoid
                                                67. | Relu
                                                68. | Dawsn
                                                69. | Min'
                                                70. | Max'
                                                71. | Sum'
                                                72. | LogSumExp'
                                                73. | LogSumExp of bool * int
                                                74. | L1norm'
                                                75. | L2norm'
                                                76. | L2NormSqr'
                                                77. | ClipByValue
                                                78. | ClipByL2norm
                                                79. | Pow
                                                80. | ScalarPow
                                                81. | PowScalar
                                                82. | Atan2
                                                83. | ScalarAtan2
                                                84. | Atan2Scalar
                                                85. | Hypot
                                                86. | Min2
                                                87. | Max2
                                                88. | Add
                                                89. | Sub
                                                90. | Mul
                                                91. | Div
                                                92. | AddScalar
                                                93. | SubScalar
                                                94. | MulScalar
                                                95. | DivScalar
                                                96. | ScalarAdd
                                                97. | ScalarSub
                                                98. | ScalarMul
                                                99. | ScalarDiv
                                                100. | FMA
                                                101. | EltEqual
                                                102. | EltNotEqual
                                                103. | EltLess
                                                104. | EltGreater
                                                105. | EltLessEqual
                                                106. | EltGreaterEqual
                                                107. | EltEqualScalar
                                                108. | EltNotEqualScalar
                                                109. | EltLessScalar
                                                110. | EltGreaterScalar
                                                111. | EltLessEqualScalar
                                                112. | EltGreaterEqualScalar
                                                113. | Conv1d of Owl_types_common.padding * int array
                                                114. | Conv2d of Owl_types_common.padding * int array
                                                115. | Conv3d of Owl_types_common.padding * int array
                                                116. | TransposeConv1d of Owl_types_common.padding * int array
                                                117. | TransposeConv2d of Owl_types_common.padding * int array
                                                118. | TransposeConv3d of Owl_types_common.padding * int array
                                                119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                                120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                                121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                                122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                                123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                                124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                                125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                                126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                                127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                                128. | UpSampling2d of int array
                                                129. | Conv1dBackwardInput of int array
                                                130. | Conv1dBackwardKernel of int array
                                                131. | Conv2dBackwardInput of int array
                                                132. | Conv2dBackwardKernel of int array
                                                133. | Conv3dBackwardInput of int array
                                                134. | Conv3dBackwardKernel of int array
                                                135. | TransposeConv1dBackwardInput of int array
                                                136. | TransposeConv1dBackwardKernel of int array
                                                137. | TransposeConv2dBackwardInput of int array
                                                138. | TransposeConv2dBackwardKernel of int array
                                                139. | TransposeConv3dBackwardInput of int array
                                                140. | TransposeConv3dBackwardKernel of int array
                                                141. | DilatedConv1dBackwardInput of int array * int array
                                                142. | DilatedConv1dBackwardKernel of int array * int array
                                                143. | DilatedConv2dBackwardInput of int array * int array
                                                144. | DilatedConv2dBackwardKernel of int array * int array
                                                145. | DilatedConv3dBackwardInput of int array * int array
                                                146. | DilatedConv3dBackwardKernel of int array * int array
                                                147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                                148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                                149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                                150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                                151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                                152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                                153. | UpSampling2dBackward of int array
                                                154. | RowNum
                                                155. | ColNum
                                                156. | Row
                                                157. | Rows of int array
                                                158. | CopyRowTo
                                                159. | CopyColTo
                                                160. | Dot of bool * bool * elt * elt
                                                161. | Inv
                                                162. | Trace
                                                163. | Transpose of int array
                                                164. | ToRows
                                                165. | OfRows
                                                166. | Scalar_Add
                                                167. | Scalar_Sub
                                                168. | Scalar_Mul
                                                169. | Scalar_Div
                                                170. | Scalar_Pow
                                                171. | Scalar_Atan2
                                                172. | Scalar_Abs
                                                173. | Scalar_Neg
                                                174. | Scalar_Sqr
                                                175. | Scalar_Sqrt
                                                176. | Scalar_Exp
                                                177. | Scalar_Log
                                                178. | Scalar_Log2
                                                179. | Scalar_Log10
                                                180. | Scalar_Signum
                                                181. | Scalar_Floor
                                                182. | Scalar_Ceil
                                                183. | Scalar_Round
                                                184. | Scalar_Sin
                                                185. | Scalar_Cos
                                                186. | Scalar_Tan
                                                187. | Scalar_Sinh
                                                188. | Scalar_Cosh
                                                189. | Scalar_Tanh
                                                190. | Scalar_Asin
                                                191. | Scalar_Acos
                                                192. | Scalar_Atan
                                                193. | Scalar_Asinh
                                                194. | Scalar_Acosh
                                                195. | Scalar_Atanh
                                                196. | Scalar_Relu
                                                197. | Scalar_Dawsn
                                                198. | Scalar_Sigmoid
                                                199. | Fused_Adagrad of float * float
                                                  (*

                                                  TODO

                                                  *)
                                                diff --git a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/index.html b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/index.html index 3e13d9558..6179f1ad2 100644 --- a/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/index.html +++ b/docs/owl-base/Owl_computation_symbol/Make/argument-1-Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_symbol.Make.Shape)

                                                Parameter Make.Shape

                                                Core functions
                                                val infer_shape : +Shape (owl-base.Owl_computation_symbol.Make.Shape)

                                                Parameter Make.Shape

                                                Core functions
                                                val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                                TODO

                                                diff --git a/docs/owl-base/Owl_computation_symbol/Make/index.html b/docs/owl-base/Owl_computation_symbol/Make/index.html index edc07b526..29811979a 100644 --- a/docs/owl-base/Owl_computation_symbol/Make/index.html +++ b/docs/owl-base/Owl_computation_symbol/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_computation_symbol.Make)

                                                Module Owl_computation_symbol.Make

                                                Parameters

                                                Signature

                                                module Shape = Shape
                                                val op_to_str : Shape.Type.op -> string
                                                val is_random_variable : Shape.Type.op -> bool
                                                val refnum : 'a Owl_graph.node -> int
                                                val node_shape : Shape.Type.attr Owl_graph.node -> int array
                                                val node_numel : Shape.Type.attr Owl_graph.node -> int
                                                val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool
                                                val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit
                                                val shape_to_str : int array option array -> string
                                                val node_to_str : Shape.Type.attr Owl_graph.node -> string
                                                val node_to_arr : Shape.Type.t -> Shape.Type.arr
                                                val arr_to_node : Shape.Type.arr -> Shape.Type.t
                                                val node_to_elt : Shape.Type.t -> Shape.Type.elt
                                                val elt_to_node : Shape.Type.elt -> Shape.Type.t
                                                val new_block_id : unit -> int
                                                val make_empty_block : ?block_id:int -> int -> Shape.Type.block
                                                val make_value_block : Shape.Type.Device.value -> Shape.Type.t -> unit
                                                val make_node : +Make (owl-base.Owl_computation_symbol.Make)

                                                Module Owl_computation_symbol.Make

                                                Parameters

                                                Signature

                                                module Shape = Shape
                                                val op_to_str : Shape.Type.op -> string
                                                val is_random_variable : Shape.Type.op -> bool
                                                val refnum : 'a Owl_graph.node -> int
                                                val node_shape : Shape.Type.attr Owl_graph.node -> int array
                                                val node_numel : Shape.Type.attr Owl_graph.node -> int
                                                val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool
                                                val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit
                                                val shape_to_str : int array option array -> string
                                                val node_to_str : Shape.Type.attr Owl_graph.node -> string
                                                val node_to_arr : Shape.Type.t -> Shape.Type.arr
                                                val arr_to_node : Shape.Type.arr -> Shape.Type.t
                                                val node_to_elt : Shape.Type.t -> Shape.Type.elt
                                                val elt_to_node : Shape.Type.elt -> Shape.Type.t
                                                val new_block_id : unit -> int
                                                val make_empty_block : ?block_id:int -> int -> Shape.Type.block
                                                val make_value_block : Shape.Type.Device.value -> Shape.Type.t -> unit
                                                val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_symbol/index.html b/docs/owl-base/Owl_computation_symbol/index.html index 58191d209..5f4c2d67a 100644 --- a/docs/owl-base/Owl_computation_symbol/index.html +++ b/docs/owl-base/Owl_computation_symbol/index.html @@ -1,2 +1,2 @@ -Owl_computation_symbol (owl-base.Owl_computation_symbol)

                                                Module Owl_computation_symbol

                                                module Make (Shape : Owl_computation_shape_sig.Sig) : sig ... end
                                                +Owl_computation_symbol (owl-base.Owl_computation_symbol)

                                                Module Owl_computation_symbol

                                                module Make (Shape : Owl_computation_shape_sig.Sig) : sig ... end
                                                diff --git a/docs/owl-base/Owl_computation_symbol_sig/index.html b/docs/owl-base/Owl_computation_symbol_sig/index.html index 76af344dc..604b59c0a 100644 --- a/docs/owl-base/Owl_computation_symbol_sig/index.html +++ b/docs/owl-base/Owl_computation_symbol_sig/index.html @@ -1,2 +1,2 @@ -Owl_computation_symbol_sig (owl-base.Owl_computation_symbol_sig)

                                                Module Owl_computation_symbol_sig

                                                module type Sig = sig ... end
                                                +Owl_computation_symbol_sig (owl-base.Owl_computation_symbol_sig)

                                                Module Owl_computation_symbol_sig

                                                module type Sig = sig ... end
                                                diff --git a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Linalg/index.html index d849fbf60..f1beb1097 100644 --- a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device.A.Linalg)

                                                Module A.Linalg

                                                val inv : arr -> arr
                                                val logdet : arr -> elt
                                                val chol : ?upper:bool -> arr -> arr
                                                val svd : ?thin:bool -> arr -> arr * arr * arr
                                                val qr : arr -> arr * arr
                                                val lq : arr -> arr * arr
                                                val sylvester : arr -> arr -> arr -> arr
                                                val lyapunov : arr -> arr -> arr
                                                val discrete_lyapunov : +Linalg (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device.A.Linalg)

                                                Module A.Linalg

                                                val inv : arr -> arr
                                                val logdet : arr -> elt
                                                val chol : ?upper:bool -> arr -> arr
                                                val svd : ?thin:bool -> arr -> arr * arr * arr
                                                val qr : arr -> arr * arr
                                                val lq : arr -> arr * arr
                                                val sylvester : arr -> arr -> arr -> arr
                                                val lyapunov : arr -> arr -> arr
                                                val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Mat/index.html index 34e7b14b4..7a16841e9 100644 --- a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device.A.Mat)

                                                Module A.Mat

                                                val diagm : ?k:int -> arr -> arr
                                                val triu : ?k:int -> arr -> arr
                                                val tril : ?k:int -> arr -> arr
                                                val eye : int -> arr
                                                +Mat (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device.A.Mat)

                                                Module A.Mat

                                                val diagm : ?k:int -> arr -> arr
                                                val triu : ?k:int -> arr -> arr
                                                val tril : ?k:int -> arr -> arr
                                                val eye : int -> arr
                                                diff --git a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Scalar/index.html index 66fb6c221..0de2104b4 100644 --- a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device.A.Scalar)

                                                Module A.Scalar

                                                val add : elt -> elt -> elt
                                                val sub : elt -> elt -> elt
                                                val mul : elt -> elt -> elt
                                                val div : elt -> elt -> elt
                                                val pow : elt -> elt -> elt
                                                val atan2 : elt -> elt -> elt
                                                val abs : elt -> elt
                                                val neg : elt -> elt
                                                val sqr : elt -> elt
                                                val sqrt : elt -> elt
                                                val exp : elt -> elt
                                                val log : elt -> elt
                                                val log2 : elt -> elt
                                                val log10 : elt -> elt
                                                val signum : elt -> elt
                                                val floor : elt -> elt
                                                val ceil : elt -> elt
                                                val round : elt -> elt
                                                val sin : elt -> elt
                                                val cos : elt -> elt
                                                val tan : elt -> elt
                                                val sinh : elt -> elt
                                                val cosh : elt -> elt
                                                val tanh : elt -> elt
                                                val asin : elt -> elt
                                                val acos : elt -> elt
                                                val atan : elt -> elt
                                                val asinh : elt -> elt
                                                val acosh : elt -> elt
                                                val atanh : elt -> elt
                                                val relu : elt -> elt
                                                val dawsn : elt -> elt
                                                val sigmoid : elt -> elt
                                                +Scalar (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device.A.Scalar)

                                                Module A.Scalar

                                                val add : elt -> elt -> elt
                                                val sub : elt -> elt -> elt
                                                val mul : elt -> elt -> elt
                                                val div : elt -> elt -> elt
                                                val pow : elt -> elt -> elt
                                                val atan2 : elt -> elt -> elt
                                                val abs : elt -> elt
                                                val neg : elt -> elt
                                                val sqr : elt -> elt
                                                val sqrt : elt -> elt
                                                val exp : elt -> elt
                                                val log : elt -> elt
                                                val log2 : elt -> elt
                                                val log10 : elt -> elt
                                                val signum : elt -> elt
                                                val floor : elt -> elt
                                                val ceil : elt -> elt
                                                val round : elt -> elt
                                                val sin : elt -> elt
                                                val cos : elt -> elt
                                                val tan : elt -> elt
                                                val sinh : elt -> elt
                                                val cosh : elt -> elt
                                                val tanh : elt -> elt
                                                val asin : elt -> elt
                                                val acos : elt -> elt
                                                val atan : elt -> elt
                                                val asinh : elt -> elt
                                                val acosh : elt -> elt
                                                val atanh : elt -> elt
                                                val relu : elt -> elt
                                                val dawsn : elt -> elt
                                                val sigmoid : elt -> elt
                                                diff --git a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/index.html index 00aa14abc..22b23b477 100644 --- a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device.A)

                                                Module Device.A

                                                include Owl_types_ndarray_algodiff.Sig
                                                include Owl_types_ndarray_eltcmp.Sig
                                                include Owl_types_ndarray_basic.Sig
                                                type arr
                                                type elt
                                                val empty : int array -> arr
                                                val zeros : int array -> arr
                                                val ones : int array -> arr
                                                val create : int array -> elt -> arr
                                                val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                val bernoulli : ?p:elt -> int array -> arr
                                                val init : int array -> (int -> elt) -> arr
                                                val init_nd : int array -> (int array -> elt) -> arr
                                                val shape : arr -> int array
                                                val numel : arr -> int
                                                val get : arr -> int array -> elt
                                                val set : arr -> int array -> elt -> unit
                                                val get_slice : int list list -> arr -> arr
                                                val set_slice : int list list -> arr -> arr -> unit
                                                val get_fancy : Owl_types_common.index list -> arr -> arr
                                                val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                val copy : arr -> arr
                                                val copy_ : out:arr -> arr -> unit
                                                val reset : arr -> unit
                                                val reshape : arr -> int array -> arr
                                                val reverse : arr -> arr
                                                val tile : arr -> int array -> arr
                                                val repeat : arr -> int array -> arr
                                                val concatenate : ?axis:int -> arr array -> arr
                                                val stack : ?axis:int -> arr array -> arr
                                                val split : ?axis:int -> int array -> arr -> arr array
                                                val expand : ?hi:bool -> arr -> int -> arr
                                                val squeeze : ?axis:int array -> arr -> arr
                                                val draw : ?axis:int -> arr -> int -> arr * int array
                                                val map : (elt -> elt) -> arr -> arr
                                                val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                val one_hot : int -> arr -> arr
                                                val pad : ?v:elt -> int list list -> arr -> arr
                                                val print : +A (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device.A)

                                                Module Device.A

                                                include Owl_types_ndarray_algodiff.Sig
                                                include Owl_types_ndarray_eltcmp.Sig
                                                include Owl_types_ndarray_basic.Sig
                                                type arr
                                                type elt
                                                val empty : int array -> arr
                                                val zeros : int array -> arr
                                                val ones : int array -> arr
                                                val create : int array -> elt -> arr
                                                val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                val bernoulli : ?p:elt -> int array -> arr
                                                val init : int array -> (int -> elt) -> arr
                                                val init_nd : int array -> (int array -> elt) -> arr
                                                val shape : arr -> int array
                                                val numel : arr -> int
                                                val get : arr -> int array -> elt
                                                val set : arr -> int array -> elt -> unit
                                                val get_slice : int list list -> arr -> arr
                                                val set_slice : int list list -> arr -> arr -> unit
                                                val get_fancy : Owl_types_common.index list -> arr -> arr
                                                val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                val copy : arr -> arr
                                                val copy_ : out:arr -> arr -> unit
                                                val reset : arr -> unit
                                                val reshape : arr -> int array -> arr
                                                val reverse : arr -> arr
                                                val tile : arr -> int array -> arr
                                                val repeat : arr -> int array -> arr
                                                val concatenate : ?axis:int -> arr array -> arr
                                                val stack : ?axis:int -> arr array -> arr
                                                val split : ?axis:int -> int array -> arr -> arr array
                                                val expand : ?hi:bool -> arr -> int -> arr
                                                val squeeze : ?axis:int array -> arr -> arr
                                                val draw : ?axis:int -> arr -> int -> arr * int array
                                                val map : (elt -> elt) -> arr -> arr
                                                val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                val one_hot : int -> arr -> arr
                                                val pad : ?v:elt -> int list list -> arr -> arr
                                                val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/index.html b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/index.html index 7f4493183..cb081737f 100644 --- a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device)

                                                Module Type.Device

                                                Type definition
                                                type device

                                                TODO

                                                type value

                                                TODO

                                                Core functions
                                                val make_device : unit -> device

                                                TODO

                                                val arr_to_value : A.arr -> value

                                                TODO

                                                val value_to_arr : value -> A.arr

                                                TODO

                                                val elt_to_value : A.elt -> value

                                                TODO

                                                val value_to_elt : value -> A.elt

                                                TODO

                                                val value_to_float : value -> float

                                                TODO

                                                val is_arr : value -> bool

                                                TODO

                                                val is_elt : value -> bool

                                                TODO

                                                +Device (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type.Device)

                                                Module Type.Device

                                                Type definition
                                                type device

                                                TODO

                                                type value

                                                TODO

                                                Core functions
                                                val make_device : unit -> device

                                                TODO

                                                val arr_to_value : A.arr -> value

                                                TODO

                                                val value_to_arr : value -> A.arr

                                                TODO

                                                val elt_to_value : A.elt -> value

                                                TODO

                                                val value_to_elt : value -> A.elt

                                                TODO

                                                val value_to_float : value -> float

                                                TODO

                                                val is_arr : value -> bool

                                                TODO

                                                val is_elt : value -> bool

                                                TODO

                                                diff --git a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/index.html b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/index.html index 7a9e97465..318901ad5 100644 --- a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/index.html +++ b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type)

                                                Module Shape.Type

                                                Type definition
                                                type state =
                                                1. | Valid
                                                2. | Invalid
                                                  (*

                                                  TODO

                                                  *)

                                                TODO

                                                and block = {
                                                1. size : int;
                                                2. block_id : int;
                                                3. mutable active : t option;
                                                4. mutable memory : Device.value;
                                                5. mutable nodes : t list;
                                                }

                                                block type keeps a reference to a block of memory and to the nodes sharing that block.

                                                and attr = {
                                                1. mutable op : op;
                                                2. mutable freeze : bool;
                                                3. mutable reuse : bool;
                                                4. mutable state : state;
                                                5. mutable shape : int array option array;
                                                6. mutable value : Device.value array;
                                                7. mutable block : block array option;
                                                }

                                                TODO

                                                and arr =
                                                1. | Arr of t
                                                and elt =
                                                1. | Elt of t
                                                and op =
                                                1. | Noop
                                                2. | Var
                                                3. | Const
                                                4. | Empty of int array
                                                5. | Zeros of int array
                                                6. | Ones of int array
                                                7. | Create of int array
                                                8. | Sequential of int array
                                                9. | Uniform of int array
                                                10. | Gaussian of int array
                                                11. | Bernoulli of int array
                                                12. | Init of int array * int -> elt
                                                13. | Get of int array
                                                14. | Set of int array
                                                15. | GetSlice of int list list
                                                16. | SetSlice of int list list
                                                17. | GetFancy of Owl_types_common.index list
                                                18. | SetFancy of Owl_types_common.index list
                                                19. | Copy
                                                20. | Reset
                                                21. | Reshape of int array
                                                22. | Reverse
                                                23. | Tile of int array
                                                24. | Repeat of int array
                                                25. | Pad of elt * int list list
                                                26. | Concatenate of int
                                                27. | Stack of int
                                                28. | Split of int * int array
                                                29. | Draw of int * int
                                                30. | Map of elt -> elt
                                                31. | Fold of int * elt -> elt -> elt
                                                32. | Scan of int * elt -> elt -> elt
                                                33. | OneHot of int
                                                34. | OfArray of int array
                                                35. | Delay of Device.A.arr -> Device.A.arr
                                                36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                37. | LazyPrint of int option +Type (owl-base.Owl_computation_symbol_sig.Sig.Shape.Type)

                                                  Module Shape.Type

                                                  Type definition
                                                  type state =
                                                  1. | Valid
                                                  2. | Invalid
                                                    (*

                                                    TODO

                                                    *)

                                                  TODO

                                                  and block = {
                                                  1. size : int;
                                                  2. block_id : int;
                                                  3. mutable active : t option;
                                                  4. mutable memory : Device.value;
                                                  5. mutable nodes : t list;
                                                  }

                                                  block type keeps a reference to a block of memory and to the nodes sharing that block.

                                                  and attr = {
                                                  1. mutable op : op;
                                                  2. mutable freeze : bool;
                                                  3. mutable reuse : bool;
                                                  4. mutable state : state;
                                                  5. mutable shape : int array option array;
                                                  6. mutable value : Device.value array;
                                                  7. mutable block : block array option;
                                                  }

                                                  TODO

                                                  and arr =
                                                  1. | Arr of t
                                                  and elt =
                                                  1. | Elt of t
                                                  and op =
                                                  1. | Noop
                                                  2. | Var
                                                  3. | Const
                                                  4. | Empty of int array
                                                  5. | Zeros of int array
                                                  6. | Ones of int array
                                                  7. | Create of int array
                                                  8. | Sequential of int array
                                                  9. | Uniform of int array
                                                  10. | Gaussian of int array
                                                  11. | Bernoulli of int array
                                                  12. | Init of int array * int -> elt
                                                  13. | Get of int array
                                                  14. | Set of int array
                                                  15. | GetSlice of int list list
                                                  16. | SetSlice of int list list
                                                  17. | GetFancy of Owl_types_common.index list
                                                  18. | SetFancy of Owl_types_common.index list
                                                  19. | Copy
                                                  20. | Reset
                                                  21. | Reshape of int array
                                                  22. | Reverse
                                                  23. | Tile of int array
                                                  24. | Repeat of int array
                                                  25. | Pad of elt * int list list
                                                  26. | Concatenate of int
                                                  27. | Stack of int
                                                  28. | Split of int * int array
                                                  29. | Draw of int * int
                                                  30. | Map of elt -> elt
                                                  31. | Fold of int * elt -> elt -> elt
                                                  32. | Scan of int * elt -> elt -> elt
                                                  33. | OneHot of int
                                                  34. | OfArray of int array
                                                  35. | Delay of Device.A.arr -> Device.A.arr
                                                  36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                  37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                                  38. | Abs
                                                  39. | Neg
                                                  40. | Floor
                                                  41. | Ceil
                                                  42. | Round
                                                  43. | Sqr
                                                  44. | Sqrt
                                                  45. | Log
                                                  46. | Log2
                                                  47. | Log10
                                                  48. | Exp
                                                  49. | Sin
                                                  50. | Cos
                                                  51. | Tan
                                                  52. | Sinh
                                                  53. | Cosh
                                                  54. | Tanh
                                                  55. | Asin
                                                  56. | Acos
                                                  57. | Atan
                                                  58. | Asinh
                                                  59. | Acosh
                                                  60. | Atanh
                                                  61. | Min of bool * int
                                                  62. | Max of bool * int
                                                  63. | Sum of bool * int
                                                  64. | SumReduce of int array
                                                  65. | Signum
                                                  66. | Sigmoid
                                                  67. | Relu
                                                  68. | Dawsn
                                                  69. | Min'
                                                  70. | Max'
                                                  71. | Sum'
                                                  72. | LogSumExp'
                                                  73. | LogSumExp of bool * int
                                                  74. | L1norm'
                                                  75. | L2norm'
                                                  76. | L2NormSqr'
                                                  77. | ClipByValue
                                                  78. | ClipByL2norm
                                                  79. | Pow
                                                  80. | ScalarPow
                                                  81. | PowScalar
                                                  82. | Atan2
                                                  83. | ScalarAtan2
                                                  84. | Atan2Scalar
                                                  85. | Hypot
                                                  86. | Min2
                                                  87. | Max2
                                                  88. | Add
                                                  89. | Sub
                                                  90. | Mul
                                                  91. | Div
                                                  92. | AddScalar
                                                  93. | SubScalar
                                                  94. | MulScalar
                                                  95. | DivScalar
                                                  96. | ScalarAdd
                                                  97. | ScalarSub
                                                  98. | ScalarMul
                                                  99. | ScalarDiv
                                                  100. | FMA
                                                  101. | EltEqual
                                                  102. | EltNotEqual
                                                  103. | EltLess
                                                  104. | EltGreater
                                                  105. | EltLessEqual
                                                  106. | EltGreaterEqual
                                                  107. | EltEqualScalar
                                                  108. | EltNotEqualScalar
                                                  109. | EltLessScalar
                                                  110. | EltGreaterScalar
                                                  111. | EltLessEqualScalar
                                                  112. | EltGreaterEqualScalar
                                                  113. | Conv1d of Owl_types_common.padding * int array
                                                  114. | Conv2d of Owl_types_common.padding * int array
                                                  115. | Conv3d of Owl_types_common.padding * int array
                                                  116. | TransposeConv1d of Owl_types_common.padding * int array
                                                  117. | TransposeConv2d of Owl_types_common.padding * int array
                                                  118. | TransposeConv3d of Owl_types_common.padding * int array
                                                  119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                                  120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                                  121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                                  122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                                  123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                                  124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                                  125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                                  126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                                  127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                                  128. | UpSampling2d of int array
                                                  129. | Conv1dBackwardInput of int array
                                                  130. | Conv1dBackwardKernel of int array
                                                  131. | Conv2dBackwardInput of int array
                                                  132. | Conv2dBackwardKernel of int array
                                                  133. | Conv3dBackwardInput of int array
                                                  134. | Conv3dBackwardKernel of int array
                                                  135. | TransposeConv1dBackwardInput of int array
                                                  136. | TransposeConv1dBackwardKernel of int array
                                                  137. | TransposeConv2dBackwardInput of int array
                                                  138. | TransposeConv2dBackwardKernel of int array
                                                  139. | TransposeConv3dBackwardInput of int array
                                                  140. | TransposeConv3dBackwardKernel of int array
                                                  141. | DilatedConv1dBackwardInput of int array * int array
                                                  142. | DilatedConv1dBackwardKernel of int array * int array
                                                  143. | DilatedConv2dBackwardInput of int array * int array
                                                  144. | DilatedConv2dBackwardKernel of int array * int array
                                                  145. | DilatedConv3dBackwardInput of int array * int array
                                                  146. | DilatedConv3dBackwardKernel of int array * int array
                                                  147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                                  148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                                  149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                                  150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                                  151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                                  152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                                  153. | UpSampling2dBackward of int array
                                                  154. | RowNum
                                                  155. | ColNum
                                                  156. | Row
                                                  157. | Rows of int array
                                                  158. | CopyRowTo
                                                  159. | CopyColTo
                                                  160. | Dot of bool * bool * elt * elt
                                                  161. | Inv
                                                  162. | Trace
                                                  163. | Transpose of int array
                                                  164. | ToRows
                                                  165. | OfRows
                                                  166. | Scalar_Add
                                                  167. | Scalar_Sub
                                                  168. | Scalar_Mul
                                                  169. | Scalar_Div
                                                  170. | Scalar_Pow
                                                  171. | Scalar_Atan2
                                                  172. | Scalar_Abs
                                                  173. | Scalar_Neg
                                                  174. | Scalar_Sqr
                                                  175. | Scalar_Sqrt
                                                  176. | Scalar_Exp
                                                  177. | Scalar_Log
                                                  178. | Scalar_Log2
                                                  179. | Scalar_Log10
                                                  180. | Scalar_Signum
                                                  181. | Scalar_Floor
                                                  182. | Scalar_Ceil
                                                  183. | Scalar_Round
                                                  184. | Scalar_Sin
                                                  185. | Scalar_Cos
                                                  186. | Scalar_Tan
                                                  187. | Scalar_Sinh
                                                  188. | Scalar_Cosh
                                                  189. | Scalar_Tanh
                                                  190. | Scalar_Asin
                                                  191. | Scalar_Acos
                                                  192. | Scalar_Atan
                                                  193. | Scalar_Asinh
                                                  194. | Scalar_Acosh
                                                  195. | Scalar_Atanh
                                                  196. | Scalar_Relu
                                                  197. | Scalar_Dawsn
                                                  198. | Scalar_Sigmoid
                                                  199. | Fused_Adagrad of float * float
                                                    (*

                                                    TODO

                                                    *)
                                                  diff --git a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/index.html b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/index.html index 6170e4b70..b67c59371 100644 --- a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/index.html +++ b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_computation_symbol_sig.Sig.Shape)

                                                  Module Sig.Shape

                                                  Core functions
                                                  val infer_shape : +Shape (owl-base.Owl_computation_symbol_sig.Sig.Shape)

                                                  Module Sig.Shape

                                                  Core functions
                                                  val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                                  TODO

                                                  diff --git a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/index.html b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/index.html index 418e621bd..80397f456 100644 --- a/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_computation_symbol_sig/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_computation_symbol_sig.Sig)

                                                  Module type Owl_computation_symbol_sig.Sig

                                                  Core functions
                                                  val op_to_str : Shape.Type.op -> string

                                                  TODO

                                                  val is_random_variable : Shape.Type.op -> bool

                                                  TODO

                                                  val refnum : 'a Owl_graph.node -> int

                                                  TODO

                                                  val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                                  TODO

                                                  val node_numel : Shape.Type.attr Owl_graph.node -> int

                                                  TODO

                                                  val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                                  TODO

                                                  val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                                  TODO

                                                  val shape_to_str : int array option array -> string

                                                  TODO

                                                  val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                                  TODO

                                                  val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                                  TODO

                                                  val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                                  TODO

                                                  val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                                  TODO

                                                  val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                                  TODO

                                                  val make_node : +Sig (owl-base.Owl_computation_symbol_sig.Sig)

                                                  Module type Owl_computation_symbol_sig.Sig

                                                  Core functions
                                                  val op_to_str : Shape.Type.op -> string

                                                  TODO

                                                  val is_random_variable : Shape.Type.op -> bool

                                                  TODO

                                                  val refnum : 'a Owl_graph.node -> int

                                                  TODO

                                                  val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                                  TODO

                                                  val node_numel : Shape.Type.attr Owl_graph.node -> int

                                                  TODO

                                                  val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                                  TODO

                                                  val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                                  TODO

                                                  val shape_to_str : int array option array -> string

                                                  TODO

                                                  val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                                  TODO

                                                  val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                                  TODO

                                                  val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                                  TODO

                                                  val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                                  TODO

                                                  val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                                  TODO

                                                  val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Linalg/index.html index 5cbb2433a..a8c45cbeb 100644 --- a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_type.Make.Device.A.Linalg)

                                                  Module A.Linalg

                                                  val inv : arr -> arr
                                                  val logdet : arr -> elt
                                                  val chol : ?upper:bool -> arr -> arr
                                                  val svd : ?thin:bool -> arr -> arr * arr * arr
                                                  val qr : arr -> arr * arr
                                                  val lq : arr -> arr * arr
                                                  val sylvester : arr -> arr -> arr -> arr
                                                  val lyapunov : arr -> arr -> arr
                                                  val discrete_lyapunov : +Linalg (owl-base.Owl_computation_type.Make.Device.A.Linalg)

                                                  Module A.Linalg

                                                  val inv : arr -> arr
                                                  val logdet : arr -> elt
                                                  val chol : ?upper:bool -> arr -> arr
                                                  val svd : ?thin:bool -> arr -> arr * arr * arr
                                                  val qr : arr -> arr * arr
                                                  val lq : arr -> arr * arr
                                                  val sylvester : arr -> arr -> arr -> arr
                                                  val lyapunov : arr -> arr -> arr
                                                  val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Mat/index.html b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Mat/index.html index 69387a54d..b8d863d5c 100644 --- a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_type.Make.Device.A.Mat)

                                                  Module A.Mat

                                                  val diagm : ?k:int -> arr -> arr
                                                  val triu : ?k:int -> arr -> arr
                                                  val tril : ?k:int -> arr -> arr
                                                  val eye : int -> arr
                                                  +Mat (owl-base.Owl_computation_type.Make.Device.A.Mat)

                                                  Module A.Mat

                                                  val diagm : ?k:int -> arr -> arr
                                                  val triu : ?k:int -> arr -> arr
                                                  val tril : ?k:int -> arr -> arr
                                                  val eye : int -> arr
                                                  diff --git a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Scalar/index.html index 917220cba..e0416d1cc 100644 --- a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_type.Make.Device.A.Scalar)

                                                  Module A.Scalar

                                                  val add : elt -> elt -> elt
                                                  val sub : elt -> elt -> elt
                                                  val mul : elt -> elt -> elt
                                                  val div : elt -> elt -> elt
                                                  val pow : elt -> elt -> elt
                                                  val atan2 : elt -> elt -> elt
                                                  val abs : elt -> elt
                                                  val neg : elt -> elt
                                                  val sqr : elt -> elt
                                                  val sqrt : elt -> elt
                                                  val exp : elt -> elt
                                                  val log : elt -> elt
                                                  val log2 : elt -> elt
                                                  val log10 : elt -> elt
                                                  val signum : elt -> elt
                                                  val floor : elt -> elt
                                                  val ceil : elt -> elt
                                                  val round : elt -> elt
                                                  val sin : elt -> elt
                                                  val cos : elt -> elt
                                                  val tan : elt -> elt
                                                  val sinh : elt -> elt
                                                  val cosh : elt -> elt
                                                  val tanh : elt -> elt
                                                  val asin : elt -> elt
                                                  val acos : elt -> elt
                                                  val atan : elt -> elt
                                                  val asinh : elt -> elt
                                                  val acosh : elt -> elt
                                                  val atanh : elt -> elt
                                                  val relu : elt -> elt
                                                  val dawsn : elt -> elt
                                                  val sigmoid : elt -> elt
                                                  +Scalar (owl-base.Owl_computation_type.Make.Device.A.Scalar)

                                                  Module A.Scalar

                                                  val add : elt -> elt -> elt
                                                  val sub : elt -> elt -> elt
                                                  val mul : elt -> elt -> elt
                                                  val div : elt -> elt -> elt
                                                  val pow : elt -> elt -> elt
                                                  val atan2 : elt -> elt -> elt
                                                  val abs : elt -> elt
                                                  val neg : elt -> elt
                                                  val sqr : elt -> elt
                                                  val sqrt : elt -> elt
                                                  val exp : elt -> elt
                                                  val log : elt -> elt
                                                  val log2 : elt -> elt
                                                  val log10 : elt -> elt
                                                  val signum : elt -> elt
                                                  val floor : elt -> elt
                                                  val ceil : elt -> elt
                                                  val round : elt -> elt
                                                  val sin : elt -> elt
                                                  val cos : elt -> elt
                                                  val tan : elt -> elt
                                                  val sinh : elt -> elt
                                                  val cosh : elt -> elt
                                                  val tanh : elt -> elt
                                                  val asin : elt -> elt
                                                  val acos : elt -> elt
                                                  val atan : elt -> elt
                                                  val asinh : elt -> elt
                                                  val acosh : elt -> elt
                                                  val atanh : elt -> elt
                                                  val relu : elt -> elt
                                                  val dawsn : elt -> elt
                                                  val sigmoid : elt -> elt
                                                  diff --git a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/index.html b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/index.html index a9e26b361..0377bce0d 100644 --- a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/index.html +++ b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_type.Make.Device.A)

                                                  Module Device.A

                                                  include Owl_types_ndarray_algodiff.Sig
                                                  include Owl_types_ndarray_eltcmp.Sig
                                                  include Owl_types_ndarray_basic.Sig
                                                  type arr
                                                  type elt
                                                  val empty : int array -> arr
                                                  val zeros : int array -> arr
                                                  val ones : int array -> arr
                                                  val create : int array -> elt -> arr
                                                  val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                  val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                  val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                  val bernoulli : ?p:elt -> int array -> arr
                                                  val init : int array -> (int -> elt) -> arr
                                                  val init_nd : int array -> (int array -> elt) -> arr
                                                  val shape : arr -> int array
                                                  val numel : arr -> int
                                                  val get : arr -> int array -> elt
                                                  val set : arr -> int array -> elt -> unit
                                                  val get_slice : int list list -> arr -> arr
                                                  val set_slice : int list list -> arr -> arr -> unit
                                                  val get_fancy : Owl_types_common.index list -> arr -> arr
                                                  val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                  val copy : arr -> arr
                                                  val copy_ : out:arr -> arr -> unit
                                                  val reset : arr -> unit
                                                  val reshape : arr -> int array -> arr
                                                  val reverse : arr -> arr
                                                  val tile : arr -> int array -> arr
                                                  val repeat : arr -> int array -> arr
                                                  val concatenate : ?axis:int -> arr array -> arr
                                                  val stack : ?axis:int -> arr array -> arr
                                                  val split : ?axis:int -> int array -> arr -> arr array
                                                  val expand : ?hi:bool -> arr -> int -> arr
                                                  val squeeze : ?axis:int array -> arr -> arr
                                                  val draw : ?axis:int -> arr -> int -> arr * int array
                                                  val map : (elt -> elt) -> arr -> arr
                                                  val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                  val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                  val one_hot : int -> arr -> arr
                                                  val pad : ?v:elt -> int list list -> arr -> arr
                                                  val print : +A (owl-base.Owl_computation_type.Make.Device.A)

                                                  Module Device.A

                                                  include Owl_types_ndarray_algodiff.Sig
                                                  include Owl_types_ndarray_eltcmp.Sig
                                                  include Owl_types_ndarray_basic.Sig
                                                  type arr
                                                  type elt
                                                  val empty : int array -> arr
                                                  val zeros : int array -> arr
                                                  val ones : int array -> arr
                                                  val create : int array -> elt -> arr
                                                  val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                  val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                  val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                  val bernoulli : ?p:elt -> int array -> arr
                                                  val init : int array -> (int -> elt) -> arr
                                                  val init_nd : int array -> (int array -> elt) -> arr
                                                  val shape : arr -> int array
                                                  val numel : arr -> int
                                                  val get : arr -> int array -> elt
                                                  val set : arr -> int array -> elt -> unit
                                                  val get_slice : int list list -> arr -> arr
                                                  val set_slice : int list list -> arr -> arr -> unit
                                                  val get_fancy : Owl_types_common.index list -> arr -> arr
                                                  val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                  val copy : arr -> arr
                                                  val copy_ : out:arr -> arr -> unit
                                                  val reset : arr -> unit
                                                  val reshape : arr -> int array -> arr
                                                  val reverse : arr -> arr
                                                  val tile : arr -> int array -> arr
                                                  val repeat : arr -> int array -> arr
                                                  val concatenate : ?axis:int -> arr array -> arr
                                                  val stack : ?axis:int -> arr array -> arr
                                                  val split : ?axis:int -> int array -> arr -> arr array
                                                  val expand : ?hi:bool -> arr -> int -> arr
                                                  val squeeze : ?axis:int array -> arr -> arr
                                                  val draw : ?axis:int -> arr -> int -> arr * int array
                                                  val map : (elt -> elt) -> arr -> arr
                                                  val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                  val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                  val one_hot : int -> arr -> arr
                                                  val pad : ?v:elt -> int list list -> arr -> arr
                                                  val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/index.html b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/index.html index 9443e731d..f434f2129 100644 --- a/docs/owl-base/Owl_computation_type/Make/argument-1-Device/index.html +++ b/docs/owl-base/Owl_computation_type/Make/argument-1-Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_type.Make.Device)

                                                  Parameter Make.Device

                                                  Type definition
                                                  type device

                                                  TODO

                                                  type value

                                                  TODO

                                                  Core functions
                                                  val make_device : unit -> device

                                                  TODO

                                                  val arr_to_value : A.arr -> value

                                                  TODO

                                                  val value_to_arr : value -> A.arr

                                                  TODO

                                                  val elt_to_value : A.elt -> value

                                                  TODO

                                                  val value_to_elt : value -> A.elt

                                                  TODO

                                                  val value_to_float : value -> float

                                                  TODO

                                                  val is_arr : value -> bool

                                                  TODO

                                                  val is_elt : value -> bool

                                                  TODO

                                                  +Device (owl-base.Owl_computation_type.Make.Device)

                                                  Parameter Make.Device

                                                  Type definition
                                                  type device

                                                  TODO

                                                  type value

                                                  TODO

                                                  Core functions
                                                  val make_device : unit -> device

                                                  TODO

                                                  val arr_to_value : A.arr -> value

                                                  TODO

                                                  val value_to_arr : value -> A.arr

                                                  TODO

                                                  val elt_to_value : A.elt -> value

                                                  TODO

                                                  val value_to_elt : value -> A.elt

                                                  TODO

                                                  val value_to_float : value -> float

                                                  TODO

                                                  val is_arr : value -> bool

                                                  TODO

                                                  val is_elt : value -> bool

                                                  TODO

                                                  diff --git a/docs/owl-base/Owl_computation_type/Make/index.html b/docs/owl-base/Owl_computation_type/Make/index.html index 62d1ecb93..fad3c6300 100644 --- a/docs/owl-base/Owl_computation_type/Make/index.html +++ b/docs/owl-base/Owl_computation_type/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_computation_type.Make)

                                                  Module Owl_computation_type.Make

                                                  Parameters

                                                  Signature

                                                  module Device = Device
                                                  type state =
                                                  1. | Valid
                                                  2. | Invalid
                                                  and block = {
                                                  1. size : int;
                                                  2. block_id : int;
                                                  3. mutable active : t option;
                                                  4. mutable memory : Device.value;
                                                  5. mutable nodes : t list;
                                                  }
                                                  and attr = {
                                                  1. mutable op : op;
                                                  2. mutable freeze : bool;
                                                  3. mutable reuse : bool;
                                                  4. mutable state : state;
                                                  5. mutable shape : int array option array;
                                                  6. mutable value : Device.value array;
                                                  7. mutable block : block array option;
                                                  }
                                                  and arr =
                                                  1. | Arr of t
                                                  and elt =
                                                  1. | Elt of t
                                                  and op =
                                                  1. | Noop
                                                  2. | Var
                                                  3. | Const
                                                  4. | Empty of int array
                                                  5. | Zeros of int array
                                                  6. | Ones of int array
                                                  7. | Create of int array
                                                  8. | Sequential of int array
                                                  9. | Uniform of int array
                                                  10. | Gaussian of int array
                                                  11. | Bernoulli of int array
                                                  12. | Init of int array * int -> elt
                                                  13. | Get of int array
                                                  14. | Set of int array
                                                  15. | GetSlice of int list list
                                                  16. | SetSlice of int list list
                                                  17. | GetFancy of Owl_types.index list
                                                  18. | SetFancy of Owl_types.index list
                                                  19. | Copy
                                                  20. | Reset
                                                  21. | Reshape of int array
                                                  22. | Reverse
                                                  23. | Tile of int array
                                                  24. | Repeat of int array
                                                  25. | Pad of elt * int list list
                                                  26. | Concatenate of int
                                                  27. | Stack of int
                                                  28. | Split of int * int array
                                                  29. | Draw of int * int
                                                  30. | Map of elt -> elt
                                                  31. | Fold of int * elt -> elt -> elt
                                                  32. | Scan of int * elt -> elt -> elt
                                                  33. | OneHot of int
                                                  34. | OfArray of int array
                                                  35. | Delay of Device.A.arr -> Device.A.arr
                                                  36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                  37. | LazyPrint of int option +Make (owl-base.Owl_computation_type.Make)

                                                    Module Owl_computation_type.Make

                                                    Parameters

                                                    Signature

                                                    module Device = Device
                                                    type state =
                                                    1. | Valid
                                                    2. | Invalid
                                                    and block = {
                                                    1. size : int;
                                                    2. block_id : int;
                                                    3. mutable active : t option;
                                                    4. mutable memory : Device.value;
                                                    5. mutable nodes : t list;
                                                    }
                                                    and attr = {
                                                    1. mutable op : op;
                                                    2. mutable freeze : bool;
                                                    3. mutable reuse : bool;
                                                    4. mutable state : state;
                                                    5. mutable shape : int array option array;
                                                    6. mutable value : Device.value array;
                                                    7. mutable block : block array option;
                                                    }
                                                    and arr =
                                                    1. | Arr of t
                                                    and elt =
                                                    1. | Elt of t
                                                    and op =
                                                    1. | Noop
                                                    2. | Var
                                                    3. | Const
                                                    4. | Empty of int array
                                                    5. | Zeros of int array
                                                    6. | Ones of int array
                                                    7. | Create of int array
                                                    8. | Sequential of int array
                                                    9. | Uniform of int array
                                                    10. | Gaussian of int array
                                                    11. | Bernoulli of int array
                                                    12. | Init of int array * int -> elt
                                                    13. | Get of int array
                                                    14. | Set of int array
                                                    15. | GetSlice of int list list
                                                    16. | SetSlice of int list list
                                                    17. | GetFancy of Owl_types.index list
                                                    18. | SetFancy of Owl_types.index list
                                                    19. | Copy
                                                    20. | Reset
                                                    21. | Reshape of int array
                                                    22. | Reverse
                                                    23. | Tile of int array
                                                    24. | Repeat of int array
                                                    25. | Pad of elt * int list list
                                                    26. | Concatenate of int
                                                    27. | Stack of int
                                                    28. | Split of int * int array
                                                    29. | Draw of int * int
                                                    30. | Map of elt -> elt
                                                    31. | Fold of int * elt -> elt -> elt
                                                    32. | Scan of int * elt -> elt -> elt
                                                    33. | OneHot of int
                                                    34. | OfArray of int array
                                                    35. | Delay of Device.A.arr -> Device.A.arr
                                                    36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                    37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                                    38. | Abs
                                                    39. | Neg
                                                    40. | Floor
                                                    41. | Ceil
                                                    42. | Round
                                                    43. | Sqr
                                                    44. | Sqrt
                                                    45. | Log
                                                    46. | Log2
                                                    47. | Log10
                                                    48. | Exp
                                                    49. | Sin
                                                    50. | Cos
                                                    51. | Tan
                                                    52. | Sinh
                                                    53. | Cosh
                                                    54. | Tanh
                                                    55. | Asin
                                                    56. | Acos
                                                    57. | Atan
                                                    58. | Asinh
                                                    59. | Acosh
                                                    60. | Atanh
                                                    61. | Min of bool * int
                                                    62. | Max of bool * int
                                                    63. | Sum of bool * int
                                                    64. | SumReduce of int array
                                                    65. | Signum
                                                    66. | Sigmoid
                                                    67. | Relu
                                                    68. | Dawsn
                                                    69. | Min'
                                                    70. | Max'
                                                    71. | Sum'
                                                    72. | LogSumExp'
                                                    73. | LogSumExp of bool * int
                                                    74. | L1norm'
                                                    75. | L2norm'
                                                    76. | L2NormSqr'
                                                    77. | ClipByValue
                                                    78. | ClipByL2norm
                                                    79. | Pow
                                                    80. | ScalarPow
                                                    81. | PowScalar
                                                    82. | Atan2
                                                    83. | ScalarAtan2
                                                    84. | Atan2Scalar
                                                    85. | Hypot
                                                    86. | Min2
                                                    87. | Max2
                                                    88. | Add
                                                    89. | Sub
                                                    90. | Mul
                                                    91. | Div
                                                    92. | AddScalar
                                                    93. | SubScalar
                                                    94. | MulScalar
                                                    95. | DivScalar
                                                    96. | ScalarAdd
                                                    97. | ScalarSub
                                                    98. | ScalarMul
                                                    99. | ScalarDiv
                                                    100. | FMA
                                                    101. | EltEqual
                                                    102. | EltNotEqual
                                                    103. | EltLess
                                                    104. | EltGreater
                                                    105. | EltLessEqual
                                                    106. | EltGreaterEqual
                                                    107. | EltEqualScalar
                                                    108. | EltNotEqualScalar
                                                    109. | EltLessScalar
                                                    110. | EltGreaterScalar
                                                    111. | EltLessEqualScalar
                                                    112. | EltGreaterEqualScalar
                                                    113. | Conv1d of Owl_types.padding * int array
                                                    114. | Conv2d of Owl_types.padding * int array
                                                    115. | Conv3d of Owl_types.padding * int array
                                                    116. | TransposeConv1d of Owl_types.padding * int array
                                                    117. | TransposeConv2d of Owl_types.padding * int array
                                                    118. | TransposeConv3d of Owl_types.padding * int array
                                                    119. | DilatedConv1d of Owl_types.padding * int array * int array
                                                    120. | DilatedConv2d of Owl_types.padding * int array * int array
                                                    121. | DilatedConv3d of Owl_types.padding * int array * int array
                                                    122. | MaxPool1d of Owl_types.padding * int array * int array
                                                    123. | MaxPool2d of Owl_types.padding * int array * int array
                                                    124. | MaxPool3d of Owl_types.padding * int array * int array
                                                    125. | AvgPool1d of Owl_types.padding * int array * int array
                                                    126. | AvgPool2d of Owl_types.padding * int array * int array
                                                    127. | AvgPool3d of Owl_types.padding * int array * int array
                                                    128. | UpSampling2d of int array
                                                    129. | Conv1dBackwardInput of int array
                                                    130. | Conv1dBackwardKernel of int array
                                                    131. | Conv2dBackwardInput of int array
                                                    132. | Conv2dBackwardKernel of int array
                                                    133. | Conv3dBackwardInput of int array
                                                    134. | Conv3dBackwardKernel of int array
                                                    135. | TransposeConv1dBackwardInput of int array
                                                    136. | TransposeConv1dBackwardKernel of int array
                                                    137. | TransposeConv2dBackwardInput of int array
                                                    138. | TransposeConv2dBackwardKernel of int array
                                                    139. | TransposeConv3dBackwardInput of int array
                                                    140. | TransposeConv3dBackwardKernel of int array
                                                    141. | DilatedConv1dBackwardInput of int array * int array
                                                    142. | DilatedConv1dBackwardKernel of int array * int array
                                                    143. | DilatedConv2dBackwardInput of int array * int array
                                                    144. | DilatedConv2dBackwardKernel of int array * int array
                                                    145. | DilatedConv3dBackwardInput of int array * int array
                                                    146. | DilatedConv3dBackwardKernel of int array * int array
                                                    147. | MaxPool1dBackward of Owl_types.padding * int array * int array
                                                    148. | MaxPool2dBackward of Owl_types.padding * int array * int array
                                                    149. | MaxPool3dBackward of Owl_types.padding * int array * int array
                                                    150. | AvgPool1dBackward of Owl_types.padding * int array * int array
                                                    151. | AvgPool2dBackward of Owl_types.padding * int array * int array
                                                    152. | AvgPool3dBackward of Owl_types.padding * int array * int array
                                                    153. | UpSampling2dBackward of int array
                                                    154. | RowNum
                                                    155. | ColNum
                                                    156. | Row
                                                    157. | Rows of int array
                                                    158. | CopyRowTo
                                                    159. | CopyColTo
                                                    160. | Dot of bool * bool * elt * elt
                                                    161. | Inv
                                                    162. | Trace
                                                    163. | Transpose of int array
                                                    164. | ToRows
                                                    165. | OfRows
                                                    166. | Scalar_Add
                                                    167. | Scalar_Sub
                                                    168. | Scalar_Mul
                                                    169. | Scalar_Div
                                                    170. | Scalar_Pow
                                                    171. | Scalar_Atan2
                                                    172. | Scalar_Abs
                                                    173. | Scalar_Neg
                                                    174. | Scalar_Sqr
                                                    175. | Scalar_Sqrt
                                                    176. | Scalar_Exp
                                                    177. | Scalar_Log
                                                    178. | Scalar_Log2
                                                    179. | Scalar_Log10
                                                    180. | Scalar_Signum
                                                    181. | Scalar_Floor
                                                    182. | Scalar_Ceil
                                                    183. | Scalar_Round
                                                    184. | Scalar_Sin
                                                    185. | Scalar_Cos
                                                    186. | Scalar_Tan
                                                    187. | Scalar_Sinh
                                                    188. | Scalar_Cosh
                                                    189. | Scalar_Tanh
                                                    190. | Scalar_Asin
                                                    191. | Scalar_Acos
                                                    192. | Scalar_Atan
                                                    193. | Scalar_Asinh
                                                    194. | Scalar_Acosh
                                                    195. | Scalar_Atanh
                                                    196. | Scalar_Relu
                                                    197. | Scalar_Dawsn
                                                    198. | Scalar_Sigmoid
                                                    199. | Fused_Adagrad of float * float
                                                    diff --git a/docs/owl-base/Owl_computation_type/index.html b/docs/owl-base/Owl_computation_type/index.html index 9d6935b68..1e95f3379 100644 --- a/docs/owl-base/Owl_computation_type/index.html +++ b/docs/owl-base/Owl_computation_type/index.html @@ -1,2 +1,2 @@ -Owl_computation_type (owl-base.Owl_computation_type)

                                                    Module Owl_computation_type

                                                    +Owl_computation_type (owl-base.Owl_computation_type)

                                                    Module Owl_computation_type

                                                    diff --git a/docs/owl-base/Owl_computation_type_sig/index.html b/docs/owl-base/Owl_computation_type_sig/index.html index 9fe3d2e4d..6222c0e60 100644 --- a/docs/owl-base/Owl_computation_type_sig/index.html +++ b/docs/owl-base/Owl_computation_type_sig/index.html @@ -1,2 +1,2 @@ -Owl_computation_type_sig (owl-base.Owl_computation_type_sig)

                                                    Module Owl_computation_type_sig

                                                    module type Sig = sig ... end
                                                    +Owl_computation_type_sig (owl-base.Owl_computation_type_sig)

                                                    Module Owl_computation_type_sig

                                                    module type Sig = sig ... end
                                                    diff --git a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Linalg/index.html b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Linalg/index.html index cb35c487e..95a6f2223 100644 --- a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_computation_type_sig.Sig.Device.A.Linalg)

                                                    Module A.Linalg

                                                    val inv : arr -> arr
                                                    val logdet : arr -> elt
                                                    val chol : ?upper:bool -> arr -> arr
                                                    val svd : ?thin:bool -> arr -> arr * arr * arr
                                                    val qr : arr -> arr * arr
                                                    val lq : arr -> arr * arr
                                                    val sylvester : arr -> arr -> arr -> arr
                                                    val lyapunov : arr -> arr -> arr
                                                    val discrete_lyapunov : +Linalg (owl-base.Owl_computation_type_sig.Sig.Device.A.Linalg)

                                                    Module A.Linalg

                                                    val inv : arr -> arr
                                                    val logdet : arr -> elt
                                                    val chol : ?upper:bool -> arr -> arr
                                                    val svd : ?thin:bool -> arr -> arr * arr * arr
                                                    val qr : arr -> arr * arr
                                                    val lq : arr -> arr * arr
                                                    val sylvester : arr -> arr -> arr -> arr
                                                    val lyapunov : arr -> arr -> arr
                                                    val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Mat/index.html b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Mat/index.html index dcd47358f..9a9d0142c 100644 --- a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_computation_type_sig.Sig.Device.A.Mat)

                                                    Module A.Mat

                                                    val diagm : ?k:int -> arr -> arr
                                                    val triu : ?k:int -> arr -> arr
                                                    val tril : ?k:int -> arr -> arr
                                                    val eye : int -> arr
                                                    +Mat (owl-base.Owl_computation_type_sig.Sig.Device.A.Mat)

                                                    Module A.Mat

                                                    val diagm : ?k:int -> arr -> arr
                                                    val triu : ?k:int -> arr -> arr
                                                    val tril : ?k:int -> arr -> arr
                                                    val eye : int -> arr
                                                    diff --git a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Scalar/index.html b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Scalar/index.html index 46c26f8b2..150e8ca36 100644 --- a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_computation_type_sig.Sig.Device.A.Scalar)

                                                    Module A.Scalar

                                                    val add : elt -> elt -> elt
                                                    val sub : elt -> elt -> elt
                                                    val mul : elt -> elt -> elt
                                                    val div : elt -> elt -> elt
                                                    val pow : elt -> elt -> elt
                                                    val atan2 : elt -> elt -> elt
                                                    val abs : elt -> elt
                                                    val neg : elt -> elt
                                                    val sqr : elt -> elt
                                                    val sqrt : elt -> elt
                                                    val exp : elt -> elt
                                                    val log : elt -> elt
                                                    val log2 : elt -> elt
                                                    val log10 : elt -> elt
                                                    val signum : elt -> elt
                                                    val floor : elt -> elt
                                                    val ceil : elt -> elt
                                                    val round : elt -> elt
                                                    val sin : elt -> elt
                                                    val cos : elt -> elt
                                                    val tan : elt -> elt
                                                    val sinh : elt -> elt
                                                    val cosh : elt -> elt
                                                    val tanh : elt -> elt
                                                    val asin : elt -> elt
                                                    val acos : elt -> elt
                                                    val atan : elt -> elt
                                                    val asinh : elt -> elt
                                                    val acosh : elt -> elt
                                                    val atanh : elt -> elt
                                                    val relu : elt -> elt
                                                    val dawsn : elt -> elt
                                                    val sigmoid : elt -> elt
                                                    +Scalar (owl-base.Owl_computation_type_sig.Sig.Device.A.Scalar)

                                                    Module A.Scalar

                                                    val add : elt -> elt -> elt
                                                    val sub : elt -> elt -> elt
                                                    val mul : elt -> elt -> elt
                                                    val div : elt -> elt -> elt
                                                    val pow : elt -> elt -> elt
                                                    val atan2 : elt -> elt -> elt
                                                    val abs : elt -> elt
                                                    val neg : elt -> elt
                                                    val sqr : elt -> elt
                                                    val sqrt : elt -> elt
                                                    val exp : elt -> elt
                                                    val log : elt -> elt
                                                    val log2 : elt -> elt
                                                    val log10 : elt -> elt
                                                    val signum : elt -> elt
                                                    val floor : elt -> elt
                                                    val ceil : elt -> elt
                                                    val round : elt -> elt
                                                    val sin : elt -> elt
                                                    val cos : elt -> elt
                                                    val tan : elt -> elt
                                                    val sinh : elt -> elt
                                                    val cosh : elt -> elt
                                                    val tanh : elt -> elt
                                                    val asin : elt -> elt
                                                    val acos : elt -> elt
                                                    val atan : elt -> elt
                                                    val asinh : elt -> elt
                                                    val acosh : elt -> elt
                                                    val atanh : elt -> elt
                                                    val relu : elt -> elt
                                                    val dawsn : elt -> elt
                                                    val sigmoid : elt -> elt
                                                    diff --git a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/index.html b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/index.html index a0da21f9c..75d6511e1 100644 --- a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/index.html +++ b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_computation_type_sig.Sig.Device.A)

                                                    Module Device.A

                                                    include Owl_types_ndarray_algodiff.Sig
                                                    include Owl_types_ndarray_eltcmp.Sig
                                                    include Owl_types_ndarray_basic.Sig
                                                    type arr
                                                    type elt
                                                    val empty : int array -> arr
                                                    val zeros : int array -> arr
                                                    val ones : int array -> arr
                                                    val create : int array -> elt -> arr
                                                    val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                    val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                    val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                    val bernoulli : ?p:elt -> int array -> arr
                                                    val init : int array -> (int -> elt) -> arr
                                                    val init_nd : int array -> (int array -> elt) -> arr
                                                    val shape : arr -> int array
                                                    val numel : arr -> int
                                                    val get : arr -> int array -> elt
                                                    val set : arr -> int array -> elt -> unit
                                                    val get_slice : int list list -> arr -> arr
                                                    val set_slice : int list list -> arr -> arr -> unit
                                                    val get_fancy : Owl_types_common.index list -> arr -> arr
                                                    val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                    val copy : arr -> arr
                                                    val copy_ : out:arr -> arr -> unit
                                                    val reset : arr -> unit
                                                    val reshape : arr -> int array -> arr
                                                    val reverse : arr -> arr
                                                    val tile : arr -> int array -> arr
                                                    val repeat : arr -> int array -> arr
                                                    val concatenate : ?axis:int -> arr array -> arr
                                                    val stack : ?axis:int -> arr array -> arr
                                                    val split : ?axis:int -> int array -> arr -> arr array
                                                    val expand : ?hi:bool -> arr -> int -> arr
                                                    val squeeze : ?axis:int array -> arr -> arr
                                                    val draw : ?axis:int -> arr -> int -> arr * int array
                                                    val map : (elt -> elt) -> arr -> arr
                                                    val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                    val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                    val one_hot : int -> arr -> arr
                                                    val pad : ?v:elt -> int list list -> arr -> arr
                                                    val print : +A (owl-base.Owl_computation_type_sig.Sig.Device.A)

                                                    Module Device.A

                                                    include Owl_types_ndarray_algodiff.Sig
                                                    include Owl_types_ndarray_eltcmp.Sig
                                                    include Owl_types_ndarray_basic.Sig
                                                    type arr
                                                    type elt
                                                    val empty : int array -> arr
                                                    val zeros : int array -> arr
                                                    val ones : int array -> arr
                                                    val create : int array -> elt -> arr
                                                    val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                    val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                    val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                    val bernoulli : ?p:elt -> int array -> arr
                                                    val init : int array -> (int -> elt) -> arr
                                                    val init_nd : int array -> (int array -> elt) -> arr
                                                    val shape : arr -> int array
                                                    val numel : arr -> int
                                                    val get : arr -> int array -> elt
                                                    val set : arr -> int array -> elt -> unit
                                                    val get_slice : int list list -> arr -> arr
                                                    val set_slice : int list list -> arr -> arr -> unit
                                                    val get_fancy : Owl_types_common.index list -> arr -> arr
                                                    val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                    val copy : arr -> arr
                                                    val copy_ : out:arr -> arr -> unit
                                                    val reset : arr -> unit
                                                    val reshape : arr -> int array -> arr
                                                    val reverse : arr -> arr
                                                    val tile : arr -> int array -> arr
                                                    val repeat : arr -> int array -> arr
                                                    val concatenate : ?axis:int -> arr array -> arr
                                                    val stack : ?axis:int -> arr array -> arr
                                                    val split : ?axis:int -> int array -> arr -> arr array
                                                    val expand : ?hi:bool -> arr -> int -> arr
                                                    val squeeze : ?axis:int array -> arr -> arr
                                                    val draw : ?axis:int -> arr -> int -> arr * int array
                                                    val map : (elt -> elt) -> arr -> arr
                                                    val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                    val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                    val one_hot : int -> arr -> arr
                                                    val pad : ?v:elt -> int list list -> arr -> arr
                                                    val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/index.html b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/index.html index 5470a584d..4babd3c46 100644 --- a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/index.html +++ b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_computation_type_sig.Sig.Device)

                                                    Module Sig.Device

                                                    Type definition
                                                    type device

                                                    TODO

                                                    type value

                                                    TODO

                                                    Core functions
                                                    val make_device : unit -> device

                                                    TODO

                                                    val arr_to_value : A.arr -> value

                                                    TODO

                                                    val value_to_arr : value -> A.arr

                                                    TODO

                                                    val elt_to_value : A.elt -> value

                                                    TODO

                                                    val value_to_elt : value -> A.elt

                                                    TODO

                                                    val value_to_float : value -> float

                                                    TODO

                                                    val is_arr : value -> bool

                                                    TODO

                                                    val is_elt : value -> bool

                                                    TODO

                                                    +Device (owl-base.Owl_computation_type_sig.Sig.Device)

                                                    Module Sig.Device

                                                    Type definition
                                                    type device

                                                    TODO

                                                    type value

                                                    TODO

                                                    Core functions
                                                    val make_device : unit -> device

                                                    TODO

                                                    val arr_to_value : A.arr -> value

                                                    TODO

                                                    val value_to_arr : value -> A.arr

                                                    TODO

                                                    val elt_to_value : A.elt -> value

                                                    TODO

                                                    val value_to_elt : value -> A.elt

                                                    TODO

                                                    val value_to_float : value -> float

                                                    TODO

                                                    val is_arr : value -> bool

                                                    TODO

                                                    val is_elt : value -> bool

                                                    TODO

                                                    diff --git a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/index.html b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/index.html index 5991f02ec..35ee4d5c5 100644 --- a/docs/owl-base/Owl_computation_type_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_computation_type_sig/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_computation_type_sig.Sig)

                                                    Module type Owl_computation_type_sig.Sig

                                                    Type definition
                                                    type state =
                                                    1. | Valid
                                                    2. | Invalid
                                                      (*

                                                      TODO

                                                      *)

                                                    TODO

                                                    and block = {
                                                    1. size : int;
                                                    2. block_id : int;
                                                    3. mutable active : t option;
                                                    4. mutable memory : Device.value;
                                                    5. mutable nodes : t list;
                                                    }

                                                    block type keeps a reference to a block of memory and to the nodes sharing that block.

                                                    and attr = {
                                                    1. mutable op : op;
                                                    2. mutable freeze : bool;
                                                    3. mutable reuse : bool;
                                                    4. mutable state : state;
                                                    5. mutable shape : int array option array;
                                                    6. mutable value : Device.value array;
                                                    7. mutable block : block array option;
                                                    }

                                                    TODO

                                                    and arr =
                                                    1. | Arr of t
                                                    and elt =
                                                    1. | Elt of t
                                                    and op =
                                                    1. | Noop
                                                    2. | Var
                                                    3. | Const
                                                    4. | Empty of int array
                                                    5. | Zeros of int array
                                                    6. | Ones of int array
                                                    7. | Create of int array
                                                    8. | Sequential of int array
                                                    9. | Uniform of int array
                                                    10. | Gaussian of int array
                                                    11. | Bernoulli of int array
                                                    12. | Init of int array * int -> elt
                                                    13. | Get of int array
                                                    14. | Set of int array
                                                    15. | GetSlice of int list list
                                                    16. | SetSlice of int list list
                                                    17. | GetFancy of Owl_types_common.index list
                                                    18. | SetFancy of Owl_types_common.index list
                                                    19. | Copy
                                                    20. | Reset
                                                    21. | Reshape of int array
                                                    22. | Reverse
                                                    23. | Tile of int array
                                                    24. | Repeat of int array
                                                    25. | Pad of elt * int list list
                                                    26. | Concatenate of int
                                                    27. | Stack of int
                                                    28. | Split of int * int array
                                                    29. | Draw of int * int
                                                    30. | Map of elt -> elt
                                                    31. | Fold of int * elt -> elt -> elt
                                                    32. | Scan of int * elt -> elt -> elt
                                                    33. | OneHot of int
                                                    34. | OfArray of int array
                                                    35. | Delay of Device.A.arr -> Device.A.arr
                                                    36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                    37. | LazyPrint of int option +Sig (owl-base.Owl_computation_type_sig.Sig)

                                                      Module type Owl_computation_type_sig.Sig

                                                      Type definition
                                                      type state =
                                                      1. | Valid
                                                      2. | Invalid
                                                        (*

                                                        TODO

                                                        *)

                                                      TODO

                                                      and block = {
                                                      1. size : int;
                                                      2. block_id : int;
                                                      3. mutable active : t option;
                                                      4. mutable memory : Device.value;
                                                      5. mutable nodes : t list;
                                                      }

                                                      block type keeps a reference to a block of memory and to the nodes sharing that block.

                                                      and attr = {
                                                      1. mutable op : op;
                                                      2. mutable freeze : bool;
                                                      3. mutable reuse : bool;
                                                      4. mutable state : state;
                                                      5. mutable shape : int array option array;
                                                      6. mutable value : Device.value array;
                                                      7. mutable block : block array option;
                                                      }

                                                      TODO

                                                      and arr =
                                                      1. | Arr of t
                                                      and elt =
                                                      1. | Elt of t
                                                      and op =
                                                      1. | Noop
                                                      2. | Var
                                                      3. | Const
                                                      4. | Empty of int array
                                                      5. | Zeros of int array
                                                      6. | Ones of int array
                                                      7. | Create of int array
                                                      8. | Sequential of int array
                                                      9. | Uniform of int array
                                                      10. | Gaussian of int array
                                                      11. | Bernoulli of int array
                                                      12. | Init of int array * int -> elt
                                                      13. | Get of int array
                                                      14. | Set of int array
                                                      15. | GetSlice of int list list
                                                      16. | SetSlice of int list list
                                                      17. | GetFancy of Owl_types_common.index list
                                                      18. | SetFancy of Owl_types_common.index list
                                                      19. | Copy
                                                      20. | Reset
                                                      21. | Reshape of int array
                                                      22. | Reverse
                                                      23. | Tile of int array
                                                      24. | Repeat of int array
                                                      25. | Pad of elt * int list list
                                                      26. | Concatenate of int
                                                      27. | Stack of int
                                                      28. | Split of int * int array
                                                      29. | Draw of int * int
                                                      30. | Map of elt -> elt
                                                      31. | Fold of int * elt -> elt -> elt
                                                      32. | Scan of int * elt -> elt -> elt
                                                      33. | OneHot of int
                                                      34. | OfArray of int array
                                                      35. | Delay of Device.A.arr -> Device.A.arr
                                                      36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                      37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                                      38. | Abs
                                                      39. | Neg
                                                      40. | Floor
                                                      41. | Ceil
                                                      42. | Round
                                                      43. | Sqr
                                                      44. | Sqrt
                                                      45. | Log
                                                      46. | Log2
                                                      47. | Log10
                                                      48. | Exp
                                                      49. | Sin
                                                      50. | Cos
                                                      51. | Tan
                                                      52. | Sinh
                                                      53. | Cosh
                                                      54. | Tanh
                                                      55. | Asin
                                                      56. | Acos
                                                      57. | Atan
                                                      58. | Asinh
                                                      59. | Acosh
                                                      60. | Atanh
                                                      61. | Min of bool * int
                                                      62. | Max of bool * int
                                                      63. | Sum of bool * int
                                                      64. | SumReduce of int array
                                                      65. | Signum
                                                      66. | Sigmoid
                                                      67. | Relu
                                                      68. | Dawsn
                                                      69. | Min'
                                                      70. | Max'
                                                      71. | Sum'
                                                      72. | LogSumExp'
                                                      73. | LogSumExp of bool * int
                                                      74. | L1norm'
                                                      75. | L2norm'
                                                      76. | L2NormSqr'
                                                      77. | ClipByValue
                                                      78. | ClipByL2norm
                                                      79. | Pow
                                                      80. | ScalarPow
                                                      81. | PowScalar
                                                      82. | Atan2
                                                      83. | ScalarAtan2
                                                      84. | Atan2Scalar
                                                      85. | Hypot
                                                      86. | Min2
                                                      87. | Max2
                                                      88. | Add
                                                      89. | Sub
                                                      90. | Mul
                                                      91. | Div
                                                      92. | AddScalar
                                                      93. | SubScalar
                                                      94. | MulScalar
                                                      95. | DivScalar
                                                      96. | ScalarAdd
                                                      97. | ScalarSub
                                                      98. | ScalarMul
                                                      99. | ScalarDiv
                                                      100. | FMA
                                                      101. | EltEqual
                                                      102. | EltNotEqual
                                                      103. | EltLess
                                                      104. | EltGreater
                                                      105. | EltLessEqual
                                                      106. | EltGreaterEqual
                                                      107. | EltEqualScalar
                                                      108. | EltNotEqualScalar
                                                      109. | EltLessScalar
                                                      110. | EltGreaterScalar
                                                      111. | EltLessEqualScalar
                                                      112. | EltGreaterEqualScalar
                                                      113. | Conv1d of Owl_types_common.padding * int array
                                                      114. | Conv2d of Owl_types_common.padding * int array
                                                      115. | Conv3d of Owl_types_common.padding * int array
                                                      116. | TransposeConv1d of Owl_types_common.padding * int array
                                                      117. | TransposeConv2d of Owl_types_common.padding * int array
                                                      118. | TransposeConv3d of Owl_types_common.padding * int array
                                                      119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                                      120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                                      121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                                      122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                                      123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                                      124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                                      125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                                      126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                                      127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                                      128. | UpSampling2d of int array
                                                      129. | Conv1dBackwardInput of int array
                                                      130. | Conv1dBackwardKernel of int array
                                                      131. | Conv2dBackwardInput of int array
                                                      132. | Conv2dBackwardKernel of int array
                                                      133. | Conv3dBackwardInput of int array
                                                      134. | Conv3dBackwardKernel of int array
                                                      135. | TransposeConv1dBackwardInput of int array
                                                      136. | TransposeConv1dBackwardKernel of int array
                                                      137. | TransposeConv2dBackwardInput of int array
                                                      138. | TransposeConv2dBackwardKernel of int array
                                                      139. | TransposeConv3dBackwardInput of int array
                                                      140. | TransposeConv3dBackwardKernel of int array
                                                      141. | DilatedConv1dBackwardInput of int array * int array
                                                      142. | DilatedConv1dBackwardKernel of int array * int array
                                                      143. | DilatedConv2dBackwardInput of int array * int array
                                                      144. | DilatedConv2dBackwardKernel of int array * int array
                                                      145. | DilatedConv3dBackwardInput of int array * int array
                                                      146. | DilatedConv3dBackwardKernel of int array * int array
                                                      147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                                      148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                                      149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                                      150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                                      151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                                      152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                                      153. | UpSampling2dBackward of int array
                                                      154. | RowNum
                                                      155. | ColNum
                                                      156. | Row
                                                      157. | Rows of int array
                                                      158. | CopyRowTo
                                                      159. | CopyColTo
                                                      160. | Dot of bool * bool * elt * elt
                                                      161. | Inv
                                                      162. | Trace
                                                      163. | Transpose of int array
                                                      164. | ToRows
                                                      165. | OfRows
                                                      166. | Scalar_Add
                                                      167. | Scalar_Sub
                                                      168. | Scalar_Mul
                                                      169. | Scalar_Div
                                                      170. | Scalar_Pow
                                                      171. | Scalar_Atan2
                                                      172. | Scalar_Abs
                                                      173. | Scalar_Neg
                                                      174. | Scalar_Sqr
                                                      175. | Scalar_Sqrt
                                                      176. | Scalar_Exp
                                                      177. | Scalar_Log
                                                      178. | Scalar_Log2
                                                      179. | Scalar_Log10
                                                      180. | Scalar_Signum
                                                      181. | Scalar_Floor
                                                      182. | Scalar_Ceil
                                                      183. | Scalar_Round
                                                      184. | Scalar_Sin
                                                      185. | Scalar_Cos
                                                      186. | Scalar_Tan
                                                      187. | Scalar_Sinh
                                                      188. | Scalar_Cosh
                                                      189. | Scalar_Tanh
                                                      190. | Scalar_Asin
                                                      191. | Scalar_Acos
                                                      192. | Scalar_Atan
                                                      193. | Scalar_Asinh
                                                      194. | Scalar_Acosh
                                                      195. | Scalar_Atanh
                                                      196. | Scalar_Relu
                                                      197. | Scalar_Dawsn
                                                      198. | Scalar_Sigmoid
                                                      199. | Fused_Adagrad of float * float
                                                        (*

                                                        TODO

                                                        *)
                                                      diff --git a/docs/owl-base/Owl_const/CGS/index.html b/docs/owl-base/Owl_const/CGS/index.html index 096314d6e..9691ab790 100644 --- a/docs/owl-base/Owl_const/CGS/index.html +++ b/docs/owl-base/Owl_const/CGS/index.html @@ -1,2 +1,2 @@ -CGS (owl-base.Owl_const.CGS)

                                                      Module Owl_const.CGS

                                                      val speed_of_light : float

                                                      speed_of_light = 2.99792458e10

                                                      val gravitational_constant : float

                                                      gravitational_constant = 6.673e-8

                                                      val plancks_constant_h : float

                                                      plancks_constant_h = 6.62606896e-27

                                                      val plancks_constant_hbar : float

                                                      plancks_constant_hbar = 1.05457162825e-27

                                                      val astronomical_unit : float

                                                      astronomical_unit = 1.49597870691e13

                                                      val light_year : float

                                                      light_year = 9.46053620707e17

                                                      val parsec : float

                                                      parsec = 3.08567758135e18

                                                      val grav_accel : float

                                                      grav_accel = 9.80665e2

                                                      val electron_volt : float

                                                      electron_volt = 1.602176487e-12

                                                      val mass_electron : float

                                                      mass_electron = 9.10938188e-28

                                                      val mass_muon : float

                                                      mass_muon = 1.88353109e-25

                                                      val mass_proton : float

                                                      mass_proton = 1.67262158e-24

                                                      val mass_neutron : float

                                                      mass_neutron = 1.67492716e-24

                                                      val rydberg : float

                                                      rydberg = 2.17987196968e-11

                                                      val boltzmann : float

                                                      boltzmann = 1.3806504e-16

                                                      val molar_gas : float

                                                      molar_gas = 8.314472e7

                                                      val standard_gas_volume : float

                                                      standard_gas_volume = 2.2710981e4

                                                      val minute : float

                                                      minute = 6e1

                                                      val hour : float

                                                      hour = 3.6e3

                                                      val day : float

                                                      day = 8.64e4

                                                      val week : float

                                                      week = 6.048e5

                                                      val inch : float

                                                      inch = 2.54e0

                                                      val foot : float

                                                      foot = 3.048e1

                                                      val yard : float

                                                      yard = 9.144e1

                                                      val mile : float

                                                      mile = 1.609344e5

                                                      val nautical_mile : float

                                                      nautical_mile = 1.852e5

                                                      val fathom : float

                                                      fathom = 1.8288e2

                                                      val mil : float

                                                      mil = 2.54e-3

                                                      val point : float

                                                      point = 3.52777777778e-2

                                                      val texpoint : float

                                                      texpoint = 3.51459803515e-2

                                                      val micron : float

                                                      micron = 1e-4

                                                      val angstrom : float

                                                      angstrom = 1e-8

                                                      val hectare : float

                                                      hectare = 1e8

                                                      val acre : float

                                                      acre = 4.04685642241e7

                                                      val barn : float

                                                      barn = 1e-24

                                                      val liter : float

                                                      liter = 1e3

                                                      val us_gallon : float

                                                      us_gallon = 3.78541178402e3

                                                      val quart : float

                                                      quart = 9.46352946004e2

                                                      val pint : float

                                                      pint = 4.73176473002e2

                                                      val cup : float

                                                      cup = 2.36588236501e2

                                                      val fluid_ounce : float

                                                      fluid_ounce = 2.95735295626e1

                                                      val tablespoon : float

                                                      tablespoon = 1.47867647813e1

                                                      val teaspoon : float

                                                      teaspoon = 4.92892159375e0

                                                      val canadian_gallon : float

                                                      canadian_gallon = 4.54609e3

                                                      val uk_gallon : float

                                                      uk_gallon = 4.546092e3

                                                      val miles_per_hour : float

                                                      miles_per_hour = 4.4704e1

                                                      val kilometers_per_hour : float

                                                      kilometers_per_hour = 2.77777777778e1

                                                      val knot : float

                                                      knot = 5.14444444444e1

                                                      val pound_mass : float

                                                      pound_mass = 4.5359237e2

                                                      val ounce_mass : float

                                                      ounce_mass = 2.8349523125e1

                                                      val ton : float

                                                      ton = 9.0718474e5

                                                      val metric_ton : float

                                                      metric_ton = 1e6

                                                      val uk_ton : float

                                                      uk_ton = 1.0160469088e6

                                                      val troy_ounce : float

                                                      troy_ounce = 3.1103475e1

                                                      val carat : float

                                                      carat = 2e-1

                                                      val unified_atomic_mass : float

                                                      unified_atomic_mass = 1.660538782e-24

                                                      val gram_force : float

                                                      gram_force = 9.80665e2

                                                      val pound_force : float

                                                      pound_force = 4.44822161526e5

                                                      val kilopound_force : float

                                                      kilopound_force = 4.44822161526e8

                                                      val poundal : float

                                                      poundal = 1.38255e4

                                                      val calorie : float

                                                      calorie = 4.1868e7

                                                      val btu : float

                                                      btu = 1.05505585262e10

                                                      val therm : float

                                                      therm = 1.05506e15

                                                      val horsepower : float

                                                      horsepower = 7.457e9

                                                      val bar : float

                                                      bar = 1e6

                                                      val std_atmosphere : float

                                                      std_atmosphere = 1.01325e6

                                                      val torr : float

                                                      torr = 1.33322368421e3

                                                      val meter_of_mercury : float

                                                      meter_of_mercury = 1.33322368421e6

                                                      val inch_of_mercury : float

                                                      inch_of_mercury = 3.38638815789e4

                                                      val inch_of_water : float

                                                      inch_of_water = 2.490889e3

                                                      val psi : float

                                                      psi = 6.89475729317e4

                                                      val poise : float

                                                      poise = 1e0

                                                      val stokes : float

                                                      stokes = 1e0

                                                      val stilb : float

                                                      stilb = 1e0

                                                      val lumen : float

                                                      lumen = 1e0

                                                      val lux : float

                                                      lux = 1e-4

                                                      val phot : float

                                                      phot = 1e0

                                                      val footcandle : float

                                                      footcandle = 1.076e-3

                                                      val lambert : float

                                                      lambert = 1e0

                                                      val footlambert : float

                                                      footlambert = 1.07639104e-3

                                                      val curie : float

                                                      curie = 3.7e10

                                                      val roentgen : float

                                                      roentgen = 2.58e-7

                                                      val rad : float

                                                      rad = 1e2

                                                      val solar_mass : float

                                                      solar_mass = 1.98892e33

                                                      val bohr_radius : float

                                                      bohr_radius = 5.291772083e-9

                                                      val newton : float

                                                      newton = 1e5

                                                      val dyne : float

                                                      dyne = 1e0

                                                      val joule : float

                                                      joule = 1e7

                                                      val erg : float

                                                      erg = 1e0

                                                      val stefan_boltzmann_constant : float

                                                      stefan_boltzmann_constant = 5.67040047374e-5

                                                      val thomson_cross_section : float

                                                      thomson_cross_section = 6.65245893699e-25

                                                      +CGS (owl-base.Owl_const.CGS)

                                                      Module Owl_const.CGS

                                                      val speed_of_light : float

                                                      speed_of_light = 2.99792458e10

                                                      val gravitational_constant : float

                                                      gravitational_constant = 6.673e-8

                                                      val plancks_constant_h : float

                                                      plancks_constant_h = 6.62606896e-27

                                                      val plancks_constant_hbar : float

                                                      plancks_constant_hbar = 1.05457162825e-27

                                                      val astronomical_unit : float

                                                      astronomical_unit = 1.49597870691e13

                                                      val light_year : float

                                                      light_year = 9.46053620707e17

                                                      val parsec : float

                                                      parsec = 3.08567758135e18

                                                      val grav_accel : float

                                                      grav_accel = 9.80665e2

                                                      val electron_volt : float

                                                      electron_volt = 1.602176487e-12

                                                      val mass_electron : float

                                                      mass_electron = 9.10938188e-28

                                                      val mass_muon : float

                                                      mass_muon = 1.88353109e-25

                                                      val mass_proton : float

                                                      mass_proton = 1.67262158e-24

                                                      val mass_neutron : float

                                                      mass_neutron = 1.67492716e-24

                                                      val rydberg : float

                                                      rydberg = 2.17987196968e-11

                                                      val boltzmann : float

                                                      boltzmann = 1.3806504e-16

                                                      val molar_gas : float

                                                      molar_gas = 8.314472e7

                                                      val standard_gas_volume : float

                                                      standard_gas_volume = 2.2710981e4

                                                      val minute : float

                                                      minute = 6e1

                                                      val hour : float

                                                      hour = 3.6e3

                                                      val day : float

                                                      day = 8.64e4

                                                      val week : float

                                                      week = 6.048e5

                                                      val inch : float

                                                      inch = 2.54e0

                                                      val foot : float

                                                      foot = 3.048e1

                                                      val yard : float

                                                      yard = 9.144e1

                                                      val mile : float

                                                      mile = 1.609344e5

                                                      val nautical_mile : float

                                                      nautical_mile = 1.852e5

                                                      val fathom : float

                                                      fathom = 1.8288e2

                                                      val mil : float

                                                      mil = 2.54e-3

                                                      val point : float

                                                      point = 3.52777777778e-2

                                                      val texpoint : float

                                                      texpoint = 3.51459803515e-2

                                                      val micron : float

                                                      micron = 1e-4

                                                      val angstrom : float

                                                      angstrom = 1e-8

                                                      val hectare : float

                                                      hectare = 1e8

                                                      val acre : float

                                                      acre = 4.04685642241e7

                                                      val barn : float

                                                      barn = 1e-24

                                                      val liter : float

                                                      liter = 1e3

                                                      val us_gallon : float

                                                      us_gallon = 3.78541178402e3

                                                      val quart : float

                                                      quart = 9.46352946004e2

                                                      val pint : float

                                                      pint = 4.73176473002e2

                                                      val cup : float

                                                      cup = 2.36588236501e2

                                                      val fluid_ounce : float

                                                      fluid_ounce = 2.95735295626e1

                                                      val tablespoon : float

                                                      tablespoon = 1.47867647813e1

                                                      val teaspoon : float

                                                      teaspoon = 4.92892159375e0

                                                      val canadian_gallon : float

                                                      canadian_gallon = 4.54609e3

                                                      val uk_gallon : float

                                                      uk_gallon = 4.546092e3

                                                      val miles_per_hour : float

                                                      miles_per_hour = 4.4704e1

                                                      val kilometers_per_hour : float

                                                      kilometers_per_hour = 2.77777777778e1

                                                      val knot : float

                                                      knot = 5.14444444444e1

                                                      val pound_mass : float

                                                      pound_mass = 4.5359237e2

                                                      val ounce_mass : float

                                                      ounce_mass = 2.8349523125e1

                                                      val ton : float

                                                      ton = 9.0718474e5

                                                      val metric_ton : float

                                                      metric_ton = 1e6

                                                      val uk_ton : float

                                                      uk_ton = 1.0160469088e6

                                                      val troy_ounce : float

                                                      troy_ounce = 3.1103475e1

                                                      val carat : float

                                                      carat = 2e-1

                                                      val unified_atomic_mass : float

                                                      unified_atomic_mass = 1.660538782e-24

                                                      val gram_force : float

                                                      gram_force = 9.80665e2

                                                      val pound_force : float

                                                      pound_force = 4.44822161526e5

                                                      val kilopound_force : float

                                                      kilopound_force = 4.44822161526e8

                                                      val poundal : float

                                                      poundal = 1.38255e4

                                                      val calorie : float

                                                      calorie = 4.1868e7

                                                      val btu : float

                                                      btu = 1.05505585262e10

                                                      val therm : float

                                                      therm = 1.05506e15

                                                      val horsepower : float

                                                      horsepower = 7.457e9

                                                      val bar : float

                                                      bar = 1e6

                                                      val std_atmosphere : float

                                                      std_atmosphere = 1.01325e6

                                                      val torr : float

                                                      torr = 1.33322368421e3

                                                      val meter_of_mercury : float

                                                      meter_of_mercury = 1.33322368421e6

                                                      val inch_of_mercury : float

                                                      inch_of_mercury = 3.38638815789e4

                                                      val inch_of_water : float

                                                      inch_of_water = 2.490889e3

                                                      val psi : float

                                                      psi = 6.89475729317e4

                                                      val poise : float

                                                      poise = 1e0

                                                      val stokes : float

                                                      stokes = 1e0

                                                      val stilb : float

                                                      stilb = 1e0

                                                      val lumen : float

                                                      lumen = 1e0

                                                      val lux : float

                                                      lux = 1e-4

                                                      val phot : float

                                                      phot = 1e0

                                                      val footcandle : float

                                                      footcandle = 1.076e-3

                                                      val lambert : float

                                                      lambert = 1e0

                                                      val footlambert : float

                                                      footlambert = 1.07639104e-3

                                                      val curie : float

                                                      curie = 3.7e10

                                                      val roentgen : float

                                                      roentgen = 2.58e-7

                                                      val rad : float

                                                      rad = 1e2

                                                      val solar_mass : float

                                                      solar_mass = 1.98892e33

                                                      val bohr_radius : float

                                                      bohr_radius = 5.291772083e-9

                                                      val newton : float

                                                      newton = 1e5

                                                      val dyne : float

                                                      dyne = 1e0

                                                      val joule : float

                                                      joule = 1e7

                                                      val erg : float

                                                      erg = 1e0

                                                      val stefan_boltzmann_constant : float

                                                      stefan_boltzmann_constant = 5.67040047374e-5

                                                      val thomson_cross_section : float

                                                      thomson_cross_section = 6.65245893699e-25

                                                      diff --git a/docs/owl-base/Owl_const/CGSM/index.html b/docs/owl-base/Owl_const/CGSM/index.html index 55fb34036..635fb3f59 100644 --- a/docs/owl-base/Owl_const/CGSM/index.html +++ b/docs/owl-base/Owl_const/CGSM/index.html @@ -1,2 +1,2 @@ -CGSM (owl-base.Owl_const.CGSM)

                                                      Module Owl_const.CGSM

                                                      val speed_of_light : float

                                                      speed_of_light = 2.99792458e10

                                                      val gravitational_constant : float

                                                      gravitational_constant = 6.673e-8

                                                      val plancks_constant_h : float

                                                      plancks_constant_h = 6.62606896e-27

                                                      val plancks_constant_hbar : float

                                                      plancks_constant_hbar = 1.05457162825e-27

                                                      val astronomical_unit : float

                                                      astronomical_unit = 1.49597870691e13

                                                      val light_year : float

                                                      light_year = 9.46053620707e17

                                                      val parsec : float

                                                      parsec = 3.08567758135e18

                                                      val grav_accel : float

                                                      grav_accel = 9.80665e2

                                                      val electron_volt : float

                                                      electron_volt = 1.602176487e-12

                                                      val mass_electron : float

                                                      mass_electron = 9.10938188e-28

                                                      val mass_muon : float

                                                      mass_muon = 1.88353109e-25

                                                      val mass_proton : float

                                                      mass_proton = 1.67262158e-24

                                                      val mass_neutron : float

                                                      mass_neutron = 1.67492716e-24

                                                      val rydberg : float

                                                      rydberg = 2.17987196968e-11

                                                      val boltzmann : float

                                                      boltzmann = 1.3806504e-16

                                                      val molar_gas : float

                                                      molar_gas = 8.314472e7

                                                      val standard_gas_volume : float

                                                      standard_gas_volume = 2.2710981e4

                                                      val minute : float

                                                      minute = 6e1

                                                      val hour : float

                                                      hour = 3.6e3

                                                      val day : float

                                                      day = 8.64e4

                                                      val week : float

                                                      week = 6.048e5

                                                      val inch : float

                                                      inch = 2.54e0

                                                      val foot : float

                                                      foot = 3.048e1

                                                      val yard : float

                                                      yard = 9.144e1

                                                      val mile : float

                                                      mile = 1.609344e5

                                                      val nautical_mile : float

                                                      nautical_mile = 1.852e5

                                                      val fathom : float

                                                      fathom = 1.8288e2

                                                      val mil : float

                                                      mil = 2.54e-3

                                                      val point : float

                                                      point = 3.52777777778e-2

                                                      val texpoint : float

                                                      texpoint = 3.51459803515e-2

                                                      val micron : float

                                                      micron = 1e-4

                                                      val angstrom : float

                                                      angstrom = 1e-8

                                                      val hectare : float

                                                      hectare = 1e8

                                                      val acre : float

                                                      acre = 4.04685642241e7

                                                      val barn : float

                                                      barn = 1e-24

                                                      val liter : float

                                                      liter = 1e3

                                                      val us_gallon : float

                                                      us_gallon = 3.78541178402e3

                                                      val quart : float

                                                      quart = 9.46352946004e2

                                                      val pint : float

                                                      pint = 4.73176473002e2

                                                      val cup : float

                                                      cup = 2.36588236501e2

                                                      val fluid_ounce : float

                                                      fluid_ounce = 2.95735295626e1

                                                      val tablespoon : float

                                                      tablespoon = 1.47867647813e1

                                                      val teaspoon : float

                                                      teaspoon = 4.92892159375e0

                                                      val canadian_gallon : float

                                                      canadian_gallon = 4.54609e3

                                                      val uk_gallon : float

                                                      uk_gallon = 4.546092e3

                                                      val miles_per_hour : float

                                                      miles_per_hour = 4.4704e1

                                                      val kilometers_per_hour : float

                                                      kilometers_per_hour = 2.77777777778e1

                                                      val knot : float

                                                      knot = 5.14444444444e1

                                                      val pound_mass : float

                                                      pound_mass = 4.5359237e2

                                                      val ounce_mass : float

                                                      ounce_mass = 2.8349523125e1

                                                      val ton : float

                                                      ton = 9.0718474e5

                                                      val metric_ton : float

                                                      metric_ton = 1e6

                                                      val uk_ton : float

                                                      uk_ton = 1.0160469088e6

                                                      val troy_ounce : float

                                                      troy_ounce = 3.1103475e1

                                                      val carat : float

                                                      carat = 2e-1

                                                      val unified_atomic_mass : float

                                                      unified_atomic_mass = 1.660538782e-24

                                                      val gram_force : float

                                                      gram_force = 9.80665e2

                                                      val pound_force : float

                                                      pound_force = 4.44822161526e5

                                                      val kilopound_force : float

                                                      kilopound_force = 4.44822161526e8

                                                      val poundal : float

                                                      poundal = 1.38255e4

                                                      val calorie : float

                                                      calorie = 4.1868e7

                                                      val btu : float

                                                      btu = 1.05505585262e10

                                                      val therm : float

                                                      therm = 1.05506e15

                                                      val horsepower : float

                                                      horsepower = 7.457e9

                                                      val bar : float

                                                      bar = 1e6

                                                      val std_atmosphere : float

                                                      std_atmosphere = 1.01325e6

                                                      val torr : float

                                                      torr = 1.33322368421e3

                                                      val meter_of_mercury : float

                                                      meter_of_mercury = 1.33322368421e6

                                                      val inch_of_mercury : float

                                                      inch_of_mercury = 3.38638815789e4

                                                      val inch_of_water : float

                                                      inch_of_water = 2.490889e3

                                                      val psi : float

                                                      psi = 6.89475729317e4

                                                      val poise : float

                                                      poise = 1e0

                                                      val stokes : float

                                                      stokes = 1e0

                                                      val stilb : float

                                                      stilb = 1e0

                                                      val lumen : float

                                                      lumen = 1e0

                                                      val lux : float

                                                      lux = 1e-4

                                                      val phot : float

                                                      phot = 1e0

                                                      val footcandle : float

                                                      footcandle = 1.076e-3

                                                      val lambert : float

                                                      lambert = 1e0

                                                      val footlambert : float

                                                      footlambert = 1.07639104e-3

                                                      val curie : float

                                                      curie = 3.7e10

                                                      val roentgen : float

                                                      roentgen = 2.58e-8

                                                      val rad : float

                                                      rad = 1e2

                                                      val solar_mass : float

                                                      solar_mass = 1.98892e33

                                                      val bohr_radius : float

                                                      bohr_radius = 5.291772083e-9

                                                      val newton : float

                                                      newton = 1e5

                                                      val dyne : float

                                                      dyne = 1e0

                                                      val joule : float

                                                      joule = 1e7

                                                      val erg : float

                                                      erg = 1e0

                                                      val stefan_boltzmann_constant : float

                                                      stefan_boltzmann_constant = 5.67040047374e-5

                                                      val thomson_cross_section : float

                                                      thomson_cross_section = 6.65245893699e-25

                                                      val bohr_magneton : float

                                                      bohr_magneton = 9.27400899e-21

                                                      val nuclear_magneton : float

                                                      nuclear_magneton = 5.05078317e-24

                                                      val electron_magnetic_moment : float

                                                      electron_magnetic_moment = 9.28476362e-21

                                                      val proton_magnetic_moment : float

                                                      proton_magnetic_moment = 1.410606633e-23

                                                      val faraday : float

                                                      faraday = 9.64853429775e3

                                                      val electron_charge : float

                                                      electron_charge = 1.602176487e-20

                                                      +CGSM (owl-base.Owl_const.CGSM)

                                                      Module Owl_const.CGSM

                                                      val speed_of_light : float

                                                      speed_of_light = 2.99792458e10

                                                      val gravitational_constant : float

                                                      gravitational_constant = 6.673e-8

                                                      val plancks_constant_h : float

                                                      plancks_constant_h = 6.62606896e-27

                                                      val plancks_constant_hbar : float

                                                      plancks_constant_hbar = 1.05457162825e-27

                                                      val astronomical_unit : float

                                                      astronomical_unit = 1.49597870691e13

                                                      val light_year : float

                                                      light_year = 9.46053620707e17

                                                      val parsec : float

                                                      parsec = 3.08567758135e18

                                                      val grav_accel : float

                                                      grav_accel = 9.80665e2

                                                      val electron_volt : float

                                                      electron_volt = 1.602176487e-12

                                                      val mass_electron : float

                                                      mass_electron = 9.10938188e-28

                                                      val mass_muon : float

                                                      mass_muon = 1.88353109e-25

                                                      val mass_proton : float

                                                      mass_proton = 1.67262158e-24

                                                      val mass_neutron : float

                                                      mass_neutron = 1.67492716e-24

                                                      val rydberg : float

                                                      rydberg = 2.17987196968e-11

                                                      val boltzmann : float

                                                      boltzmann = 1.3806504e-16

                                                      val molar_gas : float

                                                      molar_gas = 8.314472e7

                                                      val standard_gas_volume : float

                                                      standard_gas_volume = 2.2710981e4

                                                      val minute : float

                                                      minute = 6e1

                                                      val hour : float

                                                      hour = 3.6e3

                                                      val day : float

                                                      day = 8.64e4

                                                      val week : float

                                                      week = 6.048e5

                                                      val inch : float

                                                      inch = 2.54e0

                                                      val foot : float

                                                      foot = 3.048e1

                                                      val yard : float

                                                      yard = 9.144e1

                                                      val mile : float

                                                      mile = 1.609344e5

                                                      val nautical_mile : float

                                                      nautical_mile = 1.852e5

                                                      val fathom : float

                                                      fathom = 1.8288e2

                                                      val mil : float

                                                      mil = 2.54e-3

                                                      val point : float

                                                      point = 3.52777777778e-2

                                                      val texpoint : float

                                                      texpoint = 3.51459803515e-2

                                                      val micron : float

                                                      micron = 1e-4

                                                      val angstrom : float

                                                      angstrom = 1e-8

                                                      val hectare : float

                                                      hectare = 1e8

                                                      val acre : float

                                                      acre = 4.04685642241e7

                                                      val barn : float

                                                      barn = 1e-24

                                                      val liter : float

                                                      liter = 1e3

                                                      val us_gallon : float

                                                      us_gallon = 3.78541178402e3

                                                      val quart : float

                                                      quart = 9.46352946004e2

                                                      val pint : float

                                                      pint = 4.73176473002e2

                                                      val cup : float

                                                      cup = 2.36588236501e2

                                                      val fluid_ounce : float

                                                      fluid_ounce = 2.95735295626e1

                                                      val tablespoon : float

                                                      tablespoon = 1.47867647813e1

                                                      val teaspoon : float

                                                      teaspoon = 4.92892159375e0

                                                      val canadian_gallon : float

                                                      canadian_gallon = 4.54609e3

                                                      val uk_gallon : float

                                                      uk_gallon = 4.546092e3

                                                      val miles_per_hour : float

                                                      miles_per_hour = 4.4704e1

                                                      val kilometers_per_hour : float

                                                      kilometers_per_hour = 2.77777777778e1

                                                      val knot : float

                                                      knot = 5.14444444444e1

                                                      val pound_mass : float

                                                      pound_mass = 4.5359237e2

                                                      val ounce_mass : float

                                                      ounce_mass = 2.8349523125e1

                                                      val ton : float

                                                      ton = 9.0718474e5

                                                      val metric_ton : float

                                                      metric_ton = 1e6

                                                      val uk_ton : float

                                                      uk_ton = 1.0160469088e6

                                                      val troy_ounce : float

                                                      troy_ounce = 3.1103475e1

                                                      val carat : float

                                                      carat = 2e-1

                                                      val unified_atomic_mass : float

                                                      unified_atomic_mass = 1.660538782e-24

                                                      val gram_force : float

                                                      gram_force = 9.80665e2

                                                      val pound_force : float

                                                      pound_force = 4.44822161526e5

                                                      val kilopound_force : float

                                                      kilopound_force = 4.44822161526e8

                                                      val poundal : float

                                                      poundal = 1.38255e4

                                                      val calorie : float

                                                      calorie = 4.1868e7

                                                      val btu : float

                                                      btu = 1.05505585262e10

                                                      val therm : float

                                                      therm = 1.05506e15

                                                      val horsepower : float

                                                      horsepower = 7.457e9

                                                      val bar : float

                                                      bar = 1e6

                                                      val std_atmosphere : float

                                                      std_atmosphere = 1.01325e6

                                                      val torr : float

                                                      torr = 1.33322368421e3

                                                      val meter_of_mercury : float

                                                      meter_of_mercury = 1.33322368421e6

                                                      val inch_of_mercury : float

                                                      inch_of_mercury = 3.38638815789e4

                                                      val inch_of_water : float

                                                      inch_of_water = 2.490889e3

                                                      val psi : float

                                                      psi = 6.89475729317e4

                                                      val poise : float

                                                      poise = 1e0

                                                      val stokes : float

                                                      stokes = 1e0

                                                      val stilb : float

                                                      stilb = 1e0

                                                      val lumen : float

                                                      lumen = 1e0

                                                      val lux : float

                                                      lux = 1e-4

                                                      val phot : float

                                                      phot = 1e0

                                                      val footcandle : float

                                                      footcandle = 1.076e-3

                                                      val lambert : float

                                                      lambert = 1e0

                                                      val footlambert : float

                                                      footlambert = 1.07639104e-3

                                                      val curie : float

                                                      curie = 3.7e10

                                                      val roentgen : float

                                                      roentgen = 2.58e-8

                                                      val rad : float

                                                      rad = 1e2

                                                      val solar_mass : float

                                                      solar_mass = 1.98892e33

                                                      val bohr_radius : float

                                                      bohr_radius = 5.291772083e-9

                                                      val newton : float

                                                      newton = 1e5

                                                      val dyne : float

                                                      dyne = 1e0

                                                      val joule : float

                                                      joule = 1e7

                                                      val erg : float

                                                      erg = 1e0

                                                      val stefan_boltzmann_constant : float

                                                      stefan_boltzmann_constant = 5.67040047374e-5

                                                      val thomson_cross_section : float

                                                      thomson_cross_section = 6.65245893699e-25

                                                      val bohr_magneton : float

                                                      bohr_magneton = 9.27400899e-21

                                                      val nuclear_magneton : float

                                                      nuclear_magneton = 5.05078317e-24

                                                      val electron_magnetic_moment : float

                                                      electron_magnetic_moment = 9.28476362e-21

                                                      val proton_magnetic_moment : float

                                                      proton_magnetic_moment = 1.410606633e-23

                                                      val faraday : float

                                                      faraday = 9.64853429775e3

                                                      val electron_charge : float

                                                      electron_charge = 1.602176487e-20

                                                      diff --git a/docs/owl-base/Owl_const/MKS/index.html b/docs/owl-base/Owl_const/MKS/index.html index 3396e130d..44077705b 100644 --- a/docs/owl-base/Owl_const/MKS/index.html +++ b/docs/owl-base/Owl_const/MKS/index.html @@ -1,2 +1,2 @@ -MKS (owl-base.Owl_const.MKS)

                                                      Module Owl_const.MKS

                                                      val speed_of_light : float

                                                      speed_of_light = 2.99792458e8

                                                      val gravitational_constant : float

                                                      gravitational_constant = 6.673e-11

                                                      val plancks_constant_h : float

                                                      plancks_constant_h = 6.62606896e-34

                                                      val plancks_constant_hbar : float

                                                      plancks_constant_hbar = 1.05457162825e-34

                                                      val astronomical_unit : float

                                                      astronomical_unit = 1.49597870691e11

                                                      val light_year : float

                                                      light_year = 9.46053620707e15

                                                      val parsec : float

                                                      parsec = 3.08567758135e16

                                                      val grav_accel : float

                                                      grav_accel = 9.80665e0

                                                      val electron_volt : float

                                                      electron_volt = 1.602176487e-19

                                                      val mass_electron : float

                                                      mass_electron = 9.10938188e-31

                                                      val mass_muon : float

                                                      mass_muon = 1.88353109e-28

                                                      val mass_proton : float

                                                      mass_proton = 1.67262158e-27

                                                      val mass_neutron : float

                                                      mass_neutron = 1.67492716e-27

                                                      val rydberg : float

                                                      rydberg = 2.17987196968e-18

                                                      val boltzmann : float

                                                      boltzmann = 1.3806504e-23

                                                      val molar_gas : float

                                                      molar_gas = 8.314472e0

                                                      val standard_gas_volume : float

                                                      standard_gas_volume = 2.2710981e-2

                                                      val minute : float

                                                      minute = 6e1

                                                      val hour : float

                                                      hour = 3.6e3

                                                      val day : float

                                                      day = 8.64e4

                                                      val week : float

                                                      week = 6.048e5

                                                      val inch : float

                                                      inch = 2.54e-2

                                                      val foot : float

                                                      foot = 3.048e-1

                                                      val yard : float

                                                      yard = 9.144e-1

                                                      val mile : float

                                                      mile = 1.609344e3

                                                      val nautical_mile : float

                                                      nautical_mile = 1.852e3

                                                      val fathom : float

                                                      fathom = 1.8288e0

                                                      val mil : float

                                                      mil = 2.54e-5

                                                      val point : float

                                                      point = 3.52777777778e-4

                                                      val texpoint : float

                                                      texpoint = 3.51459803515e-4

                                                      val micron : float

                                                      micron = 1e-6

                                                      val angstrom : float

                                                      angstrom = 1e-10

                                                      val hectare : float

                                                      hectare = 1e4

                                                      val acre : float

                                                      acre = 4.04685642241e3

                                                      val barn : float

                                                      barn = 1e-28

                                                      val liter : float

                                                      liter = 1e-3

                                                      val us_gallon : float

                                                      us_gallon = 3.78541178402e-3

                                                      val quart : float

                                                      quart = 9.46352946004e-4

                                                      val pint : float

                                                      pint = 4.73176473002e-4

                                                      val cup : float

                                                      cup = 2.36588236501e-4

                                                      val fluid_ounce : float

                                                      fluid_ounce = 2.95735295626e-5

                                                      val tablespoon : float

                                                      tablespoon = 1.47867647813e-5

                                                      val teaspoon : float

                                                      teaspoon = 4.92892159375e-6

                                                      val canadian_gallon : float

                                                      canadian_gallon = 4.54609e-3

                                                      val uk_gallon : float

                                                      uk_gallon = 4.546092e-3

                                                      val miles_per_hour : float

                                                      miles_per_hour = 4.4704e-1

                                                      val kilometers_per_hour : float

                                                      kilometers_per_hour = 2.77777777778e-1

                                                      val knot : float

                                                      knot = 5.14444444444e-1

                                                      val pound_mass : float

                                                      pound_mass = 4.5359237e-1

                                                      val ounce_mass : float

                                                      ounce_mass = 2.8349523125e-2

                                                      val ton : float

                                                      ton = 9.0718474e2

                                                      val metric_ton : float

                                                      metric_ton = 1e3

                                                      val uk_ton : float

                                                      uk_ton = 1.0160469088e3

                                                      val troy_ounce : float

                                                      troy_ounce = 3.1103475e-2

                                                      val carat : float

                                                      carat = 2e-4

                                                      val unified_atomic_mass : float

                                                      unified_atomic_mass = 1.660538782e-27

                                                      val gram_force : float

                                                      gram_force = 9.80665e-3

                                                      val pound_force : float

                                                      pound_force = 4.44822161526e0

                                                      val kilopound_force : float

                                                      kilopound_force = 4.44822161526e3

                                                      val poundal : float

                                                      poundal = 1.38255e-1

                                                      val calorie : float

                                                      calorie = 4.1868e0

                                                      val btu : float

                                                      btu = 1.05505585262e3

                                                      val therm : float

                                                      therm = 1.05506e8

                                                      val horsepower : float

                                                      horsepower = 7.457e2

                                                      val bar : float

                                                      bar = 1e5

                                                      val std_atmosphere : float

                                                      std_atmosphere = 1.01325e5

                                                      val torr : float

                                                      torr = 1.33322368421e2

                                                      val meter_of_mercury : float

                                                      meter_of_mercury = 1.33322368421e5

                                                      val inch_of_mercury : float

                                                      inch_of_mercury = 3.38638815789e3

                                                      val inch_of_water : float

                                                      inch_of_water = 2.490889e2

                                                      val psi : float

                                                      psi = 6.89475729317e3

                                                      val poise : float

                                                      poise = 1e-1

                                                      val stokes : float

                                                      stokes = 1e-4

                                                      val stilb : float

                                                      stilb = 1e4

                                                      val lumen : float

                                                      lumen = 1e0

                                                      val lux : float

                                                      lux = 1e0

                                                      val phot : float

                                                      phot = 1e4

                                                      val footcandle : float

                                                      footcandle = 1.076e1

                                                      val lambert : float

                                                      lambert = 1e4

                                                      val footlambert : float

                                                      footlambert = 1.07639104e1

                                                      val curie : float

                                                      curie = 3.7e10

                                                      val roentgen : float

                                                      roentgen = 2.58e-4

                                                      val rad : float

                                                      rad = 1e-2

                                                      val solar_mass : float

                                                      solar_mass = 1.98892e30

                                                      val bohr_radius : float

                                                      bohr_radius = 5.291772083e-11

                                                      val newton : float

                                                      newton = 1e0

                                                      val dyne : float

                                                      dyne = 1e-5

                                                      val joule : float

                                                      joule = 1e0

                                                      val erg : float

                                                      erg = 1e-7

                                                      val stefan_boltzmann_constant : float

                                                      stefan_boltzmann_constant = 5.67040047374e-8

                                                      val thomson_cross_section : float

                                                      thomson_cross_section = 6.65245893699e-29

                                                      val bohr_magneton : float

                                                      bohr_magneton = 9.27400899e-24

                                                      val nuclear_magneton : float

                                                      nuclear_magneton = 5.05078317e-27

                                                      val electron_magnetic_moment : float

                                                      electron_magnetic_moment = 9.28476362e-24

                                                      val proton_magnetic_moment : float

                                                      proton_magnetic_moment = 1.410606633e-26

                                                      val faraday : float

                                                      faraday = 9.64853429775e4

                                                      val electron_charge : float

                                                      electron_charge = 1.602176487e-19

                                                      val vacuum_permittivity : float

                                                      vacuum_permittivity = 8.854187817e-12

                                                      val vacuum_permeability : float

                                                      vacuum_permeability = 1.25663706144e-6

                                                      val debye : float

                                                      debye = 3.33564095198e-30

                                                      val gauss : float

                                                      gauss = 1e-4

                                                      +MKS (owl-base.Owl_const.MKS)

                                                      Module Owl_const.MKS

                                                      val speed_of_light : float

                                                      speed_of_light = 2.99792458e8

                                                      val gravitational_constant : float

                                                      gravitational_constant = 6.673e-11

                                                      val plancks_constant_h : float

                                                      plancks_constant_h = 6.62606896e-34

                                                      val plancks_constant_hbar : float

                                                      plancks_constant_hbar = 1.05457162825e-34

                                                      val astronomical_unit : float

                                                      astronomical_unit = 1.49597870691e11

                                                      val light_year : float

                                                      light_year = 9.46053620707e15

                                                      val parsec : float

                                                      parsec = 3.08567758135e16

                                                      val grav_accel : float

                                                      grav_accel = 9.80665e0

                                                      val electron_volt : float

                                                      electron_volt = 1.602176487e-19

                                                      val mass_electron : float

                                                      mass_electron = 9.10938188e-31

                                                      val mass_muon : float

                                                      mass_muon = 1.88353109e-28

                                                      val mass_proton : float

                                                      mass_proton = 1.67262158e-27

                                                      val mass_neutron : float

                                                      mass_neutron = 1.67492716e-27

                                                      val rydberg : float

                                                      rydberg = 2.17987196968e-18

                                                      val boltzmann : float

                                                      boltzmann = 1.3806504e-23

                                                      val molar_gas : float

                                                      molar_gas = 8.314472e0

                                                      val standard_gas_volume : float

                                                      standard_gas_volume = 2.2710981e-2

                                                      val minute : float

                                                      minute = 6e1

                                                      val hour : float

                                                      hour = 3.6e3

                                                      val day : float

                                                      day = 8.64e4

                                                      val week : float

                                                      week = 6.048e5

                                                      val inch : float

                                                      inch = 2.54e-2

                                                      val foot : float

                                                      foot = 3.048e-1

                                                      val yard : float

                                                      yard = 9.144e-1

                                                      val mile : float

                                                      mile = 1.609344e3

                                                      val nautical_mile : float

                                                      nautical_mile = 1.852e3

                                                      val fathom : float

                                                      fathom = 1.8288e0

                                                      val mil : float

                                                      mil = 2.54e-5

                                                      val point : float

                                                      point = 3.52777777778e-4

                                                      val texpoint : float

                                                      texpoint = 3.51459803515e-4

                                                      val micron : float

                                                      micron = 1e-6

                                                      val angstrom : float

                                                      angstrom = 1e-10

                                                      val hectare : float

                                                      hectare = 1e4

                                                      val acre : float

                                                      acre = 4.04685642241e3

                                                      val barn : float

                                                      barn = 1e-28

                                                      val liter : float

                                                      liter = 1e-3

                                                      val us_gallon : float

                                                      us_gallon = 3.78541178402e-3

                                                      val quart : float

                                                      quart = 9.46352946004e-4

                                                      val pint : float

                                                      pint = 4.73176473002e-4

                                                      val cup : float

                                                      cup = 2.36588236501e-4

                                                      val fluid_ounce : float

                                                      fluid_ounce = 2.95735295626e-5

                                                      val tablespoon : float

                                                      tablespoon = 1.47867647813e-5

                                                      val teaspoon : float

                                                      teaspoon = 4.92892159375e-6

                                                      val canadian_gallon : float

                                                      canadian_gallon = 4.54609e-3

                                                      val uk_gallon : float

                                                      uk_gallon = 4.546092e-3

                                                      val miles_per_hour : float

                                                      miles_per_hour = 4.4704e-1

                                                      val kilometers_per_hour : float

                                                      kilometers_per_hour = 2.77777777778e-1

                                                      val knot : float

                                                      knot = 5.14444444444e-1

                                                      val pound_mass : float

                                                      pound_mass = 4.5359237e-1

                                                      val ounce_mass : float

                                                      ounce_mass = 2.8349523125e-2

                                                      val ton : float

                                                      ton = 9.0718474e2

                                                      val metric_ton : float

                                                      metric_ton = 1e3

                                                      val uk_ton : float

                                                      uk_ton = 1.0160469088e3

                                                      val troy_ounce : float

                                                      troy_ounce = 3.1103475e-2

                                                      val carat : float

                                                      carat = 2e-4

                                                      val unified_atomic_mass : float

                                                      unified_atomic_mass = 1.660538782e-27

                                                      val gram_force : float

                                                      gram_force = 9.80665e-3

                                                      val pound_force : float

                                                      pound_force = 4.44822161526e0

                                                      val kilopound_force : float

                                                      kilopound_force = 4.44822161526e3

                                                      val poundal : float

                                                      poundal = 1.38255e-1

                                                      val calorie : float

                                                      calorie = 4.1868e0

                                                      val btu : float

                                                      btu = 1.05505585262e3

                                                      val therm : float

                                                      therm = 1.05506e8

                                                      val horsepower : float

                                                      horsepower = 7.457e2

                                                      val bar : float

                                                      bar = 1e5

                                                      val std_atmosphere : float

                                                      std_atmosphere = 1.01325e5

                                                      val torr : float

                                                      torr = 1.33322368421e2

                                                      val meter_of_mercury : float

                                                      meter_of_mercury = 1.33322368421e5

                                                      val inch_of_mercury : float

                                                      inch_of_mercury = 3.38638815789e3

                                                      val inch_of_water : float

                                                      inch_of_water = 2.490889e2

                                                      val psi : float

                                                      psi = 6.89475729317e3

                                                      val poise : float

                                                      poise = 1e-1

                                                      val stokes : float

                                                      stokes = 1e-4

                                                      val stilb : float

                                                      stilb = 1e4

                                                      val lumen : float

                                                      lumen = 1e0

                                                      val lux : float

                                                      lux = 1e0

                                                      val phot : float

                                                      phot = 1e4

                                                      val footcandle : float

                                                      footcandle = 1.076e1

                                                      val lambert : float

                                                      lambert = 1e4

                                                      val footlambert : float

                                                      footlambert = 1.07639104e1

                                                      val curie : float

                                                      curie = 3.7e10

                                                      val roentgen : float

                                                      roentgen = 2.58e-4

                                                      val rad : float

                                                      rad = 1e-2

                                                      val solar_mass : float

                                                      solar_mass = 1.98892e30

                                                      val bohr_radius : float

                                                      bohr_radius = 5.291772083e-11

                                                      val newton : float

                                                      newton = 1e0

                                                      val dyne : float

                                                      dyne = 1e-5

                                                      val joule : float

                                                      joule = 1e0

                                                      val erg : float

                                                      erg = 1e-7

                                                      val stefan_boltzmann_constant : float

                                                      stefan_boltzmann_constant = 5.67040047374e-8

                                                      val thomson_cross_section : float

                                                      thomson_cross_section = 6.65245893699e-29

                                                      val bohr_magneton : float

                                                      bohr_magneton = 9.27400899e-24

                                                      val nuclear_magneton : float

                                                      nuclear_magneton = 5.05078317e-27

                                                      val electron_magnetic_moment : float

                                                      electron_magnetic_moment = 9.28476362e-24

                                                      val proton_magnetic_moment : float

                                                      proton_magnetic_moment = 1.410606633e-26

                                                      val faraday : float

                                                      faraday = 9.64853429775e4

                                                      val electron_charge : float

                                                      electron_charge = 1.602176487e-19

                                                      val vacuum_permittivity : float

                                                      vacuum_permittivity = 8.854187817e-12

                                                      val vacuum_permeability : float

                                                      vacuum_permeability = 1.25663706144e-6

                                                      val debye : float

                                                      debye = 3.33564095198e-30

                                                      val gauss : float

                                                      gauss = 1e-4

                                                      diff --git a/docs/owl-base/Owl_const/Prefix/index.html b/docs/owl-base/Owl_const/Prefix/index.html index 0d7e18a4a..9c3e68278 100644 --- a/docs/owl-base/Owl_const/Prefix/index.html +++ b/docs/owl-base/Owl_const/Prefix/index.html @@ -1,2 +1,2 @@ -Prefix (owl-base.Owl_const.Prefix)

                                                      Module Owl_const.Prefix

                                                      val fine_structure : float

                                                      fine_structure = 7.297352533e-3

                                                      val avogadro : float

                                                      avogadro = 6.02214199e23

                                                      val yotta : float

                                                      yotta = 1e24

                                                      val zetta : float

                                                      zetta = 1e21

                                                      val exa : float

                                                      exa = 1e18

                                                      val peta : float

                                                      peta = 1e15

                                                      val tera : float

                                                      tera = 1e12

                                                      val giga : float

                                                      giga = 1e9

                                                      val mega : float

                                                      mega = 1e6

                                                      val kilo : float

                                                      kilo = 1e3

                                                      val hecto : float

                                                      hecto = 1e2

                                                      val deca : float

                                                      deca = 1e1

                                                      val deci : float

                                                      deci = 1e-1

                                                      val centi : float

                                                      centi = 1e-2

                                                      val milli : float

                                                      milli = 1e-3

                                                      val micro : float

                                                      micro = 1e-6

                                                      val nano : float

                                                      nano = 1e-9

                                                      val pico : float

                                                      pico = 1e-12

                                                      val femto : float

                                                      femto = 1e-15

                                                      val atto : float

                                                      atto = 1e-18

                                                      val zepto : float

                                                      zepto = 1e-21

                                                      val yocto : float

                                                      yocto = 1e-24

                                                      +Prefix (owl-base.Owl_const.Prefix)

                                                      Module Owl_const.Prefix

                                                      val fine_structure : float

                                                      fine_structure = 7.297352533e-3

                                                      val avogadro : float

                                                      avogadro = 6.02214199e23

                                                      val yotta : float

                                                      yotta = 1e24

                                                      val zetta : float

                                                      zetta = 1e21

                                                      val exa : float

                                                      exa = 1e18

                                                      val peta : float

                                                      peta = 1e15

                                                      val tera : float

                                                      tera = 1e12

                                                      val giga : float

                                                      giga = 1e9

                                                      val mega : float

                                                      mega = 1e6

                                                      val kilo : float

                                                      kilo = 1e3

                                                      val hecto : float

                                                      hecto = 1e2

                                                      val deca : float

                                                      deca = 1e1

                                                      val deci : float

                                                      deci = 1e-1

                                                      val centi : float

                                                      centi = 1e-2

                                                      val milli : float

                                                      milli = 1e-3

                                                      val micro : float

                                                      micro = 1e-6

                                                      val nano : float

                                                      nano = 1e-9

                                                      val pico : float

                                                      pico = 1e-12

                                                      val femto : float

                                                      femto = 1e-15

                                                      val atto : float

                                                      atto = 1e-18

                                                      val zepto : float

                                                      zepto = 1e-21

                                                      val yocto : float

                                                      yocto = 1e-24

                                                      diff --git a/docs/owl-base/Owl_const/SI/index.html b/docs/owl-base/Owl_const/SI/index.html index 5ab492359..f0e41a472 100644 --- a/docs/owl-base/Owl_const/SI/index.html +++ b/docs/owl-base/Owl_const/SI/index.html @@ -1,2 +1,2 @@ -SI (owl-base.Owl_const.SI)

                                                      Module Owl_const.SI

                                                      val speed_of_light : float

                                                      speed_of_light = 2.99792458e8

                                                      val gravitational_constant : float

                                                      gravitational_constant = 6.673e-11

                                                      val plancks_constant_h : float

                                                      plancks_constant_h = 6.62606896e-34

                                                      val plancks_constant_hbar : float

                                                      plancks_constant_hbar = 1.05457162825e-34

                                                      val astronomical_unit : float

                                                      astronomical_unit = 1.49597870691e11

                                                      val light_year : float

                                                      light_year = 9.46053620707e15

                                                      val parsec : float

                                                      parsec = 3.08567758135e16

                                                      val grav_accel : float

                                                      grav_accel = 9.80665e0

                                                      val electron_volt : float

                                                      electron_volt = 1.602176487e-19

                                                      val mass_electron : float

                                                      mass_electron = 9.10938188e-31

                                                      val mass_muon : float

                                                      mass_muon = 1.88353109e-28

                                                      val mass_proton : float

                                                      mass_proton = 1.67262158e-27

                                                      val mass_neutron : float

                                                      mass_neutron = 1.67492716e-27

                                                      val rydberg : float

                                                      rydberg = 2.17987196968e-18

                                                      val boltzmann : float

                                                      boltzmann = 1.3806504e-23

                                                      val molar_gas : float

                                                      molar_gas = 8.314472e0

                                                      val standard_gas_volume : float

                                                      standard_gas_volume = 2.2710981e-2

                                                      val minute : float

                                                      minute = 6e1

                                                      val hour : float

                                                      hour = 3.6e3

                                                      val day : float

                                                      day = 8.64e4

                                                      val week : float

                                                      week = 6.048e5

                                                      val inch : float

                                                      inch = 2.54e-2

                                                      val foot : float

                                                      foot = 3.048e-1

                                                      val yard : float

                                                      yard = 9.144e-1

                                                      val mile : float

                                                      mile = 1.609344e3

                                                      val nautical_mile : float

                                                      nautical_mile = 1.852e3

                                                      val fathom : float

                                                      fathom = 1.8288e0

                                                      val mil : float

                                                      mil = 2.54e-5

                                                      val point : float

                                                      point = 3.52777777778e-4

                                                      val texpoint : float

                                                      texpoint = 3.51459803515e-4

                                                      val micron : float

                                                      micron = 1e-6

                                                      val angstrom : float

                                                      angstrom = 1e-10

                                                      val hectare : float

                                                      hectare = 1e4

                                                      val acre : float

                                                      acre = 4.04685642241e3

                                                      val barn : float

                                                      barn = 1e-28

                                                      val liter : float

                                                      liter = 1e-3

                                                      val us_gallon : float

                                                      us_gallon = 3.78541178402e-3

                                                      val quart : float

                                                      quart = 9.46352946004e-4

                                                      val pint : float

                                                      pint = 4.73176473002e-4

                                                      val cup : float

                                                      cup = 2.36588236501e-4

                                                      val fluid_ounce : float

                                                      fluid_ounce = 2.95735295626e-5

                                                      val tablespoon : float

                                                      tablespoon = 1.47867647813e-5

                                                      val teaspoon : float

                                                      teaspoon = 4.92892159375e-6

                                                      val canadian_gallon : float

                                                      canadian_gallon = 4.54609e-3

                                                      val uk_gallon : float

                                                      uk_gallon = 4.546092e-3

                                                      val miles_per_hour : float

                                                      miles_per_hour = 4.4704e-1

                                                      val kilometers_per_hour : float

                                                      kilometers_per_hour = 2.77777777778e-1

                                                      val knot : float

                                                      knot = 5.14444444444e-1

                                                      val pound_mass : float

                                                      pound_mass = 4.5359237e-1

                                                      val ounce_mass : float

                                                      ounce_mass = 2.8349523125e-2

                                                      val ton : float

                                                      ton = 9.0718474e2

                                                      val metric_ton : float

                                                      metric_ton = 1e3

                                                      val uk_ton : float

                                                      uk_ton = 1.0160469088e3

                                                      val troy_ounce : float

                                                      troy_ounce = 3.1103475e-2

                                                      val carat : float

                                                      carat = 2e-4

                                                      val unified_atomic_mass : float

                                                      unified_atomic_mass = 1.660538782e-27

                                                      val gram_force : float

                                                      gram_force = 9.80665e-3

                                                      val pound_force : float

                                                      pound_force = 4.44822161526e0

                                                      val kilopound_force : float

                                                      kilopound_force = 4.44822161526e3

                                                      val poundal : float

                                                      poundal = 1.38255e-1

                                                      val calorie : float

                                                      calorie = 4.1868e0

                                                      val btu : float

                                                      btu = 1.05505585262e3

                                                      val therm : float

                                                      therm = 1.05506e8

                                                      val horsepower : float

                                                      horsepower = 7.457e2

                                                      val bar : float

                                                      bar = 1e5

                                                      val std_atmosphere : float

                                                      std_atmosphere = 1.01325e5

                                                      val torr : float

                                                      torr = 1.33322368421e2

                                                      val meter_of_mercury : float

                                                      meter_of_mercury = 1.33322368421e5

                                                      val inch_of_mercury : float

                                                      inch_of_mercury = 3.38638815789e3

                                                      val inch_of_water : float

                                                      inch_of_water = 2.490889e2

                                                      val psi : float

                                                      psi = 6.89475729317e3

                                                      val poise : float

                                                      poise = 1e-1

                                                      val stokes : float

                                                      stokes = 1e-4

                                                      val stilb : float

                                                      stilb = 1e4

                                                      val lumen : float

                                                      lumen = 1e0

                                                      val lux : float

                                                      lux = 1e0

                                                      val phot : float

                                                      phot = 1e4

                                                      val footcandle : float

                                                      footcandle = 1.076e1

                                                      val lambert : float

                                                      lambert = 1e4

                                                      val footlambert : float

                                                      footlambert = 1.07639104e1

                                                      val curie : float

                                                      curie = 3.7e10

                                                      val roentgen : float

                                                      roentgen = 2.58e-4

                                                      val rad : float

                                                      rad = 1e-2

                                                      val solar_mass : float

                                                      solar_mass = 1.98892e30

                                                      val bohr_radius : float

                                                      bohr_radius = 5.291772083e-11

                                                      val newton : float

                                                      newton = 1e0

                                                      val dyne : float

                                                      dyne = 1e-5

                                                      val joule : float

                                                      joule = 1e0

                                                      val erg : float

                                                      erg = 1e-7

                                                      val stefan_boltzmann_constant : float

                                                      stefan_boltzmann_constant = 5.67040047374e-8

                                                      val thomson_cross_section : float

                                                      thomson_cross_section = 6.65245893699e-29

                                                      val bohr_magneton : float

                                                      bohr_magneton = 9.27400899e-24

                                                      val nuclear_magneton : float

                                                      nuclear_magneton = 5.05078317e-27

                                                      val electron_magnetic_moment : float

                                                      electron_magnetic_moment = 9.28476362e-24

                                                      val proton_magnetic_moment : float

                                                      proton_magnetic_moment = 1.410606633e-26

                                                      val faraday : float

                                                      faraday = 9.64853429775e4

                                                      val electron_charge : float

                                                      electron_charge = 1.602176487e-19

                                                      val vacuum_permittivity : float

                                                      vacuum_permittivity = 8.854187817e-12

                                                      val vacuum_permeability : float

                                                      vacuum_permeability = 1.25663706144e-6

                                                      val debye : float

                                                      debye = 3.33564095198e-30

                                                      val gauss : float

                                                      gauss = 1e-4

                                                      +SI (owl-base.Owl_const.SI)

                                                      Module Owl_const.SI

                                                      val speed_of_light : float

                                                      speed_of_light = 2.99792458e8

                                                      val gravitational_constant : float

                                                      gravitational_constant = 6.673e-11

                                                      val plancks_constant_h : float

                                                      plancks_constant_h = 6.62606896e-34

                                                      val plancks_constant_hbar : float

                                                      plancks_constant_hbar = 1.05457162825e-34

                                                      val astronomical_unit : float

                                                      astronomical_unit = 1.49597870691e11

                                                      val light_year : float

                                                      light_year = 9.46053620707e15

                                                      val parsec : float

                                                      parsec = 3.08567758135e16

                                                      val grav_accel : float

                                                      grav_accel = 9.80665e0

                                                      val electron_volt : float

                                                      electron_volt = 1.602176487e-19

                                                      val mass_electron : float

                                                      mass_electron = 9.10938188e-31

                                                      val mass_muon : float

                                                      mass_muon = 1.88353109e-28

                                                      val mass_proton : float

                                                      mass_proton = 1.67262158e-27

                                                      val mass_neutron : float

                                                      mass_neutron = 1.67492716e-27

                                                      val rydberg : float

                                                      rydberg = 2.17987196968e-18

                                                      val boltzmann : float

                                                      boltzmann = 1.3806504e-23

                                                      val molar_gas : float

                                                      molar_gas = 8.314472e0

                                                      val standard_gas_volume : float

                                                      standard_gas_volume = 2.2710981e-2

                                                      val minute : float

                                                      minute = 6e1

                                                      val hour : float

                                                      hour = 3.6e3

                                                      val day : float

                                                      day = 8.64e4

                                                      val week : float

                                                      week = 6.048e5

                                                      val inch : float

                                                      inch = 2.54e-2

                                                      val foot : float

                                                      foot = 3.048e-1

                                                      val yard : float

                                                      yard = 9.144e-1

                                                      val mile : float

                                                      mile = 1.609344e3

                                                      val nautical_mile : float

                                                      nautical_mile = 1.852e3

                                                      val fathom : float

                                                      fathom = 1.8288e0

                                                      val mil : float

                                                      mil = 2.54e-5

                                                      val point : float

                                                      point = 3.52777777778e-4

                                                      val texpoint : float

                                                      texpoint = 3.51459803515e-4

                                                      val micron : float

                                                      micron = 1e-6

                                                      val angstrom : float

                                                      angstrom = 1e-10

                                                      val hectare : float

                                                      hectare = 1e4

                                                      val acre : float

                                                      acre = 4.04685642241e3

                                                      val barn : float

                                                      barn = 1e-28

                                                      val liter : float

                                                      liter = 1e-3

                                                      val us_gallon : float

                                                      us_gallon = 3.78541178402e-3

                                                      val quart : float

                                                      quart = 9.46352946004e-4

                                                      val pint : float

                                                      pint = 4.73176473002e-4

                                                      val cup : float

                                                      cup = 2.36588236501e-4

                                                      val fluid_ounce : float

                                                      fluid_ounce = 2.95735295626e-5

                                                      val tablespoon : float

                                                      tablespoon = 1.47867647813e-5

                                                      val teaspoon : float

                                                      teaspoon = 4.92892159375e-6

                                                      val canadian_gallon : float

                                                      canadian_gallon = 4.54609e-3

                                                      val uk_gallon : float

                                                      uk_gallon = 4.546092e-3

                                                      val miles_per_hour : float

                                                      miles_per_hour = 4.4704e-1

                                                      val kilometers_per_hour : float

                                                      kilometers_per_hour = 2.77777777778e-1

                                                      val knot : float

                                                      knot = 5.14444444444e-1

                                                      val pound_mass : float

                                                      pound_mass = 4.5359237e-1

                                                      val ounce_mass : float

                                                      ounce_mass = 2.8349523125e-2

                                                      val ton : float

                                                      ton = 9.0718474e2

                                                      val metric_ton : float

                                                      metric_ton = 1e3

                                                      val uk_ton : float

                                                      uk_ton = 1.0160469088e3

                                                      val troy_ounce : float

                                                      troy_ounce = 3.1103475e-2

                                                      val carat : float

                                                      carat = 2e-4

                                                      val unified_atomic_mass : float

                                                      unified_atomic_mass = 1.660538782e-27

                                                      val gram_force : float

                                                      gram_force = 9.80665e-3

                                                      val pound_force : float

                                                      pound_force = 4.44822161526e0

                                                      val kilopound_force : float

                                                      kilopound_force = 4.44822161526e3

                                                      val poundal : float

                                                      poundal = 1.38255e-1

                                                      val calorie : float

                                                      calorie = 4.1868e0

                                                      val btu : float

                                                      btu = 1.05505585262e3

                                                      val therm : float

                                                      therm = 1.05506e8

                                                      val horsepower : float

                                                      horsepower = 7.457e2

                                                      val bar : float

                                                      bar = 1e5

                                                      val std_atmosphere : float

                                                      std_atmosphere = 1.01325e5

                                                      val torr : float

                                                      torr = 1.33322368421e2

                                                      val meter_of_mercury : float

                                                      meter_of_mercury = 1.33322368421e5

                                                      val inch_of_mercury : float

                                                      inch_of_mercury = 3.38638815789e3

                                                      val inch_of_water : float

                                                      inch_of_water = 2.490889e2

                                                      val psi : float

                                                      psi = 6.89475729317e3

                                                      val poise : float

                                                      poise = 1e-1

                                                      val stokes : float

                                                      stokes = 1e-4

                                                      val stilb : float

                                                      stilb = 1e4

                                                      val lumen : float

                                                      lumen = 1e0

                                                      val lux : float

                                                      lux = 1e0

                                                      val phot : float

                                                      phot = 1e4

                                                      val footcandle : float

                                                      footcandle = 1.076e1

                                                      val lambert : float

                                                      lambert = 1e4

                                                      val footlambert : float

                                                      footlambert = 1.07639104e1

                                                      val curie : float

                                                      curie = 3.7e10

                                                      val roentgen : float

                                                      roentgen = 2.58e-4

                                                      val rad : float

                                                      rad = 1e-2

                                                      val solar_mass : float

                                                      solar_mass = 1.98892e30

                                                      val bohr_radius : float

                                                      bohr_radius = 5.291772083e-11

                                                      val newton : float

                                                      newton = 1e0

                                                      val dyne : float

                                                      dyne = 1e-5

                                                      val joule : float

                                                      joule = 1e0

                                                      val erg : float

                                                      erg = 1e-7

                                                      val stefan_boltzmann_constant : float

                                                      stefan_boltzmann_constant = 5.67040047374e-8

                                                      val thomson_cross_section : float

                                                      thomson_cross_section = 6.65245893699e-29

                                                      val bohr_magneton : float

                                                      bohr_magneton = 9.27400899e-24

                                                      val nuclear_magneton : float

                                                      nuclear_magneton = 5.05078317e-27

                                                      val electron_magnetic_moment : float

                                                      electron_magnetic_moment = 9.28476362e-24

                                                      val proton_magnetic_moment : float

                                                      proton_magnetic_moment = 1.410606633e-26

                                                      val faraday : float

                                                      faraday = 9.64853429775e4

                                                      val electron_charge : float

                                                      electron_charge = 1.602176487e-19

                                                      val vacuum_permittivity : float

                                                      vacuum_permittivity = 8.854187817e-12

                                                      val vacuum_permeability : float

                                                      vacuum_permeability = 1.25663706144e-6

                                                      val debye : float

                                                      debye = 3.33564095198e-30

                                                      val gauss : float

                                                      gauss = 1e-4

                                                      diff --git a/docs/owl-base/Owl_const/index.html b/docs/owl-base/Owl_const/index.html index b45222f6a..6ddfe6fdb 100644 --- a/docs/owl-base/Owl_const/index.html +++ b/docs/owl-base/Owl_const/index.html @@ -1,2 +1,2 @@ -Owl_const (owl-base.Owl_const)

                                                      Module Owl_const

                                                      Metric system: CGS, MKS, SI, and physical constants.

                                                      Values of physical constants CGS < MKS < SI. Read wikipedia on CGS and SI system for more details.

                                                      International System of Units (French: Système international d'unités, SI), historically also called the MKSA system of units for metre–kilogram–second–ampere.

                                                      The SI system of units extends the MKS system and has 7 base units, by expressing any measurement of physical quantities using fundamental units of Length, Mass, Time, Electric Current, Thermodynamic Temperature, Amount of substance and Luminous Intensity, which are Metre, Kilogram, Second, Ampere, Kelvin, Mole and Candela respectively.

                                                      http://www.npl.co.uk/upload/pdf/units-of-measurement-poster.pdf

                                                      Maths constants
                                                      val e : float

                                                      e = 2.718281828459045235360287471352662498

                                                      val euler : float

                                                      euler = 0.577215664901532860606512090082402431

                                                      val log2e : float

                                                      log2e = 1.442695040888963407359924681001892137

                                                      val log10e : float

                                                      log10e = 0.434294481903251827651128918916605082

                                                      val loge2 : float

                                                      loge2 = 0.693147180559945309417232121458176568

                                                      val loge10 : float

                                                      loge10 = 2.302585092994045684017991454684364208

                                                      val logepi : float

                                                      logepi = 1.144729885849400174143427351353058711

                                                      val sqrt1_2 : float

                                                      sqrt1_2 = 0.707106781186547524400844362104849039

                                                      val sqrt2 : float

                                                      sqrt2 = 1.414213562373095048801688724209698079

                                                      val sqrt3 : float

                                                      sqrt3 = 1.732050807568877293527446341505872366

                                                      val sqrtpi : float

                                                      sqrtpi = 1.772453850905516027298167483341145182

                                                      val pi : float

                                                      pi = 3.141592653589793238462643383279502884

                                                      val pi2 : float

                                                      pi2 = 6.283185307179586476925286766559005768

                                                      val pi4 : float

                                                      pi4 = 12.56637061435917295385057353311801153

                                                      val pi_2 : float

                                                      pi_2 = 1.570796326794896619231321691639751442

                                                      val pi_4 : float

                                                      pi_4 = 0.785398163397448309615660845819875721

                                                      val eps : float

                                                      eps = 1e-15

                                                      Constants depending on Bigarray kind
                                                      val zero : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      zero kind returns value zero of the given number type kind.

                                                      val one : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      one kind returns value one of the given number type kind.

                                                      val neg_one : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      neg_one kind returns negative one of the given number type kind.

                                                      val pos_inf : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      pos_inf kind returns positive infinity of the given number type kind.

                                                      val neg_inf : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      neg_inf kind returns negative infinity of the given number type kind.

                                                      val min_float32 : float

                                                      Miminum value of single precision float number, i.e. ~-.340282346638528859811704183484516925440.0

                                                      val max_float32 : float

                                                      Maximum value of single precision float number, i.e. 340282346638528859811704183484516925440.0

                                                      val min_float64 : float

                                                      Miminum value of double precision float number.

                                                      val max_float64 : float

                                                      Maximum value of double precision float number.

                                                      Unit prefixes
                                                      module Prefix : sig ... end
                                                      SI: International System of Units
                                                      module SI : sig ... end
                                                      MKS: MKS system of units
                                                      module MKS : sig ... end
                                                      CGS: Centimetre–gram–second system of units
                                                      module CGS : sig ... end
                                                      CGSM: Unit Systems in Electromagnetism
                                                      module CGSM : sig ... end
                                                      +Owl_const (owl-base.Owl_const)

                                                      Module Owl_const

                                                      Metric system: CGS, MKS, SI, and physical constants.

                                                      Values of physical constants CGS < MKS < SI. Read wikipedia on CGS and SI system for more details.

                                                      International System of Units (French: Système international d'unités, SI), historically also called the MKSA system of units for metre–kilogram–second–ampere.

                                                      The SI system of units extends the MKS system and has 7 base units, by expressing any measurement of physical quantities using fundamental units of Length, Mass, Time, Electric Current, Thermodynamic Temperature, Amount of substance and Luminous Intensity, which are Metre, Kilogram, Second, Ampere, Kelvin, Mole and Candela respectively.

                                                      http://www.npl.co.uk/upload/pdf/units-of-measurement-poster.pdf

                                                      Maths constants
                                                      val e : float

                                                      e = 2.718281828459045235360287471352662498

                                                      val euler : float

                                                      euler = 0.577215664901532860606512090082402431

                                                      val log2e : float

                                                      log2e = 1.442695040888963407359924681001892137

                                                      val log10e : float

                                                      log10e = 0.434294481903251827651128918916605082

                                                      val loge2 : float

                                                      loge2 = 0.693147180559945309417232121458176568

                                                      val loge10 : float

                                                      loge10 = 2.302585092994045684017991454684364208

                                                      val logepi : float

                                                      logepi = 1.144729885849400174143427351353058711

                                                      val sqrt1_2 : float

                                                      sqrt1_2 = 0.707106781186547524400844362104849039

                                                      val sqrt2 : float

                                                      sqrt2 = 1.414213562373095048801688724209698079

                                                      val sqrt3 : float

                                                      sqrt3 = 1.732050807568877293527446341505872366

                                                      val sqrtpi : float

                                                      sqrtpi = 1.772453850905516027298167483341145182

                                                      val pi : float

                                                      pi = 3.141592653589793238462643383279502884

                                                      val pi2 : float

                                                      pi2 = 6.283185307179586476925286766559005768

                                                      val pi4 : float

                                                      pi4 = 12.56637061435917295385057353311801153

                                                      val pi_2 : float

                                                      pi_2 = 1.570796326794896619231321691639751442

                                                      val pi_4 : float

                                                      pi_4 = 0.785398163397448309615660845819875721

                                                      val eps : float

                                                      eps = 1e-15

                                                      Constants depending on Bigarray kind
                                                      val zero : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      zero kind returns value zero of the given number type kind.

                                                      val one : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      one kind returns value one of the given number type kind.

                                                      val neg_one : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      neg_one kind returns negative one of the given number type kind.

                                                      val pos_inf : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      pos_inf kind returns positive infinity of the given number type kind.

                                                      val neg_inf : ('a, 'b) Stdlib.Bigarray.kind -> 'a

                                                      neg_inf kind returns negative infinity of the given number type kind.

                                                      val min_float32 : float

                                                      Miminum value of single precision float number, i.e. ~-.340282346638528859811704183484516925440.0

                                                      val max_float32 : float

                                                      Maximum value of single precision float number, i.e. 340282346638528859811704183484516925440.0

                                                      val min_float64 : float

                                                      Miminum value of double precision float number.

                                                      val max_float64 : float

                                                      Maximum value of double precision float number.

                                                      Unit prefixes
                                                      module Prefix : sig ... end
                                                      SI: International System of Units
                                                      module SI : sig ... end
                                                      MKS: MKS system of units
                                                      module MKS : sig ... end
                                                      CGS: Centimetre–gram–second system of units
                                                      module CGS : sig ... end
                                                      CGSM: Unit Systems in Electromagnetism
                                                      module CGSM : sig ... end
                                                      diff --git a/docs/owl-base/Owl_countmin_sketch/Make/argument-1-T/index.html b/docs/owl-base/Owl_countmin_sketch/Make/argument-1-T/index.html index d470e2a82..307a9bb09 100644 --- a/docs/owl-base/Owl_countmin_sketch/Make/argument-1-T/index.html +++ b/docs/owl-base/Owl_countmin_sketch/Make/argument-1-T/index.html @@ -1,2 +1,2 @@ -T (owl-base.Owl_countmin_sketch.Make.T)

                                                      Parameter Make.T

                                                      Type definition
                                                      type t

                                                      The type of count-min tables

                                                      Core functions
                                                      val init : int -> int -> t

                                                      init l w generates a table with length l and width w, all counters initialized to 0.

                                                      val incr : int -> int -> t -> unit

                                                      incr i j t increments the counter at length index i and width index j in table t.

                                                      val get : int -> int -> t -> int

                                                      get i j t gets the value of the counter at length index i and width index j in table t.

                                                      val clone : t -> t

                                                      clone t returns a new table with the same contents as t.

                                                      val merge : t -> t -> t

                                                      merge t1 t2 merges tables t1 and t2 element-wise. If t1 and t2 have the same dimensions, returns a new table whose elements are the sums of corresponding elements from t1 and t2. If dimensions do not match, raises INVALID_ARGUMENT.

                                                      +T (owl-base.Owl_countmin_sketch.Make.T)

                                                      Parameter Make.T

                                                      Type definition
                                                      type t

                                                      The type of count-min tables

                                                      Core functions
                                                      val init : int -> int -> t

                                                      init l w generates a table with length l and width w, all counters initialized to 0.

                                                      val incr : int -> int -> t -> unit

                                                      incr i j t increments the counter at length index i and width index j in table t.

                                                      val get : int -> int -> t -> int

                                                      get i j t gets the value of the counter at length index i and width index j in table t.

                                                      val clone : t -> t

                                                      clone t returns a new table with the same contents as t.

                                                      val merge : t -> t -> t

                                                      merge t1 t2 merges tables t1 and t2 element-wise. If t1 and t2 have the same dimensions, returns a new table whose elements are the sums of corresponding elements from t1 and t2. If dimensions do not match, raises INVALID_ARGUMENT.

                                                      diff --git a/docs/owl-base/Owl_countmin_sketch/Make/index.html b/docs/owl-base/Owl_countmin_sketch/Make/index.html index 53ca53348..9a03d0ff4 100644 --- a/docs/owl-base/Owl_countmin_sketch/Make/index.html +++ b/docs/owl-base/Owl_countmin_sketch/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_countmin_sketch.Make)

                                                      Module Owl_countmin_sketch.Make

                                                      Parameters

                                                      Signature

                                                      Type definition
                                                      type 'a sketch

                                                      The type of Count-Min sketches

                                                      Core functions
                                                      val init : epsilon:float -> delta:float -> 'a sketch

                                                      init epsilon delta initializes a sketch with approximation ratio (1 + epsilon) and failure probability delta.

                                                      val incr : 'a sketch -> 'a -> unit

                                                      incr s x increments the frequency count of x in sketch s in-place.

                                                      val count : 'a sketch -> 'a -> int

                                                      count s x returns the estimated frequency of element x in s.

                                                      val init_from : 'a sketch -> 'a sketch

                                                      init_from s initializes a new empty sketch with the same parameters as s, which can later be merged with s.

                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch

                                                      merge s1 s2 returns a new sketch whose counts are the sum of those in s1 and s2. Raises INVALID_ARGUMENT if the parameters of s1 and s2 do not match.

                                                      +Make (owl-base.Owl_countmin_sketch.Make)

                                                      Module Owl_countmin_sketch.Make

                                                      Parameters

                                                      Signature

                                                      Type definition
                                                      type 'a sketch

                                                      The type of Count-Min sketches

                                                      Core functions
                                                      val init : epsilon:float -> delta:float -> 'a sketch

                                                      init epsilon delta initializes a sketch with approximation ratio (1 + epsilon) and failure probability delta.

                                                      val incr : 'a sketch -> 'a -> unit

                                                      incr s x increments the frequency count of x in sketch s in-place.

                                                      val count : 'a sketch -> 'a -> int

                                                      count s x returns the estimated frequency of element x in s.

                                                      val init_from : 'a sketch -> 'a sketch

                                                      init_from s initializes a new empty sketch with the same parameters as s, which can later be merged with s.

                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch

                                                      merge s1 s2 returns a new sketch whose counts are the sum of those in s1 and s2. Raises INVALID_ARGUMENT if the parameters of s1 and s2 do not match.

                                                      diff --git a/docs/owl-base/Owl_countmin_sketch/Native/index.html b/docs/owl-base/Owl_countmin_sketch/Native/index.html index cbd74b5c6..1666d6cad 100644 --- a/docs/owl-base/Owl_countmin_sketch/Native/index.html +++ b/docs/owl-base/Owl_countmin_sketch/Native/index.html @@ -1,2 +1,2 @@ -Native (owl-base.Owl_countmin_sketch.Native)

                                                      Module Owl_countmin_sketch.Native

                                                      val init : epsilon:float -> delta:float -> 'a sketch
                                                      val incr : 'a sketch -> 'a -> unit
                                                      val count : 'a sketch -> 'a -> int
                                                      val init_from : 'a sketch -> 'a sketch
                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch
                                                      +Native (owl-base.Owl_countmin_sketch.Native)

                                                      Module Owl_countmin_sketch.Native

                                                      val init : epsilon:float -> delta:float -> 'a sketch
                                                      val incr : 'a sketch -> 'a -> unit
                                                      val count : 'a sketch -> 'a -> int
                                                      val init_from : 'a sketch -> 'a sketch
                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch
                                                      diff --git a/docs/owl-base/Owl_countmin_sketch/Owl/index.html b/docs/owl-base/Owl_countmin_sketch/Owl/index.html index 25f51ac5e..d30e00a24 100644 --- a/docs/owl-base/Owl_countmin_sketch/Owl/index.html +++ b/docs/owl-base/Owl_countmin_sketch/Owl/index.html @@ -1,2 +1,2 @@ -Owl (owl-base.Owl_countmin_sketch.Owl)

                                                      Module Owl_countmin_sketch.Owl

                                                      val init : epsilon:float -> delta:float -> 'a sketch
                                                      val incr : 'a sketch -> 'a -> unit
                                                      val count : 'a sketch -> 'a -> int
                                                      val init_from : 'a sketch -> 'a sketch
                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch
                                                      +Owl (owl-base.Owl_countmin_sketch.Owl)

                                                      Module Owl_countmin_sketch.Owl

                                                      val init : epsilon:float -> delta:float -> 'a sketch
                                                      val incr : 'a sketch -> 'a -> unit
                                                      val count : 'a sketch -> 'a -> int
                                                      val init_from : 'a sketch -> 'a sketch
                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch
                                                      diff --git a/docs/owl-base/Owl_countmin_sketch/index.html b/docs/owl-base/Owl_countmin_sketch/index.html index 7bebe97b3..912708b03 100644 --- a/docs/owl-base/Owl_countmin_sketch/index.html +++ b/docs/owl-base/Owl_countmin_sketch/index.html @@ -1,2 +1,2 @@ -Owl_countmin_sketch (owl-base.Owl_countmin_sketch)

                                                      Module Owl_countmin_sketch

                                                      module Native : sig ... end
                                                      module Owl : sig ... end
                                                      +Owl_countmin_sketch (owl-base.Owl_countmin_sketch)

                                                      Module Owl_countmin_sketch

                                                      module Native : sig ... end
                                                      module Owl : sig ... end
                                                      diff --git a/docs/owl-base/Owl_countmin_sketch_sig/index.html b/docs/owl-base/Owl_countmin_sketch_sig/index.html index 4fa5a1308..1f9902921 100644 --- a/docs/owl-base/Owl_countmin_sketch_sig/index.html +++ b/docs/owl-base/Owl_countmin_sketch_sig/index.html @@ -1,2 +1,2 @@ -Owl_countmin_sketch_sig (owl-base.Owl_countmin_sketch_sig)

                                                      Module Owl_countmin_sketch_sig

                                                      module type Sig = sig ... end
                                                      +Owl_countmin_sketch_sig (owl-base.Owl_countmin_sketch_sig)

                                                      Module Owl_countmin_sketch_sig

                                                      module type Sig = sig ... end
                                                      diff --git a/docs/owl-base/Owl_countmin_sketch_sig/module-type-Sig/index.html b/docs/owl-base/Owl_countmin_sketch_sig/module-type-Sig/index.html index 003a6f984..59f074238 100644 --- a/docs/owl-base/Owl_countmin_sketch_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_countmin_sketch_sig/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_countmin_sketch_sig.Sig)

                                                      Module type Owl_countmin_sketch_sig.Sig

                                                      Type definition
                                                      type 'a sketch

                                                      The type of Count-Min sketches

                                                      Core functions
                                                      val init : epsilon:float -> delta:float -> 'a sketch

                                                      init epsilon delta initializes a sketch with approximation ratio (1 + epsilon) and failure probability delta.

                                                      val incr : 'a sketch -> 'a -> unit

                                                      incr s x increments the frequency count of x in sketch s in-place.

                                                      val count : 'a sketch -> 'a -> int

                                                      count s x returns the estimated frequency of element x in s.

                                                      val init_from : 'a sketch -> 'a sketch

                                                      init_from s initializes a new empty sketch with the same parameters as s, which can later be merged with s.

                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch

                                                      merge s1 s2 returns a new sketch whose counts are the sum of those in s1 and s2. Raises INVALID_ARGUMENT if the parameters of s1 and s2 do not match.

                                                      +Sig (owl-base.Owl_countmin_sketch_sig.Sig)

                                                      Module type Owl_countmin_sketch_sig.Sig

                                                      Type definition
                                                      type 'a sketch

                                                      The type of Count-Min sketches

                                                      Core functions
                                                      val init : epsilon:float -> delta:float -> 'a sketch

                                                      init epsilon delta initializes a sketch with approximation ratio (1 + epsilon) and failure probability delta.

                                                      val incr : 'a sketch -> 'a -> unit

                                                      incr s x increments the frequency count of x in sketch s in-place.

                                                      val count : 'a sketch -> 'a -> int

                                                      count s x returns the estimated frequency of element x in s.

                                                      val init_from : 'a sketch -> 'a sketch

                                                      init_from s initializes a new empty sketch with the same parameters as s, which can later be merged with s.

                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch

                                                      merge s1 s2 returns a new sketch whose counts are the sum of those in s1 and s2. Raises INVALID_ARGUMENT if the parameters of s1 and s2 do not match.

                                                      diff --git a/docs/owl-base/Owl_countmin_table/Native/index.html b/docs/owl-base/Owl_countmin_table/Native/index.html index 4ea7ba795..1261dc371 100644 --- a/docs/owl-base/Owl_countmin_table/Native/index.html +++ b/docs/owl-base/Owl_countmin_table/Native/index.html @@ -1,2 +1,2 @@ -Native (owl-base.Owl_countmin_table.Native)

                                                      Module Owl_countmin_table.Native

                                                      Type definition
                                                      type t

                                                      The type of count-min tables

                                                      Core functions
                                                      val init : int -> int -> t

                                                      init l w generates a table with length l and width w, all counters initialized to 0.

                                                      val incr : int -> int -> t -> unit

                                                      incr i j t increments the counter at length index i and width index j in table t.

                                                      val get : int -> int -> t -> int

                                                      get i j t gets the value of the counter at length index i and width index j in table t.

                                                      val clone : t -> t

                                                      clone t returns a new table with the same contents as t.

                                                      val merge : t -> t -> t

                                                      merge t1 t2 merges tables t1 and t2 element-wise. If t1 and t2 have the same dimensions, returns a new table whose elements are the sums of corresponding elements from t1 and t2. If dimensions do not match, raises INVALID_ARGUMENT.

                                                      +Native (owl-base.Owl_countmin_table.Native)

                                                      Module Owl_countmin_table.Native

                                                      Type definition
                                                      type t

                                                      The type of count-min tables

                                                      Core functions
                                                      val init : int -> int -> t

                                                      init l w generates a table with length l and width w, all counters initialized to 0.

                                                      val incr : int -> int -> t -> unit

                                                      incr i j t increments the counter at length index i and width index j in table t.

                                                      val get : int -> int -> t -> int

                                                      get i j t gets the value of the counter at length index i and width index j in table t.

                                                      val clone : t -> t

                                                      clone t returns a new table with the same contents as t.

                                                      val merge : t -> t -> t

                                                      merge t1 t2 merges tables t1 and t2 element-wise. If t1 and t2 have the same dimensions, returns a new table whose elements are the sums of corresponding elements from t1 and t2. If dimensions do not match, raises INVALID_ARGUMENT.

                                                      diff --git a/docs/owl-base/Owl_countmin_table/Owl/index.html b/docs/owl-base/Owl_countmin_table/Owl/index.html index 74ac3f0ca..4e7d16335 100644 --- a/docs/owl-base/Owl_countmin_table/Owl/index.html +++ b/docs/owl-base/Owl_countmin_table/Owl/index.html @@ -1,2 +1,2 @@ -Owl (owl-base.Owl_countmin_table.Owl)

                                                      Module Owl_countmin_table.Owl

                                                      Type definition
                                                      type t

                                                      The type of count-min tables

                                                      Core functions
                                                      val init : int -> int -> t

                                                      init l w generates a table with length l and width w, all counters initialized to 0.

                                                      val incr : int -> int -> t -> unit

                                                      incr i j t increments the counter at length index i and width index j in table t.

                                                      val get : int -> int -> t -> int

                                                      get i j t gets the value of the counter at length index i and width index j in table t.

                                                      val clone : t -> t

                                                      clone t returns a new table with the same contents as t.

                                                      val merge : t -> t -> t

                                                      merge t1 t2 merges tables t1 and t2 element-wise. If t1 and t2 have the same dimensions, returns a new table whose elements are the sums of corresponding elements from t1 and t2. If dimensions do not match, raises INVALID_ARGUMENT.

                                                      +Owl (owl-base.Owl_countmin_table.Owl)

                                                      Module Owl_countmin_table.Owl

                                                      Type definition
                                                      type t

                                                      The type of count-min tables

                                                      Core functions
                                                      val init : int -> int -> t

                                                      init l w generates a table with length l and width w, all counters initialized to 0.

                                                      val incr : int -> int -> t -> unit

                                                      incr i j t increments the counter at length index i and width index j in table t.

                                                      val get : int -> int -> t -> int

                                                      get i j t gets the value of the counter at length index i and width index j in table t.

                                                      val clone : t -> t

                                                      clone t returns a new table with the same contents as t.

                                                      val merge : t -> t -> t

                                                      merge t1 t2 merges tables t1 and t2 element-wise. If t1 and t2 have the same dimensions, returns a new table whose elements are the sums of corresponding elements from t1 and t2. If dimensions do not match, raises INVALID_ARGUMENT.

                                                      diff --git a/docs/owl-base/Owl_countmin_table/index.html b/docs/owl-base/Owl_countmin_table/index.html index 1379f7b52..7346f5d28 100644 --- a/docs/owl-base/Owl_countmin_table/index.html +++ b/docs/owl-base/Owl_countmin_table/index.html @@ -1,2 +1,2 @@ -Owl_countmin_table (owl-base.Owl_countmin_table)

                                                      Module Owl_countmin_table

                                                      module type Sig = sig ... end
                                                      module Native : Sig
                                                      module Owl : Sig
                                                      +Owl_countmin_table (owl-base.Owl_countmin_table)

                                                      Module Owl_countmin_table

                                                      module type Sig = sig ... end
                                                      module Native : Sig
                                                      module Owl : Sig
                                                      diff --git a/docs/owl-base/Owl_countmin_table/module-type-Sig/index.html b/docs/owl-base/Owl_countmin_table/module-type-Sig/index.html index 6beb0ab72..52e8d854f 100644 --- a/docs/owl-base/Owl_countmin_table/module-type-Sig/index.html +++ b/docs/owl-base/Owl_countmin_table/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_countmin_table.Sig)

                                                      Module type Owl_countmin_table.Sig

                                                      Type definition
                                                      type t

                                                      The type of count-min tables

                                                      Core functions
                                                      val init : int -> int -> t

                                                      init l w generates a table with length l and width w, all counters initialized to 0.

                                                      val incr : int -> int -> t -> unit

                                                      incr i j t increments the counter at length index i and width index j in table t.

                                                      val get : int -> int -> t -> int

                                                      get i j t gets the value of the counter at length index i and width index j in table t.

                                                      val clone : t -> t

                                                      clone t returns a new table with the same contents as t.

                                                      val merge : t -> t -> t

                                                      merge t1 t2 merges tables t1 and t2 element-wise. If t1 and t2 have the same dimensions, returns a new table whose elements are the sums of corresponding elements from t1 and t2. If dimensions do not match, raises INVALID_ARGUMENT.

                                                      +Sig (owl-base.Owl_countmin_table.Sig)

                                                      Module type Owl_countmin_table.Sig

                                                      Type definition
                                                      type t

                                                      The type of count-min tables

                                                      Core functions
                                                      val init : int -> int -> t

                                                      init l w generates a table with length l and width w, all counters initialized to 0.

                                                      val incr : int -> int -> t -> unit

                                                      incr i j t increments the counter at length index i and width index j in table t.

                                                      val get : int -> int -> t -> int

                                                      get i j t gets the value of the counter at length index i and width index j in table t.

                                                      val clone : t -> t

                                                      clone t returns a new table with the same contents as t.

                                                      val merge : t -> t -> t

                                                      merge t1 t2 merges tables t1 and t2 element-wise. If t1 and t2 have the same dimensions, returns a new table whose elements are the sums of corresponding elements from t1 and t2. If dimensions do not match, raises INVALID_ARGUMENT.

                                                      diff --git a/docs/owl-base/Owl_dataframe/index.html b/docs/owl-base/Owl_dataframe/index.html index 6b2e23386..9a6b675cc 100644 --- a/docs/owl-base/Owl_dataframe/index.html +++ b/docs/owl-base/Owl_dataframe/index.html @@ -1,5 +1,5 @@ -Owl_dataframe (owl-base.Owl_dataframe)

                                                      Module Owl_dataframe

                                                      Type definition
                                                      type t

                                                      Abstract dataframe type.

                                                      type series =
                                                      1. | Bool_Series of bool array
                                                      2. | Int_Series of int array
                                                      3. | Float_Series of float array
                                                      4. | String_Series of string array
                                                      5. | Any_Series
                                                        (*

                                                        Abstract series type.

                                                        *)
                                                      type elt =
                                                      1. | Bool of bool
                                                      2. | Int of int
                                                      3. | Float of float
                                                      4. | String of string
                                                      5. | Any
                                                        (*

                                                        Type of the elements in a series.

                                                        *)
                                                      Packing & unpacking element
                                                      val pack_bool : bool -> elt

                                                      Pack the boolean value to elt type.

                                                      val pack_int : int -> elt

                                                      Pack the int value to elt type.

                                                      val pack_float : float -> elt

                                                      Pack the float value to elt type.

                                                      val pack_string : string -> elt

                                                      Pack the string value to elt type.

                                                      val unpack_bool : elt -> bool

                                                      Unpack elt type to boolean value.

                                                      val unpack_int : elt -> int

                                                      Unpack elt type to int value.

                                                      val unpack_float : elt -> float

                                                      Unpack elt type to float value.

                                                      val unpack_string : elt -> string

                                                      Unpack elt type to string value.

                                                      Packing & unpacking series
                                                      val pack_bool_series : bool array -> series

                                                      Pack boolean array to series type.

                                                      val pack_int_series : int array -> series

                                                      Pack int array to series type.

                                                      val pack_float_series : float array -> series

                                                      Pack float array to series type.

                                                      val pack_string_series : string array -> series

                                                      Pack string array to series type.

                                                      val unpack_bool_series : series -> bool array

                                                      Unpack series type to boolean array.

                                                      val unpack_int_series : series -> int array

                                                      Unpack series type to int array.

                                                      val unpack_float_series : series -> float array

                                                      Unpack series type to float array.

                                                      val unpack_string_series : series -> string array

                                                      Unpack series type to string array.

                                                      Obtain properties
                                                      val row_num : t -> int

                                                      row_num x returns the number of rows in x.

                                                      val col_num : t -> int

                                                      col_num x returns the number of columns in x.

                                                      val shape : t -> int * int

                                                      shape x returns the shape of x, i.e. (row numnber, column number).

                                                      val numel : t -> int

                                                      numel x returns the number of elements in x.

                                                      val types : t -> string array

                                                      types x returns the string representation of column types.

                                                      val get_heads : t -> string array

                                                      get_heads x returns the column names of x.

                                                      val set_heads : t -> string array -> unit

                                                      set_heads x head_names sets head_names as the column names of x.

                                                      val id_to_head : t -> int -> string

                                                      id_to_head head_name converts head name to its corresponding column index.

                                                      val head_to_id : t -> string -> int

                                                      head_to_id i converts column index i to its corresponding head name.

                                                      Basic get and set functions
                                                      val get : t -> int -> int -> elt

                                                      get x i j returns the element at (i,j).

                                                      val set : t -> int -> int -> elt -> unit

                                                      set x i j v sets the value of element at (i,j) to v.

                                                      val get_by_name : t -> int -> string -> elt

                                                      get_by_name x i head_name is similar to get but uses column name.

                                                      val set_by_name : t -> int -> string -> elt -> unit

                                                      set_by_name x i head_name is similar to set but uses column name.

                                                      val get_row : t -> int -> elt array

                                                      get_row x i returns the ith row in x.

                                                      val get_col : t -> int -> series

                                                      get_col x i returns the ith column in x.

                                                      val get_rows : t -> int array -> elt array array

                                                      get_rows x a returns the rows of x specified in a.

                                                      val get_cols : t -> int array -> series array

                                                      get_cols x a returns the columns of x specified in a.

                                                      val get_col_by_name : t -> string -> series

                                                      get_col_by_name is similar to get_col but uses column name.

                                                      val get_cols_by_name : t -> string array -> series array

                                                      get_cols_by_name is similar to get_cols but uses column names.

                                                      val get_slice : int list list -> t -> t

                                                      get_slice s x returns a slice of x defined by s. For more details, please refer to :doc:`owl_dense_ndarray_generic`.

                                                      val get_slice_by_name : (int list * string list) -> t -> t

                                                      get_slice_by_name is similar to get_slice but uses column name.

                                                      val head : int -> t -> t

                                                      head n x returns top n rows of x.

                                                      val tail : int -> t -> t

                                                      tail n x returns bottom n rows of x.

                                                      Core operations
                                                      val make : ?data:series array -> string array -> t

                                                      make ~data head_names creates a dataframe with an array of series data and corresponding column names. If data is not passed in, the function will return an empty dataframe.

                                                      val copy : t -> t

                                                      copy x returns a copy of dataframe x.

                                                      val copy_struct : t -> t

                                                      copy_struct x only copies the structure of x with empty series.

                                                      val reset : t -> unit

                                                      reset x resets the dataframe x by setting all the time series to empty.

                                                      val unique : t -> string -> series

                                                      unique x removes the duplicates from the dataset and only returns the unique ones.

                                                      val sort : ?inc:bool -> t -> string -> t

                                                      sort ~inc x head sorts the entries in the dataframe x according to the specified column by head name head. By default, inc equals true, indicating increasing order.

                                                      val min_i : t -> string -> int

                                                      min_i x head returns the row index of the minimum value in the column specified by the head name.

                                                      val max_i : t -> string -> int

                                                      max_i x head returns the row index of the maximum value in the column specified by the head name.

                                                      val append_row : t -> elt array -> unit

                                                      append_row x row appends a row to the dataframe x.

                                                      val append_col : t -> series -> string -> unit

                                                      append_col x col appends a column to the dataframe x.

                                                      val insert_row : t -> int -> elt array -> unit

                                                      insert_row x i row inserts one row with at position i into dataframe x.

                                                      val insert_col : t -> int -> string -> series -> unit

                                                      insert_col x j col_head s inserts series s with column head col_head at position j into dataframe x.

                                                      val remove_row : t -> int -> unit

                                                      remove_row x i removes the ith row of x. Negative index is accepted.

                                                      val remove_col : t -> int -> unit

                                                      remove_col x i removes the ith column of x. Negative index is accepted.

                                                      val concat_horizontal : t -> t -> t

                                                      concat_horizontal x y merges two dataframes x and y. Note that x and y must have the same number of rows, and each column name should be unique.

                                                      val concat_vertical : t -> t -> t

                                                      concat_vertical x y concatenates two dataframes by appending y to x. The two dataframes x and y must have the same number of columns and the same column names.

                                                      Iteration functions
                                                      val iteri_row : (int -> elt array -> unit) -> t -> unit

                                                      iteri_row f x iterates the rows of x and applies f.

                                                      val iter_row : (elt array -> unit) -> t -> unit

                                                      iter_row is similar to iteri_row without passing in row indices.

                                                      val mapi_row : (int -> elt array -> elt array) -> t -> t

                                                      mapi_row f x transforms current dataframe x to a new dataframe by applying function f. Note that the returned value of f must be consistent with x w.r.t to its length and type, otherwise runtime error will occur.

                                                      val map_row : (elt array -> elt array) -> t -> t

                                                      map_row is similar to mapi_row but without passing in row indices.

                                                      val filteri_row : (int -> elt array -> bool) -> t -> t

                                                      filteri_row creates a new dataframe from x by filtering out those rows which satisfy the condition f.

                                                      val filter_row : (elt array -> bool) -> t -> t

                                                      filter_row is similar to filteri_row without passing in row indices.

                                                      val filter_mapi_row : (int -> elt array -> elt array option) -> t -> t

                                                      filter_map_row f x creates a new dataframe from x by applying f to each row. If f returns None then the row is excluded in the returned dataframe; if f returns Some row then the row is included.

                                                      val filter_map_row : (elt array -> elt array option) -> t -> t

                                                      filter_map_row is similar to filter_mapi_row without passing in row indices.

                                                      Extended indexing operators
                                                      val (.%()) : t -> (int * string) -> elt

                                                      Extended indexing operator associated with get_by_name function.

                                                      val (.%()<-) : t -> (int * string) -> elt -> unit

                                                      Extended indexing operator associated with set_by_name function.

                                                      val (.?()) : t -> (elt array -> bool) -> t

                                                      Extended indexing operator associated with filter_row function.

                                                      val (.?()<-) : t -> (elt array -> bool) -> (elt array -> elt array) -> t

                                                      Extended indexing operator associated with filter_map_row function. Given a dataframe x, f is used for filtering and g is used for transforming. In other words, x.?(f) <- g means that if f row is true then g row is included in the returned dataframe.

                                                      val (.$()) : t -> (int list * string list) -> t

                                                      Extended indexing operator associated with get_slice_by_name function.

                                                      IO & helper functions
                                                      val of_csv : +Owl_dataframe (owl-base.Owl_dataframe)

                                                      Module Owl_dataframe

                                                      Type definition
                                                      type t

                                                      Abstract dataframe type.

                                                      type series =
                                                      1. | Bool_Series of bool array
                                                      2. | Int_Series of int array
                                                      3. | Float_Series of float array
                                                      4. | String_Series of string array
                                                      5. | Any_Series
                                                        (*

                                                        Abstract series type.

                                                        *)
                                                      type elt =
                                                      1. | Bool of bool
                                                      2. | Int of int
                                                      3. | Float of float
                                                      4. | String of string
                                                      5. | Any
                                                        (*

                                                        Type of the elements in a series.

                                                        *)
                                                      Packing & unpacking element
                                                      val pack_bool : bool -> elt

                                                      Pack the boolean value to elt type.

                                                      val pack_int : int -> elt

                                                      Pack the int value to elt type.

                                                      val pack_float : float -> elt

                                                      Pack the float value to elt type.

                                                      val pack_string : string -> elt

                                                      Pack the string value to elt type.

                                                      val unpack_bool : elt -> bool

                                                      Unpack elt type to boolean value.

                                                      val unpack_int : elt -> int

                                                      Unpack elt type to int value.

                                                      val unpack_float : elt -> float

                                                      Unpack elt type to float value.

                                                      val unpack_string : elt -> string

                                                      Unpack elt type to string value.

                                                      Packing & unpacking series
                                                      val pack_bool_series : bool array -> series

                                                      Pack boolean array to series type.

                                                      val pack_int_series : int array -> series

                                                      Pack int array to series type.

                                                      val pack_float_series : float array -> series

                                                      Pack float array to series type.

                                                      val pack_string_series : string array -> series

                                                      Pack string array to series type.

                                                      val unpack_bool_series : series -> bool array

                                                      Unpack series type to boolean array.

                                                      val unpack_int_series : series -> int array

                                                      Unpack series type to int array.

                                                      val unpack_float_series : series -> float array

                                                      Unpack series type to float array.

                                                      val unpack_string_series : series -> string array

                                                      Unpack series type to string array.

                                                      Obtain properties
                                                      val row_num : t -> int

                                                      row_num x returns the number of rows in x.

                                                      val col_num : t -> int

                                                      col_num x returns the number of columns in x.

                                                      val shape : t -> int * int

                                                      shape x returns the shape of x, i.e. (row numnber, column number).

                                                      val numel : t -> int

                                                      numel x returns the number of elements in x.

                                                      val types : t -> string array

                                                      types x returns the string representation of column types.

                                                      val get_heads : t -> string array

                                                      get_heads x returns the column names of x.

                                                      val set_heads : t -> string array -> unit

                                                      set_heads x head_names sets head_names as the column names of x.

                                                      val id_to_head : t -> int -> string

                                                      id_to_head head_name converts head name to its corresponding column index.

                                                      val head_to_id : t -> string -> int

                                                      head_to_id i converts column index i to its corresponding head name.

                                                      Basic get and set functions
                                                      val get : t -> int -> int -> elt

                                                      get x i j returns the element at (i,j).

                                                      val set : t -> int -> int -> elt -> unit

                                                      set x i j v sets the value of element at (i,j) to v.

                                                      val get_by_name : t -> int -> string -> elt

                                                      get_by_name x i head_name is similar to get but uses column name.

                                                      val set_by_name : t -> int -> string -> elt -> unit

                                                      set_by_name x i head_name is similar to set but uses column name.

                                                      val get_row : t -> int -> elt array

                                                      get_row x i returns the ith row in x.

                                                      val get_col : t -> int -> series

                                                      get_col x i returns the ith column in x.

                                                      val get_rows : t -> int array -> elt array array

                                                      get_rows x a returns the rows of x specified in a.

                                                      val get_cols : t -> int array -> series array

                                                      get_cols x a returns the columns of x specified in a.

                                                      val get_col_by_name : t -> string -> series

                                                      get_col_by_name is similar to get_col but uses column name.

                                                      val get_cols_by_name : t -> string array -> series array

                                                      get_cols_by_name is similar to get_cols but uses column names.

                                                      val get_slice : int list list -> t -> t

                                                      get_slice s x returns a slice of x defined by s. For more details, please refer to :doc:`owl_dense_ndarray_generic`.

                                                      val get_slice_by_name : (int list * string list) -> t -> t

                                                      get_slice_by_name is similar to get_slice but uses column name.

                                                      val head : int -> t -> t

                                                      head n x returns top n rows of x.

                                                      val tail : int -> t -> t

                                                      tail n x returns bottom n rows of x.

                                                      Core operations
                                                      val make : ?data:series array -> string array -> t

                                                      make ~data head_names creates a dataframe with an array of series data and corresponding column names. If data is not passed in, the function will return an empty dataframe.

                                                      val copy : t -> t

                                                      copy x returns a copy of dataframe x.

                                                      val copy_struct : t -> t

                                                      copy_struct x only copies the structure of x with empty series.

                                                      val reset : t -> unit

                                                      reset x resets the dataframe x by setting all the time series to empty.

                                                      val unique : t -> string -> series

                                                      unique x removes the duplicates from the dataset and only returns the unique ones.

                                                      val sort : ?inc:bool -> t -> string -> t

                                                      sort ~inc x head sorts the entries in the dataframe x according to the specified column by head name head. By default, inc equals true, indicating increasing order.

                                                      val min_i : t -> string -> int

                                                      min_i x head returns the row index of the minimum value in the column specified by the head name.

                                                      val max_i : t -> string -> int

                                                      max_i x head returns the row index of the maximum value in the column specified by the head name.

                                                      val append_row : t -> elt array -> unit

                                                      append_row x row appends a row to the dataframe x.

                                                      val append_col : t -> series -> string -> unit

                                                      append_col x col appends a column to the dataframe x.

                                                      val insert_row : t -> int -> elt array -> unit

                                                      insert_row x i row inserts one row with at position i into dataframe x.

                                                      val insert_col : t -> int -> string -> series -> unit

                                                      insert_col x j col_head s inserts series s with column head col_head at position j into dataframe x.

                                                      val remove_row : t -> int -> unit

                                                      remove_row x i removes the ith row of x. Negative index is accepted.

                                                      val remove_col : t -> int -> unit

                                                      remove_col x i removes the ith column of x. Negative index is accepted.

                                                      val concat_horizontal : t -> t -> t

                                                      concat_horizontal x y merges two dataframes x and y. Note that x and y must have the same number of rows, and each column name should be unique.

                                                      val concat_vertical : t -> t -> t

                                                      concat_vertical x y concatenates two dataframes by appending y to x. The two dataframes x and y must have the same number of columns and the same column names.

                                                      Iteration functions
                                                      val iteri_row : (int -> elt array -> unit) -> t -> unit

                                                      iteri_row f x iterates the rows of x and applies f.

                                                      val iter_row : (elt array -> unit) -> t -> unit

                                                      iter_row is similar to iteri_row without passing in row indices.

                                                      val mapi_row : (int -> elt array -> elt array) -> t -> t

                                                      mapi_row f x transforms current dataframe x to a new dataframe by applying function f. Note that the returned value of f must be consistent with x w.r.t to its length and type, otherwise runtime error will occur.

                                                      val map_row : (elt array -> elt array) -> t -> t

                                                      map_row is similar to mapi_row but without passing in row indices.

                                                      val filteri_row : (int -> elt array -> bool) -> t -> t

                                                      filteri_row creates a new dataframe from x by filtering out those rows which satisfy the condition f.

                                                      val filter_row : (elt array -> bool) -> t -> t

                                                      filter_row is similar to filteri_row without passing in row indices.

                                                      val filter_mapi_row : (int -> elt array -> elt array option) -> t -> t

                                                      filter_map_row f x creates a new dataframe from x by applying f to each row. If f returns None then the row is excluded in the returned dataframe; if f returns Some row then the row is included.

                                                      val filter_map_row : (elt array -> elt array option) -> t -> t

                                                      filter_map_row is similar to filter_mapi_row without passing in row indices.

                                                      Extended indexing operators
                                                      val (.%()) : t -> (int * string) -> elt

                                                      Extended indexing operator associated with get_by_name function.

                                                      val (.%()<-) : t -> (int * string) -> elt -> unit

                                                      Extended indexing operator associated with set_by_name function.

                                                      val (.?()) : t -> (elt array -> bool) -> t

                                                      Extended indexing operator associated with filter_row function.

                                                      val (.?()<-) : t -> (elt array -> bool) -> (elt array -> elt array) -> t

                                                      Extended indexing operator associated with filter_map_row function. Given a dataframe x, f is used for filtering and g is used for transforming. In other words, x.?(f) <- g means that if f row is true then g row is included in the returned dataframe.

                                                      val (.$()) : t -> (int list * string list) -> t

                                                      Extended indexing operator associated with get_slice_by_name function.

                                                      IO & helper functions
                                                      val of_csv : ?sep:char -> ?head:string array -> ?types:string array -> diff --git a/docs/owl-base/Owl_exception/index.html b/docs/owl-base/Owl_exception/index.html index 479c067f6..cac70f392 100644 --- a/docs/owl-base/Owl_exception/index.html +++ b/docs/owl-base/Owl_exception/index.html @@ -1,2 +1,2 @@ -Owl_exception (owl-base.Owl_exception)

                                                      Module Owl_exception

                                                      Core function
                                                      val check : bool -> exn -> unit

                                                      check p e raises the exception e if the predicate p is false, otherwise returns unit.

                                                      Parameters: * p: predicate to check. * e: exception to raise.

                                                      Returns: * unit

                                                      val verify : bool -> (unit -> exn) -> unit

                                                      verify p f calls the function f which further raises an exception if the predicate p is false, otherwise returns unit.

                                                      Parameters: * p: predicate to check. * f: function to raise the exception.

                                                      Returns: * unit

                                                      val to_string : exn -> string

                                                      to_string e converts an exception into a string containing more detailed information for debugging the code.

                                                      val pp_exception : Stdlib.Format.formatter -> exn -> unit

                                                      pp_exception is the pretty printer for Owl exceptions.

                                                      Exception definition
                                                      exception CONV_INVALID_ARGUMENT

                                                      Input arguments of convolution operations are invalid.

                                                      exception NOT_IMPLEMENTED of string

                                                      Exception of not implemented yet.

                                                      exception NOT_SUPPORTED

                                                      Exception of not supported type.

                                                      exception FOUND

                                                      Exception of found an element.

                                                      exception NOT_FOUND

                                                      Exception of not found an element.

                                                      exception EMPTY_ARRAY

                                                      Exception of an empty array

                                                      exception TEST_FAIL

                                                      Unit Test fails.

                                                      exception INVALID_ARGUMENT of string

                                                      Input arguments are invalid.

                                                      exception INVALID_PROBABILITY of float

                                                      Invalid probability value, not within 0,1 range.

                                                      exception LINALG_MATRIX_DOT_SHAPE of int * int * int * int

                                                      Invalid matrix shapes for matrix dot product.

                                                      exception NON_NEGATIVE_INT of int

                                                      Fails if the input is negative.

                                                      exception NOT_SQUARE of int array

                                                      Fails if a matrix is not square.

                                                      exception NOT_MATRIX of int array

                                                      Fails if the input is not a matrix.

                                                      exception DIFFERENT_SHAPE of int array * int array

                                                      Fail if two ndarrays have different shape.

                                                      exception DIFFERENT_SIZE of int * int

                                                      Fail if two ndarrays have different size.

                                                      exception NOT_BROADCASTABLE

                                                      Fail if the shapes of multiple ndarrays are not broadcastable.

                                                      exception NOT_CONVERGE

                                                      Fail to converge.

                                                      exception MAX_ITERATION

                                                      Number of iteration exceeds the threshold.

                                                      exception SINGULAR

                                                      Exception of singular matrix.

                                                      exception NOT_SIMPLEX

                                                      Exception of not being simplex.

                                                      exception INDEX_OUT_OF_BOUND

                                                      Exception of index out of boundary.

                                                      exception ZOO_ILLEGAL_GIST_NAME

                                                      Exception of illegal gist name.

                                                      +Owl_exception (owl-base.Owl_exception)

                                                      Module Owl_exception

                                                      Core function
                                                      val check : bool -> exn -> unit

                                                      check p e raises the exception e if the predicate p is false, otherwise returns unit.

                                                      Parameters: * p: predicate to check. * e: exception to raise.

                                                      Returns: * unit

                                                      val verify : bool -> (unit -> exn) -> unit

                                                      verify p f calls the function f which further raises an exception if the predicate p is false, otherwise returns unit.

                                                      Parameters: * p: predicate to check. * f: function to raise the exception.

                                                      Returns: * unit

                                                      val to_string : exn -> string

                                                      to_string e converts an exception into a string containing more detailed information for debugging the code.

                                                      val pp_exception : Stdlib.Format.formatter -> exn -> unit

                                                      pp_exception is the pretty printer for Owl exceptions.

                                                      Exception definition
                                                      exception CONV_INVALID_ARGUMENT

                                                      Input arguments of convolution operations are invalid.

                                                      exception NOT_IMPLEMENTED of string

                                                      Exception of not implemented yet.

                                                      exception NOT_SUPPORTED

                                                      Exception of not supported type.

                                                      exception FOUND

                                                      Exception of found an element.

                                                      exception NOT_FOUND

                                                      Exception of not found an element.

                                                      exception EMPTY_ARRAY

                                                      Exception of an empty array

                                                      exception TEST_FAIL

                                                      Unit Test fails.

                                                      exception INVALID_ARGUMENT of string

                                                      Input arguments are invalid.

                                                      exception INVALID_PROBABILITY of float

                                                      Invalid probability value, not within 0,1 range.

                                                      exception LINALG_MATRIX_DOT_SHAPE of int * int * int * int

                                                      Invalid matrix shapes for matrix dot product.

                                                      exception NON_NEGATIVE_INT of int

                                                      Fails if the input is negative.

                                                      exception NOT_SQUARE of int array

                                                      Fails if a matrix is not square.

                                                      exception NOT_MATRIX of int array

                                                      Fails if the input is not a matrix.

                                                      exception DIFFERENT_SHAPE of int array * int array

                                                      Fail if two ndarrays have different shape.

                                                      exception DIFFERENT_SIZE of int * int

                                                      Fail if two ndarrays have different size.

                                                      exception NOT_BROADCASTABLE

                                                      Fail if the shapes of multiple ndarrays are not broadcastable.

                                                      exception NOT_CONVERGE

                                                      Fail to converge.

                                                      exception MAX_ITERATION

                                                      Number of iteration exceeds the threshold.

                                                      exception SINGULAR

                                                      Exception of singular matrix.

                                                      exception NOT_SIMPLEX

                                                      Exception of not being simplex.

                                                      exception INDEX_OUT_OF_BOUND

                                                      Exception of index out of boundary.

                                                      exception ZOO_ILLEGAL_GIST_NAME

                                                      Exception of illegal gist name.

                                                      diff --git a/docs/owl-base/Owl_graph/index.html b/docs/owl-base/Owl_graph/index.html index b0c817ae2..15e0a7c5f 100644 --- a/docs/owl-base/Owl_graph/index.html +++ b/docs/owl-base/Owl_graph/index.html @@ -1,5 +1,5 @@ -Owl_graph (owl-base.Owl_graph)

                                                      Module Owl_graph

                                                      Graph module supports basic operations on DAG.

                                                      Type definition
                                                      type order =
                                                      1. | BFS
                                                      2. | DFS
                                                        (*

                                                        Order to traverse a graph, BFS or DFS.

                                                        *)
                                                      type traversal =
                                                      1. | PreOrder
                                                      2. | PostOrder
                                                        (*

                                                        Order of the function evaluation.

                                                        *)
                                                      type dir =
                                                      1. | Ancestor
                                                      2. | Descendant
                                                        (*

                                                        Iteration direction, i.e. ancestors or descendants

                                                        *)
                                                      type 'a node

                                                      type definition of a node

                                                      Obtaining properties
                                                      val id : 'a node -> int

                                                      id x returns the id of node x.

                                                      val name : 'a node -> string

                                                      name x returns the name string of node x.

                                                      val set_name : 'a node -> string -> unit

                                                      set_name x s sets the name string of node x to s.

                                                      val parents : 'a node -> 'a node array

                                                      parents x returns the parents of node x.

                                                      val set_parents : 'a node -> 'a node array -> unit

                                                      set_parents x parents set x parents to parents.

                                                      val children : 'a node -> 'a node array

                                                      children x returns the children of node x.

                                                      val set_children : 'a node -> 'a node array -> unit

                                                      set_children x children sets x children to children.

                                                      val indegree : 'a node -> int

                                                      indegree x returns the in-degree of node x.

                                                      val outdegree : 'a node -> int

                                                      outdegree x returns the out-degree of node x.

                                                      val degree : 'a node -> int

                                                      degree x returns the total number of links of x.

                                                      val attr : 'a node -> 'a

                                                      attr x returns the attr field of node x.

                                                      val set_attr : 'a node -> 'a -> unit

                                                      set_attr x sets the attr field of node x.

                                                      val num_ancestor : 'a node array -> int

                                                      num_ancestor x returns the number of ancestors of x.

                                                      val num_descendant : 'a node array -> int

                                                      num_descendant x returns the number of descendants of x.

                                                      val length : 'a node array -> int

                                                      length x returns the total number of ancestors and descendants of x.

                                                      Manipulation functions
                                                      val node : +Owl_graph (owl-base.Owl_graph)

                                                      Module Owl_graph

                                                      Graph module supports basic operations on DAG.

                                                      Type definition
                                                      type order =
                                                      1. | BFS
                                                      2. | DFS
                                                        (*

                                                        Order to traverse a graph, BFS or DFS.

                                                        *)
                                                      type traversal =
                                                      1. | PreOrder
                                                      2. | PostOrder
                                                        (*

                                                        Order of the function evaluation.

                                                        *)
                                                      type dir =
                                                      1. | Ancestor
                                                      2. | Descendant
                                                        (*

                                                        Iteration direction, i.e. ancestors or descendants

                                                        *)
                                                      type 'a node

                                                      type definition of a node

                                                      Obtaining properties
                                                      val id : 'a node -> int

                                                      id x returns the id of node x.

                                                      val name : 'a node -> string

                                                      name x returns the name string of node x.

                                                      val set_name : 'a node -> string -> unit

                                                      set_name x s sets the name string of node x to s.

                                                      val parents : 'a node -> 'a node array

                                                      parents x returns the parents of node x.

                                                      val set_parents : 'a node -> 'a node array -> unit

                                                      set_parents x parents set x parents to parents.

                                                      val children : 'a node -> 'a node array

                                                      children x returns the children of node x.

                                                      val set_children : 'a node -> 'a node array -> unit

                                                      set_children x children sets x children to children.

                                                      val indegree : 'a node -> int

                                                      indegree x returns the in-degree of node x.

                                                      val outdegree : 'a node -> int

                                                      outdegree x returns the out-degree of node x.

                                                      val degree : 'a node -> int

                                                      degree x returns the total number of links of x.

                                                      val attr : 'a node -> 'a

                                                      attr x returns the attr field of node x.

                                                      val set_attr : 'a node -> 'a -> unit

                                                      set_attr x sets the attr field of node x.

                                                      val num_ancestor : 'a node array -> int

                                                      num_ancestor x returns the number of ancestors of x.

                                                      val num_descendant : 'a node array -> int

                                                      num_descendant x returns the number of descendants of x.

                                                      val length : 'a node array -> int

                                                      length x returns the total number of ancestors and descendants of x.

                                                      Manipulation functions
                                                      val node : ?id:int -> ?name:string -> ?prev:'a node array -> diff --git a/docs/owl-base/Owl_heavyhitters_sketch/Make/argument-1-CM/index.html b/docs/owl-base/Owl_heavyhitters_sketch/Make/argument-1-CM/index.html index 05c052cfc..a1772267b 100644 --- a/docs/owl-base/Owl_heavyhitters_sketch/Make/argument-1-CM/index.html +++ b/docs/owl-base/Owl_heavyhitters_sketch/Make/argument-1-CM/index.html @@ -1,2 +1,2 @@ -CM (owl-base.Owl_heavyhitters_sketch.Make.CM)

                                                      Parameter Make.CM

                                                      Type definition
                                                      type 'a sketch

                                                      The type of Count-Min sketches

                                                      Core functions
                                                      val init : epsilon:float -> delta:float -> 'a sketch

                                                      init epsilon delta initializes a sketch with approximation ratio (1 + epsilon) and failure probability delta.

                                                      val incr : 'a sketch -> 'a -> unit

                                                      incr s x increments the frequency count of x in sketch s in-place.

                                                      val count : 'a sketch -> 'a -> int

                                                      count s x returns the estimated frequency of element x in s.

                                                      val init_from : 'a sketch -> 'a sketch

                                                      init_from s initializes a new empty sketch with the same parameters as s, which can later be merged with s.

                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch

                                                      merge s1 s2 returns a new sketch whose counts are the sum of those in s1 and s2. Raises INVALID_ARGUMENT if the parameters of s1 and s2 do not match.

                                                      +CM (owl-base.Owl_heavyhitters_sketch.Make.CM)

                                                      Parameter Make.CM

                                                      Type definition
                                                      type 'a sketch

                                                      The type of Count-Min sketches

                                                      Core functions
                                                      val init : epsilon:float -> delta:float -> 'a sketch

                                                      init epsilon delta initializes a sketch with approximation ratio (1 + epsilon) and failure probability delta.

                                                      val incr : 'a sketch -> 'a -> unit

                                                      incr s x increments the frequency count of x in sketch s in-place.

                                                      val count : 'a sketch -> 'a -> int

                                                      count s x returns the estimated frequency of element x in s.

                                                      val init_from : 'a sketch -> 'a sketch

                                                      init_from s initializes a new empty sketch with the same parameters as s, which can later be merged with s.

                                                      val merge : 'a sketch -> 'a sketch -> 'a sketch

                                                      merge s1 s2 returns a new sketch whose counts are the sum of those in s1 and s2. Raises INVALID_ARGUMENT if the parameters of s1 and s2 do not match.

                                                      diff --git a/docs/owl-base/Owl_heavyhitters_sketch/Make/index.html b/docs/owl-base/Owl_heavyhitters_sketch/Make/index.html index f22fb9216..943a1f317 100644 --- a/docs/owl-base/Owl_heavyhitters_sketch/Make/index.html +++ b/docs/owl-base/Owl_heavyhitters_sketch/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_heavyhitters_sketch.Make)

                                                      Module Owl_heavyhitters_sketch.Make

                                                      Parameters

                                                      Signature

                                                      Type definition
                                                      type 'a t

                                                      The type of heavy-hitters sketches

                                                      Core functions
                                                      val init : k:float -> epsilon:float -> delta:float -> 'a t

                                                      `init k epsilon delta` initializes a sketch with threshold k, approximation factor epsilon, and failure probability delta.

                                                      val add : 'a t -> 'a -> unit

                                                      `add h x` adds value `x` to sketch `h` in-place.

                                                      val get : 'a t -> ('a * int) list

                                                      `get h` returns a list of all heavy-hitters in sketch `h`, as a (value, frequency) pair, sorted in decreasing order of frequency.

                                                      +Make (owl-base.Owl_heavyhitters_sketch.Make)

                                                      Module Owl_heavyhitters_sketch.Make

                                                      Parameters

                                                      Signature

                                                      Type definition
                                                      type 'a t

                                                      The type of heavy-hitters sketches

                                                      Core functions
                                                      val init : k:float -> epsilon:float -> delta:float -> 'a t

                                                      `init k epsilon delta` initializes a sketch with threshold k, approximation factor epsilon, and failure probability delta.

                                                      val add : 'a t -> 'a -> unit

                                                      `add h x` adds value `x` to sketch `h` in-place.

                                                      val get : 'a t -> ('a * int) list

                                                      `get h` returns a list of all heavy-hitters in sketch `h`, as a (value, frequency) pair, sorted in decreasing order of frequency.

                                                      diff --git a/docs/owl-base/Owl_heavyhitters_sketch/Native/index.html b/docs/owl-base/Owl_heavyhitters_sketch/Native/index.html index cfafd800c..a2ee250b4 100644 --- a/docs/owl-base/Owl_heavyhitters_sketch/Native/index.html +++ b/docs/owl-base/Owl_heavyhitters_sketch/Native/index.html @@ -1,2 +1,2 @@ -Native (owl-base.Owl_heavyhitters_sketch.Native)

                                                      Module Owl_heavyhitters_sketch.Native

                                                      val init : k:float -> epsilon:float -> delta:float -> 'a t
                                                      val add : 'a t -> 'a -> unit
                                                      val get : 'a t -> ('a * int) list
                                                      +Native (owl-base.Owl_heavyhitters_sketch.Native)

                                                      Module Owl_heavyhitters_sketch.Native

                                                      val init : k:float -> epsilon:float -> delta:float -> 'a t
                                                      val add : 'a t -> 'a -> unit
                                                      val get : 'a t -> ('a * int) list
                                                      diff --git a/docs/owl-base/Owl_heavyhitters_sketch/Owl/index.html b/docs/owl-base/Owl_heavyhitters_sketch/Owl/index.html index 4fb553bfa..11ccfb787 100644 --- a/docs/owl-base/Owl_heavyhitters_sketch/Owl/index.html +++ b/docs/owl-base/Owl_heavyhitters_sketch/Owl/index.html @@ -1,2 +1,2 @@ -Owl (owl-base.Owl_heavyhitters_sketch.Owl)

                                                      Module Owl_heavyhitters_sketch.Owl

                                                      val init : k:float -> epsilon:float -> delta:float -> 'a t
                                                      val add : 'a t -> 'a -> unit
                                                      val get : 'a t -> ('a * int) list
                                                      +Owl (owl-base.Owl_heavyhitters_sketch.Owl)

                                                      Module Owl_heavyhitters_sketch.Owl

                                                      val init : k:float -> epsilon:float -> delta:float -> 'a t
                                                      val add : 'a t -> 'a -> unit
                                                      val get : 'a t -> ('a * int) list
                                                      diff --git a/docs/owl-base/Owl_heavyhitters_sketch/index.html b/docs/owl-base/Owl_heavyhitters_sketch/index.html index 3d45cd889..d79245654 100644 --- a/docs/owl-base/Owl_heavyhitters_sketch/index.html +++ b/docs/owl-base/Owl_heavyhitters_sketch/index.html @@ -1,4 +1,4 @@ -Owl_heavyhitters_sketch (owl-base.Owl_heavyhitters_sketch)

                                                      Module Owl_heavyhitters_sketch

                                                      module Make +Owl_heavyhitters_sketch (owl-base.Owl_heavyhitters_sketch)

                                                      Module Owl_heavyhitters_sketch

                                                      module Native : sig ... end
                                                      module Owl : sig ... end
                                                      diff --git a/docs/owl-base/Owl_heavyhitters_sketch_sig/index.html b/docs/owl-base/Owl_heavyhitters_sketch_sig/index.html index b4ccb6a03..64e6d4f28 100644 --- a/docs/owl-base/Owl_heavyhitters_sketch_sig/index.html +++ b/docs/owl-base/Owl_heavyhitters_sketch_sig/index.html @@ -1,2 +1,2 @@ -Owl_heavyhitters_sketch_sig (owl-base.Owl_heavyhitters_sketch_sig)

                                                      Module Owl_heavyhitters_sketch_sig

                                                      module type Sig = sig ... end
                                                      +Owl_heavyhitters_sketch_sig (owl-base.Owl_heavyhitters_sketch_sig)

                                                      Module Owl_heavyhitters_sketch_sig

                                                      module type Sig = sig ... end
                                                      diff --git a/docs/owl-base/Owl_heavyhitters_sketch_sig/module-type-Sig/index.html b/docs/owl-base/Owl_heavyhitters_sketch_sig/module-type-Sig/index.html index e4e2c6607..1350d00d9 100644 --- a/docs/owl-base/Owl_heavyhitters_sketch_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_heavyhitters_sketch_sig/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_heavyhitters_sketch_sig.Sig)

                                                      Module type Owl_heavyhitters_sketch_sig.Sig

                                                      Type definition
                                                      type 'a t

                                                      The type of heavy-hitters sketches

                                                      Core functions
                                                      val init : k:float -> epsilon:float -> delta:float -> 'a t

                                                      `init k epsilon delta` initializes a sketch with threshold k, approximation factor epsilon, and failure probability delta.

                                                      val add : 'a t -> 'a -> unit

                                                      `add h x` adds value `x` to sketch `h` in-place.

                                                      val get : 'a t -> ('a * int) list

                                                      `get h` returns a list of all heavy-hitters in sketch `h`, as a (value, frequency) pair, sorted in decreasing order of frequency.

                                                      +Sig (owl-base.Owl_heavyhitters_sketch_sig.Sig)

                                                      Module type Owl_heavyhitters_sketch_sig.Sig

                                                      Type definition
                                                      type 'a t

                                                      The type of heavy-hitters sketches

                                                      Core functions
                                                      val init : k:float -> epsilon:float -> delta:float -> 'a t

                                                      `init k epsilon delta` initializes a sketch with threshold k, approximation factor epsilon, and failure probability delta.

                                                      val add : 'a t -> 'a -> unit

                                                      `add h x` adds value `x` to sketch `h` in-place.

                                                      val get : 'a t -> ('a * int) list

                                                      `get h` returns a list of all heavy-hitters in sketch `h`, as a (value, frequency) pair, sorted in decreasing order of frequency.

                                                      diff --git a/docs/owl-base/Owl_io/index.html b/docs/owl-base/Owl_io/index.html index 0f5069741..515e43216 100644 --- a/docs/owl-base/Owl_io/index.html +++ b/docs/owl-base/Owl_io/index.html @@ -1,23 +1,23 @@ -Owl_io (owl-base.Owl_io)

                                                      Module Owl_io

                                                      Read and write operations
                                                      val read_file : ?trim:bool -> string -> string array

                                                      TODO

                                                      val read_file_string : string -> string

                                                      TODO

                                                      val write_file : ?_flag:Stdlib.open_flag -> string -> string -> unit

                                                      TODO

                                                      val marshal_from_file : string -> 'a

                                                      TODO

                                                      val marshal_to_file : +Owl_io (owl-base.Owl_io)

                                                      Module Owl_io

                                                      Read and write operations
                                                      val read_file : ?trim:bool -> string -> string array

                                                      read_file ?trim filename reads the contents of the file specified by filename and returns an array of strings, where each string represents a line from the file.

                                                      • trim: If set to true, leading and trailing whitespace from each line is removed.
                                                      val read_file_string : string -> string

                                                      read_file_string filename reads the entire contents of the file specified by filename into a single string. Returns the contents of the file as a string.

                                                      val write_file : ?_flag:Stdlib.open_flag -> string -> string -> unit

                                                      write_file ?_flag filename content writes the content to the file specified by filename.

                                                      • _flag: Optional file opening flag, such as Open_append or Open_trunc. The default behavior is to overwrite the file if it exists.
                                                      val marshal_from_file : string -> 'a

                                                      marshal_from_file filename deserializes data from the file specified by filename using OCaml's Marshal module. Returns the deserialized data.

                                                      val marshal_to_file : ?flags:Stdlib.Marshal.extern_flags list -> 'a -> string -> - unit

                                                      TODO

                                                      val read_csv : ?sep:char -> string -> string array array

                                                      TODO

                                                      val write_csv : ?sep:char -> string array array -> string -> unit

                                                      TODO

                                                      val read_csv_proc : + unit

                                                      marshal_to_file ?flags data filename serializes the data and writes it to the file specified by filename using OCaml's Marshal module.

                                                      • flags: Optional flags for controlling the serialization behavior.
                                                      val read_csv : ?sep:char -> string -> string array array

                                                      read_csv ?sep filename reads a CSV file specified by filename and returns a 2D array of strings, where each sub-array represents a row.

                                                      • sep: The character used to separate fields. The default separator is a comma (',').
                                                      val write_csv : ?sep:char -> string array array -> string -> unit

                                                      write_csv ?sep data filename writes the 2D array of strings data to the file specified by filename in CSV format.

                                                      • sep: The character used to separate fields. The default separator is a comma (',').
                                                      val read_csv_proc : ?sep:char -> (int -> string array -> unit) -> string -> - unit

                                                      TODO

                                                      val write_csv_proc : + unit

                                                      read_csv_proc ?sep f filename processes each row of the CSV file specified by filename using the function f.

                                                      • sep: The character used to separate fields. The default separator is a comma (','). The function f takes an index and a row (as a string array) as input.
                                                      val write_csv_proc : ?sep:char -> 'a array array -> ('a -> string) -> string -> - unit

                                                      TODO

                                                      Iteration functions
                                                      val iteri_lines_of_file : + unit

                                                      write_csv_proc ?sep data to_string filename writes the 2D array of data data to the file specified by filename in CSV format.

                                                      • sep: The character used to separate fields. The default separator is a comma (','). The function to_string is used to convert each element to a string.
                                                      Iteration functions
                                                      val iteri_lines_of_file : ?verbose:bool -> (int -> string -> unit) -> string -> - unit

                                                      TODO

                                                      val mapi_lines_of_file : (int -> string -> 'a) -> string -> 'a array

                                                      TODO

                                                      val iteri_lines_of_marshal : + unit

                                                      iteri_lines_of_file ?verbose f filename iterates over each line of the file specified by filename, applying the function f to each line.

                                                      • verbose: If true, prints progress information. The default is false. The function f takes the line index and the line content as input.
                                                      val mapi_lines_of_file : (int -> string -> 'a) -> string -> 'a array

                                                      mapi_lines_of_file f filename maps the function f over each line of the file specified by filename, returning an array of results. The function f takes the line index and the line content as input and returns a value of type 'a.

                                                      val iteri_lines_of_marshal : ?verbose:bool -> (int -> 'a -> unit) -> string -> - unit

                                                      TODO

                                                      val mapi_lines_of_marshal : (int -> 'a -> 'b) -> string -> 'b array

                                                      TODO

                                                      Helper functions
                                                      val head : int -> string -> string array

                                                      TODO

                                                      val csv_head : ?sep:char -> int -> string -> string array

                                                      TODO

                                                      + unit

                                                      iteri_lines_of_marshal ?verbose f filename iterates over each line of serialized data in the file specified by filename, deserializing it and applying the function f.

                                                      • verbose: If true, prints progress information. The default is false. The function f takes the line index and the deserialized data as input.
                                                      val mapi_lines_of_marshal : (int -> 'a -> 'b) -> string -> 'b array

                                                      mapi_lines_of_marshal f filename maps the function f over each line of serialized data in the file specified by filename, deserializing it and returning an array of results. The function f takes the line index and the deserialized data as input and returns a value of type 'b.

                                                      Helper functions
                                                      val head : int -> string -> string array

                                                      head n filename reads the first n lines of the file specified by filename and returns them as an array of strings.

                                                      val csv_head : ?sep:char -> int -> string -> string array

                                                      csv_head ?sep n filename reads the first n lines of the CSV file specified by filename and returns them as an array of strings.

                                                      • sep: The character used to separate fields. The default separator is a comma (',').
                                                      diff --git a/docs/owl-base/Owl_lazy/Make/argument-1-A/Linalg/index.html b/docs/owl-base/Owl_lazy/Make/argument-1-A/Linalg/index.html index dcbb1deb7..e4f41bcea 100644 --- a/docs/owl-base/Owl_lazy/Make/argument-1-A/Linalg/index.html +++ b/docs/owl-base/Owl_lazy/Make/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_lazy.Make.A.Linalg)

                                                      Module A.Linalg

                                                      val inv : arr -> arr
                                                      val logdet : arr -> elt
                                                      val chol : ?upper:bool -> arr -> arr
                                                      val svd : ?thin:bool -> arr -> arr * arr * arr
                                                      val qr : arr -> arr * arr
                                                      val lq : arr -> arr * arr
                                                      val sylvester : arr -> arr -> arr -> arr
                                                      val lyapunov : arr -> arr -> arr
                                                      val discrete_lyapunov : +Linalg (owl-base.Owl_lazy.Make.A.Linalg)

                                                      Module A.Linalg

                                                      val inv : arr -> arr
                                                      val logdet : arr -> elt
                                                      val chol : ?upper:bool -> arr -> arr
                                                      val svd : ?thin:bool -> arr -> arr * arr * arr
                                                      val qr : arr -> arr * arr
                                                      val lq : arr -> arr * arr
                                                      val sylvester : arr -> arr -> arr -> arr
                                                      val lyapunov : arr -> arr -> arr
                                                      val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_lazy/Make/argument-1-A/Mat/index.html b/docs/owl-base/Owl_lazy/Make/argument-1-A/Mat/index.html index a95372150..f5858771e 100644 --- a/docs/owl-base/Owl_lazy/Make/argument-1-A/Mat/index.html +++ b/docs/owl-base/Owl_lazy/Make/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_lazy.Make.A.Mat)

                                                      Module A.Mat

                                                      val diagm : ?k:int -> arr -> arr
                                                      val triu : ?k:int -> arr -> arr
                                                      val tril : ?k:int -> arr -> arr
                                                      val eye : int -> arr
                                                      +Mat (owl-base.Owl_lazy.Make.A.Mat)

                                                      Module A.Mat

                                                      val diagm : ?k:int -> arr -> arr
                                                      val triu : ?k:int -> arr -> arr
                                                      val tril : ?k:int -> arr -> arr
                                                      val eye : int -> arr
                                                      diff --git a/docs/owl-base/Owl_lazy/Make/argument-1-A/Scalar/index.html b/docs/owl-base/Owl_lazy/Make/argument-1-A/Scalar/index.html index 02eed4c25..135d2f7ee 100644 --- a/docs/owl-base/Owl_lazy/Make/argument-1-A/Scalar/index.html +++ b/docs/owl-base/Owl_lazy/Make/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_lazy.Make.A.Scalar)

                                                      Module A.Scalar

                                                      val add : elt -> elt -> elt
                                                      val sub : elt -> elt -> elt
                                                      val mul : elt -> elt -> elt
                                                      val div : elt -> elt -> elt
                                                      val pow : elt -> elt -> elt
                                                      val atan2 : elt -> elt -> elt
                                                      val abs : elt -> elt
                                                      val neg : elt -> elt
                                                      val sqr : elt -> elt
                                                      val sqrt : elt -> elt
                                                      val exp : elt -> elt
                                                      val log : elt -> elt
                                                      val log2 : elt -> elt
                                                      val log10 : elt -> elt
                                                      val signum : elt -> elt
                                                      val floor : elt -> elt
                                                      val ceil : elt -> elt
                                                      val round : elt -> elt
                                                      val sin : elt -> elt
                                                      val cos : elt -> elt
                                                      val tan : elt -> elt
                                                      val sinh : elt -> elt
                                                      val cosh : elt -> elt
                                                      val tanh : elt -> elt
                                                      val asin : elt -> elt
                                                      val acos : elt -> elt
                                                      val atan : elt -> elt
                                                      val asinh : elt -> elt
                                                      val acosh : elt -> elt
                                                      val atanh : elt -> elt
                                                      val relu : elt -> elt
                                                      val dawsn : elt -> elt
                                                      val sigmoid : elt -> elt
                                                      +Scalar (owl-base.Owl_lazy.Make.A.Scalar)

                                                      Module A.Scalar

                                                      val add : elt -> elt -> elt
                                                      val sub : elt -> elt -> elt
                                                      val mul : elt -> elt -> elt
                                                      val div : elt -> elt -> elt
                                                      val pow : elt -> elt -> elt
                                                      val atan2 : elt -> elt -> elt
                                                      val abs : elt -> elt
                                                      val neg : elt -> elt
                                                      val sqr : elt -> elt
                                                      val sqrt : elt -> elt
                                                      val exp : elt -> elt
                                                      val log : elt -> elt
                                                      val log2 : elt -> elt
                                                      val log10 : elt -> elt
                                                      val signum : elt -> elt
                                                      val floor : elt -> elt
                                                      val ceil : elt -> elt
                                                      val round : elt -> elt
                                                      val sin : elt -> elt
                                                      val cos : elt -> elt
                                                      val tan : elt -> elt
                                                      val sinh : elt -> elt
                                                      val cosh : elt -> elt
                                                      val tanh : elt -> elt
                                                      val asin : elt -> elt
                                                      val acos : elt -> elt
                                                      val atan : elt -> elt
                                                      val asinh : elt -> elt
                                                      val acosh : elt -> elt
                                                      val atanh : elt -> elt
                                                      val relu : elt -> elt
                                                      val dawsn : elt -> elt
                                                      val sigmoid : elt -> elt
                                                      diff --git a/docs/owl-base/Owl_lazy/Make/argument-1-A/index.html b/docs/owl-base/Owl_lazy/Make/argument-1-A/index.html index c5d60fb99..109328373 100644 --- a/docs/owl-base/Owl_lazy/Make/argument-1-A/index.html +++ b/docs/owl-base/Owl_lazy/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_lazy.Make.A)

                                                      Parameter Make.A

                                                      include Owl_types_ndarray_mutable.Sig
                                                      include Owl_types_ndarray_algodiff.Sig
                                                      include Owl_types_ndarray_eltcmp.Sig
                                                      include Owl_types_ndarray_basic.Sig
                                                      type arr
                                                      type elt
                                                      val empty : int array -> arr
                                                      val zeros : int array -> arr
                                                      val ones : int array -> arr
                                                      val create : int array -> elt -> arr
                                                      val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                      val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                      val bernoulli : ?p:elt -> int array -> arr
                                                      val init : int array -> (int -> elt) -> arr
                                                      val init_nd : int array -> (int array -> elt) -> arr
                                                      val shape : arr -> int array
                                                      val numel : arr -> int
                                                      val get : arr -> int array -> elt
                                                      val set : arr -> int array -> elt -> unit
                                                      val get_slice : int list list -> arr -> arr
                                                      val set_slice : int list list -> arr -> arr -> unit
                                                      val get_fancy : Owl_types_common.index list -> arr -> arr
                                                      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                      val copy : arr -> arr
                                                      val copy_ : out:arr -> arr -> unit
                                                      val reset : arr -> unit
                                                      val reshape : arr -> int array -> arr
                                                      val reverse : arr -> arr
                                                      val tile : arr -> int array -> arr
                                                      val repeat : arr -> int array -> arr
                                                      val concatenate : ?axis:int -> arr array -> arr
                                                      val stack : ?axis:int -> arr array -> arr
                                                      val split : ?axis:int -> int array -> arr -> arr array
                                                      val expand : ?hi:bool -> arr -> int -> arr
                                                      val squeeze : ?axis:int array -> arr -> arr
                                                      val draw : ?axis:int -> arr -> int -> arr * int array
                                                      val map : (elt -> elt) -> arr -> arr
                                                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                      val one_hot : int -> arr -> arr
                                                      val pad : ?v:elt -> int list list -> arr -> arr
                                                      val print : +A (owl-base.Owl_lazy.Make.A)

                                                      Parameter Make.A

                                                      include Owl_types_ndarray_mutable.Sig
                                                      include Owl_types_ndarray_algodiff.Sig
                                                      include Owl_types_ndarray_eltcmp.Sig
                                                      include Owl_types_ndarray_basic.Sig
                                                      type arr
                                                      type elt
                                                      val empty : int array -> arr
                                                      val zeros : int array -> arr
                                                      val ones : int array -> arr
                                                      val create : int array -> elt -> arr
                                                      val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                      val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                      val bernoulli : ?p:elt -> int array -> arr
                                                      val init : int array -> (int -> elt) -> arr
                                                      val init_nd : int array -> (int array -> elt) -> arr
                                                      val shape : arr -> int array
                                                      val numel : arr -> int
                                                      val get : arr -> int array -> elt
                                                      val set : arr -> int array -> elt -> unit
                                                      val get_slice : int list list -> arr -> arr
                                                      val set_slice : int list list -> arr -> arr -> unit
                                                      val get_fancy : Owl_types_common.index list -> arr -> arr
                                                      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                      val copy : arr -> arr
                                                      val copy_ : out:arr -> arr -> unit
                                                      val reset : arr -> unit
                                                      val reshape : arr -> int array -> arr
                                                      val reverse : arr -> arr
                                                      val tile : arr -> int array -> arr
                                                      val repeat : arr -> int array -> arr
                                                      val concatenate : ?axis:int -> arr array -> arr
                                                      val stack : ?axis:int -> arr array -> arr
                                                      val split : ?axis:int -> int array -> arr -> arr array
                                                      val expand : ?hi:bool -> arr -> int -> arr
                                                      val squeeze : ?axis:int array -> arr -> arr
                                                      val draw : ?axis:int -> arr -> int -> arr * int array
                                                      val map : (elt -> elt) -> arr -> arr
                                                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                      val one_hot : int -> arr -> arr
                                                      val pad : ?v:elt -> int list list -> arr -> arr
                                                      val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_lazy/Make/index.html b/docs/owl-base/Owl_lazy/Make/index.html index 9f09c1de4..3793155bc 100644 --- a/docs/owl-base/Owl_lazy/Make/index.html +++ b/docs/owl-base/Owl_lazy/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_lazy.Make)

                                                      Module Owl_lazy.Make

                                                      Parameters

                                                      Signature

                                                      Type definition
                                                      type arr

                                                      TODO

                                                      type elt

                                                      TODO

                                                      type value

                                                      TODO

                                                      type attr

                                                      TODO

                                                      type graph

                                                      TODO

                                                      Type conversion functions
                                                      val arr_to_value : A.arr -> value

                                                      TODO

                                                      val value_to_arr : value -> A.arr

                                                      TODO

                                                      val elt_to_value : A.elt -> value

                                                      TODO

                                                      val value_to_elt : value -> A.elt

                                                      TODO

                                                      val value_to_float : value -> float

                                                      TODO

                                                      val node_to_arr : attr Owl_graph.node -> arr

                                                      TODO

                                                      val arr_to_node : arr -> attr Owl_graph.node

                                                      TODO

                                                      val node_to_elt : attr Owl_graph.node -> elt

                                                      TODO

                                                      val elt_to_node : elt -> attr Owl_graph.node

                                                      TODO

                                                      val pack_arr : A.arr -> arr

                                                      TODO

                                                      val unpack_arr : arr -> A.arr

                                                      TODO

                                                      val pack_elt : A.elt -> elt

                                                      TODO

                                                      val unpack_elt : elt -> A.elt

                                                      TODO

                                                      val float_to_elt : float -> elt

                                                      TODO

                                                      val elt_to_float : elt -> float

                                                      TODO

                                                      Utility functions
                                                      val graph_to_dot : graph -> string

                                                      TODO

                                                      val graph_to_trace : graph -> string

                                                      TODO

                                                      Create variables
                                                      val var_arr : ?shape:int array -> string -> arr

                                                      TODO

                                                      val var_elt : string -> elt

                                                      TODO

                                                      val const_arr : string -> A.arr -> arr

                                                      TODO

                                                      val const_elt : string -> A.elt -> elt

                                                      TODO

                                                      val assign_arr : arr -> A.arr -> unit

                                                      TODO

                                                      val assign_elt : elt -> A.elt -> unit

                                                      TODO

                                                      val unsafe_assign_arr : arr -> A.arr -> unit

                                                      TODO

                                                      Maths functions
                                                      val noop : arr -> arr

                                                      TODO

                                                      val empty : int array -> arr

                                                      TODO

                                                      val zeros : int array -> arr

                                                      TODO

                                                      val ones : int array -> arr

                                                      TODO

                                                      val create : int array -> elt -> arr

                                                      TODO

                                                      val sequential : ?a:elt -> ?step:elt -> int array -> arr

                                                      TODO

                                                      val uniform : ?a:elt -> ?b:elt -> int array -> arr

                                                      TODO

                                                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr

                                                      TODO

                                                      val bernoulli : ?p:elt -> int array -> arr

                                                      TODO

                                                      val init : int array -> (int -> elt) -> arr

                                                      TODO

                                                      val shape : arr -> int array

                                                      TODO

                                                      val numel : arr -> int

                                                      TODO

                                                      val get : arr -> int array -> elt

                                                      TODO

                                                      val set : arr -> int array -> elt -> unit

                                                      TODO

                                                      val get_slice : int list list -> arr -> arr

                                                      TODO

                                                      val set_slice : int list list -> arr -> arr -> unit

                                                      TODO

                                                      val copy : arr -> arr

                                                      TODO

                                                      val reset : arr -> unit

                                                      TODO

                                                      val reshape : arr -> int array -> arr

                                                      TODO

                                                      val reverse : arr -> arr

                                                      TODO

                                                      val tile : arr -> int array -> arr

                                                      TODO

                                                      val repeat : arr -> int array -> arr

                                                      TODO

                                                      val concatenate : ?axis:int -> arr array -> arr

                                                      TODO

                                                      val split : ?axis:int -> int array -> arr -> arr array

                                                      TODO

                                                      val draw : ?axis:int -> arr -> int -> arr * 'a array

                                                      TODO

                                                      val map : (elt -> elt) -> arr -> arr

                                                      TODO

                                                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr

                                                      TODO

                                                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr

                                                      TODO

                                                      val one_hot : int -> arr -> arr

                                                      TODO

                                                      val lazy_print : +Make (owl-base.Owl_lazy.Make)

                                                      Module Owl_lazy.Make

                                                      Parameters

                                                      Signature

                                                      Type definition
                                                      type arr

                                                      TODO

                                                      type elt

                                                      TODO

                                                      type value

                                                      TODO

                                                      type attr

                                                      TODO

                                                      type graph

                                                      TODO

                                                      Type conversion functions
                                                      val arr_to_value : A.arr -> value

                                                      TODO

                                                      val value_to_arr : value -> A.arr

                                                      TODO

                                                      val elt_to_value : A.elt -> value

                                                      TODO

                                                      val value_to_elt : value -> A.elt

                                                      TODO

                                                      val value_to_float : value -> float

                                                      TODO

                                                      val node_to_arr : attr Owl_graph.node -> arr

                                                      TODO

                                                      val arr_to_node : arr -> attr Owl_graph.node

                                                      TODO

                                                      val node_to_elt : attr Owl_graph.node -> elt

                                                      TODO

                                                      val elt_to_node : elt -> attr Owl_graph.node

                                                      TODO

                                                      val pack_arr : A.arr -> arr

                                                      TODO

                                                      val unpack_arr : arr -> A.arr

                                                      TODO

                                                      val pack_elt : A.elt -> elt

                                                      TODO

                                                      val unpack_elt : elt -> A.elt

                                                      TODO

                                                      val float_to_elt : float -> elt

                                                      TODO

                                                      val elt_to_float : elt -> float

                                                      TODO

                                                      Utility functions
                                                      val graph_to_dot : graph -> string

                                                      TODO

                                                      val graph_to_trace : graph -> string

                                                      TODO

                                                      Create variables
                                                      val var_arr : ?shape:int array -> string -> arr

                                                      TODO

                                                      val var_elt : string -> elt

                                                      TODO

                                                      val const_arr : string -> A.arr -> arr

                                                      TODO

                                                      val const_elt : string -> A.elt -> elt

                                                      TODO

                                                      val assign_arr : arr -> A.arr -> unit

                                                      TODO

                                                      val assign_elt : elt -> A.elt -> unit

                                                      TODO

                                                      val unsafe_assign_arr : arr -> A.arr -> unit

                                                      TODO

                                                      Maths functions
                                                      val noop : arr -> arr

                                                      TODO

                                                      val empty : int array -> arr

                                                      TODO

                                                      val zeros : int array -> arr

                                                      TODO

                                                      val ones : int array -> arr

                                                      TODO

                                                      val create : int array -> elt -> arr

                                                      TODO

                                                      val sequential : ?a:elt -> ?step:elt -> int array -> arr

                                                      TODO

                                                      val uniform : ?a:elt -> ?b:elt -> int array -> arr

                                                      TODO

                                                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr

                                                      TODO

                                                      val bernoulli : ?p:elt -> int array -> arr

                                                      TODO

                                                      val init : int array -> (int -> elt) -> arr

                                                      TODO

                                                      val shape : arr -> int array

                                                      TODO

                                                      val numel : arr -> int

                                                      TODO

                                                      val get : arr -> int array -> elt

                                                      TODO

                                                      val set : arr -> int array -> elt -> unit

                                                      TODO

                                                      val get_slice : int list list -> arr -> arr

                                                      TODO

                                                      val set_slice : int list list -> arr -> arr -> unit

                                                      TODO

                                                      val copy : arr -> arr

                                                      TODO

                                                      val reset : arr -> unit

                                                      TODO

                                                      val reshape : arr -> int array -> arr

                                                      TODO

                                                      val reverse : arr -> arr

                                                      TODO

                                                      val tile : arr -> int array -> arr

                                                      TODO

                                                      val repeat : arr -> int array -> arr

                                                      TODO

                                                      val concatenate : ?axis:int -> arr array -> arr

                                                      TODO

                                                      val split : ?axis:int -> int array -> arr -> arr array

                                                      TODO

                                                      val draw : ?axis:int -> arr -> int -> arr * 'a array

                                                      TODO

                                                      val map : (elt -> elt) -> arr -> arr

                                                      TODO

                                                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr

                                                      TODO

                                                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr

                                                      TODO

                                                      val one_hot : int -> arr -> arr

                                                      TODO

                                                      val lazy_print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_lazy/index.html b/docs/owl-base/Owl_lazy/index.html index 6696a8ebe..36d4fd266 100644 --- a/docs/owl-base/Owl_lazy/index.html +++ b/docs/owl-base/Owl_lazy/index.html @@ -1,2 +1,2 @@ -Owl_lazy (owl-base.Owl_lazy)

                                                      Module Owl_lazy

                                                      module Make (A : Owl_types.Ndarray_Mutable) : sig ... end
                                                      +Owl_lazy (owl-base.Owl_lazy)

                                                      Module Owl_lazy

                                                      module Make (A : Owl_types.Ndarray_Mutable) : sig ... end
                                                      diff --git a/docs/owl-base/Owl_log/index.html b/docs/owl-base/Owl_log/index.html index e854eb92e..5c60ac167 100644 --- a/docs/owl-base/Owl_log/index.html +++ b/docs/owl-base/Owl_log/index.html @@ -1,2 +1,2 @@ -Owl_log (owl-base.Owl_log)

                                                      Module Owl_log

                                                      Log module provides logging functionality.

                                                      Type definition
                                                      type level =
                                                      1. | DEBUG
                                                      2. | INFO
                                                      3. | WARN
                                                      4. | ERROR
                                                      5. | FATAL
                                                        (*

                                                        Type definition of log levels, priority is from low to high. Using set_level function to set global logging level to high one can mask the output from low level logging.

                                                        *)
                                                      Configuration functions
                                                      val set_level : level -> unit

                                                      This function sets the global logging level. Low level logging will be omitted.

                                                      val set_output : Stdlib.out_channel -> unit

                                                      This function sets the channel for the logging output. The default one is the standard output.

                                                      val set_color : bool -> unit

                                                      set_color true turns on the colour; set_color false turns it off.

                                                      Log functions
                                                      val debug : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at DEBUG level.

                                                      val info : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at INFO level.

                                                      val warn : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at WARN level.

                                                      val error : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at ERROR level.

                                                      val fatal : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at FATAL level.

                                                      +Owl_log (owl-base.Owl_log)

                                                      Module Owl_log

                                                      Log module provides logging functionality.

                                                      Type definition
                                                      type level =
                                                      1. | DEBUG
                                                      2. | INFO
                                                      3. | WARN
                                                      4. | ERROR
                                                      5. | FATAL
                                                        (*

                                                        Type definition of log levels, priority is from low to high. Using set_level function to set global logging level to high one can mask the output from low level logging.

                                                        *)
                                                      Configuration functions
                                                      val set_level : level -> unit

                                                      This function sets the global logging level. Low level logging will be omitted.

                                                      val set_output : Stdlib.out_channel -> unit

                                                      This function sets the channel for the logging output. The default one is the standard output.

                                                      val set_color : bool -> unit

                                                      set_color true turns on the colour; set_color false turns it off.

                                                      Log functions
                                                      val debug : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at DEBUG level.

                                                      val info : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at INFO level.

                                                      val warn : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at WARN level.

                                                      val error : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at ERROR level.

                                                      val fatal : ('a, Stdlib.out_channel, unit) Stdlib.format -> 'a

                                                      This function outputs log at FATAL level.

                                                      diff --git a/docs/owl-base/Owl_maths_interpolate/index.html b/docs/owl-base/Owl_maths_interpolate/index.html index d113ff5e9..7b2126c15 100644 --- a/docs/owl-base/Owl_maths_interpolate/index.html +++ b/docs/owl-base/Owl_maths_interpolate/index.html @@ -1,2 +1,2 @@ -Owl_maths_interpolate (owl-base.Owl_maths_interpolate)

                                                      Module Owl_maths_interpolate

                                                      Interpolation and Extrapolation

                                                      val polint : float array -> float array -> float -> float * float

                                                      polint xs ys x performs polynomial interpolation of the given arrays xs and ys. Given arrays xs[0..(n-1)] and ys[0..(n-1)], and a value x, the function returns a value y, and an error estimate dy. If P(x) is the polynomial of degree N − 1 such that P(xs[i]) = ys[i] for i = 0,...,n-1,

                                                      Parameters: * xs: an array of input x values of P(x). * ys: an array of corresponding y values of P(x). * x: value to interpolate.

                                                      Returns: * (y, dy) wherein y is the returned value y = P(x), and dy is the estimated error.

                                                      val ratint : float array -> float array -> float -> float * float

                                                      TODO

                                                      +Owl_maths_interpolate (owl-base.Owl_maths_interpolate)

                                                      Module Owl_maths_interpolate

                                                      Interpolation and Extrapolation

                                                      val polint : float array -> float array -> float -> float * float

                                                      polint xs ys x performs polynomial interpolation of the given arrays xs and ys. Given arrays xs[0..(n-1)] and ys[0..(n-1)], and a value x, the function returns a value y, and an error estimate dy. If P(x) is the polynomial of degree N − 1 such that P(xs[i]) = ys[i] for i = 0,...,n-1,

                                                      Parameters: * xs: an array of input x values of P(x). * ys: an array of corresponding y values of P(x). * x: value to interpolate.

                                                      Returns: * (y, dy) wherein y is the returned value y = P(x), and dy is the estimated error.

                                                      val ratint : float array -> float array -> float -> float * float

                                                      TODO

                                                      diff --git a/docs/owl-base/Owl_maths_quadrature/index.html b/docs/owl-base/Owl_maths_quadrature/index.html index 315b63f69..9715a9d5f 100644 --- a/docs/owl-base/Owl_maths_quadrature/index.html +++ b/docs/owl-base/Owl_maths_quadrature/index.html @@ -1,17 +1,34 @@ -Owl_maths_quadrature (owl-base.Owl_maths_quadrature)

                                                      Module Owl_maths_quadrature

                                                      Numerical Integration

                                                      Integration functions
                                                      val trapz : ?n:int -> ?eps:float -> (float -> float) -> float -> float -> float

                                                      trapz f a b computes the integral of f on the interval [a,b] using the trapezoidal rule, i.e. :math:`\int_a^b f(x) dx`.

                                                      Parameters: * f: function to be integrated. * n: the maximum allowed number of steps. The default value is 20. * eps: the desired fractional accuracy. The default value is 1e-6. * a: lower bound of the integrated interval. * b: upper bound of the integrated interval.

                                                      Returns: * y: the integral of f on [a, b].

                                                      val simpson : +Owl_maths_quadrature (owl-base.Owl_maths_quadrature)

                                                      Module Owl_maths_quadrature

                                                      Numerical Integration

                                                      Integration functions
                                                      val trapz : ?n:int -> ?eps:float -> (float -> float) -> float -> float -> float

                                                      trapz f a b computes the integral of f on the interval [a,b] using the trapezoidal rule, i.e. \int_a^b f(x) dx.

                                                      Parameters: * f: function to be integrated. * n: the maximum allowed number of steps. The default value is 20. * eps: the desired fractional accuracy. The default value is 1e-6. * a: lower bound of the integrated interval. * b: upper bound of the integrated interval.

                                                      Returns: * y: the integral of f on [a, b].

                                                      val simpson : ?n:int -> ?eps:float -> (float -> float) -> float -> float -> - float

                                                      simpson f a b computes the integral of f on the interval [a,b] using the Simpson's rule, i.e. :math:`\int_a^b f(x) dx`.

                                                      Parameters: * f: function to be integrated. * n: the maximum allowed number of steps. The default value is 20. * eps: the desired fractional accuracy. The default value is 1e-6. * a: lower bound of the integrated interval. * b: upper bound of the integrated interval.

                                                      Returns: * y: the integral of f on [a, b].

                                                      val romberg : + float

                                                      simpson f a b computes the integral of f on the interval [a,b] using the Simpson's rule, i.e. \int_a^b f(x) dx.

                                                      Parameters: * f: function to be integrated. * n: the maximum allowed number of steps. The default value is 20. * eps: the desired fractional accuracy. The default value is 1e-6. * a: lower bound of the integrated interval. * b: upper bound of the integrated interval.

                                                      Returns: * y: the integral of f on [a, b].

                                                      val romberg : ?n:int -> ?eps:float -> (float -> float) -> float -> float -> - float

                                                      romberg f a b computes the integral of f on the interval [a,b] using the Romberg method, i.e. :math:`\int_a^b f(x) dx`. Note that this algorithm is much faster than trapz and simpson.

                                                      Parameters: * f: function to be integrated. * n: the maximum allowed number of steps. The default value is 20. * eps: the desired fractional accuracy. The default value is 1e-6. * a: lower bound of the integrated interval. * b: upper bound of the integrated interval.

                                                      Returns: * y: the integral of f on [a, b].

                                                      val gaussian_fixed : ?n:int -> (float -> float) -> float -> float -> float

                                                      gaussian_fixed f a b computes the integral of f on the interval [a,b] using the Gaussian quadrature of fixed order. Note that this algorithm is much faster than others due to cached weights.

                                                      Parameters: * f: function to be integrated. * n: the order of polynomial. The default value is 10. * a: lower bound of the integrated interval. * b: upper bound of the integrated interval.

                                                      Returns: * y: the integral of f on [a, b].

                                                      val gaussian : + float

                                                      romberg f a b computes the integral of f on the interval [a,b] using the Romberg method, i.e. \int_a^b f(x) dx. Note that this algorithm is much faster than trapz and simpson.

                                                      Parameters: * f: function to be integrated. * n: the maximum allowed number of steps. The default value is 20. * eps: the desired fractional accuracy. The default value is 1e-6. * a: lower bound of the integrated interval. * b: upper bound of the integrated interval.

                                                      Returns: * y: the integral of f on [a, b].

                                                      val gaussian_fixed : ?n:int -> (float -> float) -> float -> float -> float

                                                      gaussian_fixed f a b computes the integral of f on the interval [a,b] using the Gaussian quadrature of fixed order. Note that this algorithm is much faster than others due to cached weights.

                                                      Parameters: * f: function to be integrated. * n: the order of polynomial. The default value is 10. * a: lower bound of the integrated interval. * b: upper bound of the integrated interval.

                                                      Returns: * y: the integral of f on [a, b].

                                                      val gaussian : ?n:int -> ?eps:float -> (float -> float) -> diff --git a/docs/owl-base/Owl_maths_root/index.html b/docs/owl-base/Owl_maths_root/index.html index ddd1ea471..e5782fdcc 100644 --- a/docs/owl-base/Owl_maths_root/index.html +++ b/docs/owl-base/Owl_maths_root/index.html @@ -1,5 +1,5 @@ -Owl_maths_root (owl-base.Owl_maths_root)

                                                      Module Owl_maths_root

                                                      Root finding algorithms for nonlinear functions

                                                      Type definition
                                                      type solver =
                                                      1. | Bisec
                                                      2. | FalsePos
                                                      3. | Ridder
                                                      4. | Brent
                                                        (*

                                                        Type of root functions of univariate functions.

                                                        *)
                                                      Core functions
                                                      val fzero : +Owl_maths_root (owl-base.Owl_maths_root)

                                                      Module Owl_maths_root

                                                      Root finding algorithms for nonlinear functions

                                                      Type definition
                                                      type solver =
                                                      1. | Bisec
                                                      2. | FalsePos
                                                      3. | Ridder
                                                      4. | Brent
                                                        (*

                                                        Type of root functions of univariate functions.

                                                        *)
                                                      Core functions
                                                      val fzero : ?solver:solver -> ?max_iter:int -> ?xtol:float -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Linalg/index.html index dcd301752..773e2af45 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Linalg)

                                                      Module Operator.Linalg

                                                      val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr
                                                      val svd : +Linalg (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Linalg)

                                                      Module Operator.Linalg

                                                      val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr
                                                      val sylvester : diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Mat/index.html index 67dc251d3..41b7ee3a6 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Mat)

                                                      Module Operator.Mat

                                                      +Mat (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Mat)

                                                      Module Operator.Mat

                                                      diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Scalar/index.html index 71a4f9d76..b23bd4f2e 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Scalar/index.html @@ -1,5 +1,5 @@ -Scalar (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Scalar)

                                                      Module Operator.Scalar

                                                      val add : +Scalar (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Scalar)

                                                      Module Operator.Scalar

                                                      val sub : diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 358512a61..aaa5a7e2c 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                                      Module A.Linalg

                                                      val inv : arr -> arr
                                                      val logdet : arr -> elt
                                                      val chol : ?upper:bool -> arr -> arr
                                                      val svd : ?thin:bool -> arr -> arr * arr * arr
                                                      val qr : arr -> arr * arr
                                                      val lq : arr -> arr * arr
                                                      val sylvester : arr -> arr -> arr -> arr
                                                      val lyapunov : arr -> arr -> arr
                                                      val discrete_lyapunov : +Linalg (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                                      Module A.Linalg

                                                      val inv : arr -> arr
                                                      val logdet : arr -> elt
                                                      val chol : ?upper:bool -> arr -> arr
                                                      val svd : ?thin:bool -> arr -> arr * arr * arr
                                                      val qr : arr -> arr * arr
                                                      val lq : arr -> arr * arr
                                                      val sylvester : arr -> arr -> arr -> arr
                                                      val lyapunov : arr -> arr -> arr
                                                      val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 0b0e98a21..20ef10b25 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                                                      Module A.Mat

                                                      val diagm : ?k:int -> arr -> arr
                                                      val triu : ?k:int -> arr -> arr
                                                      val tril : ?k:int -> arr -> arr
                                                      val eye : int -> arr
                                                      +Mat (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                                                      Module A.Mat

                                                      val diagm : ?k:int -> arr -> arr
                                                      val triu : ?k:int -> arr -> arr
                                                      val tril : ?k:int -> arr -> arr
                                                      val eye : int -> arr
                                                      diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index eadef40b3..93c3d4f97 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                                      Module A.Scalar

                                                      val add : elt -> elt -> elt
                                                      val sub : elt -> elt -> elt
                                                      val mul : elt -> elt -> elt
                                                      val div : elt -> elt -> elt
                                                      val pow : elt -> elt -> elt
                                                      val atan2 : elt -> elt -> elt
                                                      val abs : elt -> elt
                                                      val neg : elt -> elt
                                                      val sqr : elt -> elt
                                                      val sqrt : elt -> elt
                                                      val exp : elt -> elt
                                                      val log : elt -> elt
                                                      val log2 : elt -> elt
                                                      val log10 : elt -> elt
                                                      val signum : elt -> elt
                                                      val floor : elt -> elt
                                                      val ceil : elt -> elt
                                                      val round : elt -> elt
                                                      val sin : elt -> elt
                                                      val cos : elt -> elt
                                                      val tan : elt -> elt
                                                      val sinh : elt -> elt
                                                      val cosh : elt -> elt
                                                      val tanh : elt -> elt
                                                      val asin : elt -> elt
                                                      val acos : elt -> elt
                                                      val atan : elt -> elt
                                                      val asinh : elt -> elt
                                                      val acosh : elt -> elt
                                                      val atanh : elt -> elt
                                                      val relu : elt -> elt
                                                      val dawsn : elt -> elt
                                                      val sigmoid : elt -> elt
                                                      +Scalar (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                                      Module A.Scalar

                                                      val add : elt -> elt -> elt
                                                      val sub : elt -> elt -> elt
                                                      val mul : elt -> elt -> elt
                                                      val div : elt -> elt -> elt
                                                      val pow : elt -> elt -> elt
                                                      val atan2 : elt -> elt -> elt
                                                      val abs : elt -> elt
                                                      val neg : elt -> elt
                                                      val sqr : elt -> elt
                                                      val sqrt : elt -> elt
                                                      val exp : elt -> elt
                                                      val log : elt -> elt
                                                      val log2 : elt -> elt
                                                      val log10 : elt -> elt
                                                      val signum : elt -> elt
                                                      val floor : elt -> elt
                                                      val ceil : elt -> elt
                                                      val round : elt -> elt
                                                      val sin : elt -> elt
                                                      val cos : elt -> elt
                                                      val tan : elt -> elt
                                                      val sinh : elt -> elt
                                                      val cosh : elt -> elt
                                                      val tanh : elt -> elt
                                                      val asin : elt -> elt
                                                      val acos : elt -> elt
                                                      val atan : elt -> elt
                                                      val asinh : elt -> elt
                                                      val acosh : elt -> elt
                                                      val atanh : elt -> elt
                                                      val relu : elt -> elt
                                                      val dawsn : elt -> elt
                                                      val sigmoid : elt -> elt
                                                      diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index 209750758..36e93c5e2 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                                                      Module Device.A

                                                      val empty : int array -> arr
                                                      val zeros : int array -> arr
                                                      val ones : int array -> arr
                                                      val create : int array -> elt -> arr
                                                      val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                      val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                      val bernoulli : ?p:elt -> int array -> arr
                                                      val init : int array -> (int -> elt) -> arr
                                                      val init_nd : int array -> (int array -> elt) -> arr
                                                      val shape : arr -> int array
                                                      val numel : arr -> int
                                                      val get : arr -> int array -> elt
                                                      val set : arr -> int array -> elt -> unit
                                                      val get_slice : int list list -> arr -> arr
                                                      val set_slice : int list list -> arr -> arr -> unit
                                                      val get_fancy : Owl_types_common.index list -> arr -> arr
                                                      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                      val copy : arr -> arr
                                                      val copy_ : out:arr -> arr -> unit
                                                      val reset : arr -> unit
                                                      val reshape : arr -> int array -> arr
                                                      val reverse : arr -> arr
                                                      val tile : arr -> int array -> arr
                                                      val repeat : arr -> int array -> arr
                                                      val concatenate : ?axis:int -> arr array -> arr
                                                      val stack : ?axis:int -> arr array -> arr
                                                      val split : ?axis:int -> int array -> arr -> arr array
                                                      val expand : ?hi:bool -> arr -> int -> arr
                                                      val squeeze : ?axis:int array -> arr -> arr
                                                      val draw : ?axis:int -> arr -> int -> arr * int array
                                                      val map : (elt -> elt) -> arr -> arr
                                                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                      val one_hot : int -> arr -> arr
                                                      val pad : ?v:elt -> int list list -> arr -> arr
                                                      val print : +A (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                                                      Module Device.A

                                                      val empty : int array -> arr
                                                      val zeros : int array -> arr
                                                      val ones : int array -> arr
                                                      val create : int array -> elt -> arr
                                                      val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                      val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                      val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                      val bernoulli : ?p:elt -> int array -> arr
                                                      val init : int array -> (int -> elt) -> arr
                                                      val init_nd : int array -> (int array -> elt) -> arr
                                                      val shape : arr -> int array
                                                      val numel : arr -> int
                                                      val get : arr -> int array -> elt
                                                      val set : arr -> int array -> elt -> unit
                                                      val get_slice : int list list -> arr -> arr
                                                      val set_slice : int list list -> arr -> arr -> unit
                                                      val get_fancy : Owl_types_common.index list -> arr -> arr
                                                      val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                      val copy : arr -> arr
                                                      val copy_ : out:arr -> arr -> unit
                                                      val reset : arr -> unit
                                                      val reshape : arr -> int array -> arr
                                                      val reverse : arr -> arr
                                                      val tile : arr -> int array -> arr
                                                      val repeat : arr -> int array -> arr
                                                      val concatenate : ?axis:int -> arr array -> arr
                                                      val stack : ?axis:int -> arr array -> arr
                                                      val split : ?axis:int -> int array -> arr -> arr array
                                                      val expand : ?hi:bool -> arr -> int -> arr
                                                      val squeeze : ?axis:int array -> arr -> arr
                                                      val draw : ?axis:int -> arr -> int -> arr * int array
                                                      val map : (elt -> elt) -> arr -> arr
                                                      val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                      val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                      val one_hot : int -> arr -> arr
                                                      val pad : ?v:elt -> int list list -> arr -> arr
                                                      val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index 2fc78c334..b6c1c0796 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

                                                      Module Type.Device

                                                      module A : sig ... end
                                                      val make_device : unit -> device
                                                      val arr_to_value : A.arr -> value
                                                      val value_to_arr : value -> A.arr
                                                      val elt_to_value : A.elt -> value
                                                      val value_to_elt : value -> A.elt
                                                      val value_to_float : value -> float
                                                      val is_arr : value -> bool
                                                      val is_elt : value -> bool
                                                      +Device (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

                                                      Module Type.Device

                                                      module A : sig ... end
                                                      val make_device : unit -> device
                                                      val arr_to_value : A.arr -> value
                                                      val value_to_arr : value -> A.arr
                                                      val elt_to_value : A.elt -> value
                                                      val value_to_elt : value -> A.elt
                                                      val value_to_float : value -> float
                                                      val is_arr : value -> bool
                                                      val is_elt : value -> bool
                                                      diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index afa950937..8dd8af6d6 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type)

                                                      Module Shape.Type

                                                      module Device : sig ... end
                                                      and block = E.Graph.Optimiser.Operator.Symbol.Shape.Type.block = {
                                                      1. size : int;
                                                      2. block_id : int;
                                                      3. mutable active : t option;
                                                      4. mutable memory : Device.value;
                                                      5. mutable nodes : t list;
                                                      }
                                                      and attr = E.Graph.Optimiser.Operator.Symbol.Shape.Type.attr = {
                                                      1. mutable op : op;
                                                      2. mutable freeze : bool;
                                                      3. mutable reuse : bool;
                                                      4. mutable state : state;
                                                      5. mutable shape : int array option array;
                                                      6. mutable value : Device.value array;
                                                      7. mutable block : block array option;
                                                      }
                                                      and op = E.Graph.Optimiser.Operator.Symbol.Shape.Type.op =
                                                      1. | Noop
                                                      2. | Var
                                                      3. | Const
                                                      4. | Empty of int array
                                                      5. | Zeros of int array
                                                      6. | Ones of int array
                                                      7. | Create of int array
                                                      8. | Sequential of int array
                                                      9. | Uniform of int array
                                                      10. | Gaussian of int array
                                                      11. | Bernoulli of int array
                                                      12. | Init of int array * int -> elt
                                                      13. | Get of int array
                                                      14. | Set of int array
                                                      15. | GetSlice of int list list
                                                      16. | SetSlice of int list list
                                                      17. | GetFancy of Owl_types_common.index list
                                                      18. | SetFancy of Owl_types_common.index list
                                                      19. | Copy
                                                      20. | Reset
                                                      21. | Reshape of int array
                                                      22. | Reverse
                                                      23. | Tile of int array
                                                      24. | Repeat of int array
                                                      25. | Pad of elt * int list list
                                                      26. | Concatenate of int
                                                      27. | Stack of int
                                                      28. | Split of int * int array
                                                      29. | Draw of int * int
                                                      30. | Map of elt -> elt
                                                      31. | Fold of int * elt -> elt -> elt
                                                      32. | Scan of int * elt -> elt -> elt
                                                      33. | OneHot of int
                                                      34. | OfArray of int array
                                                      35. | Delay of Device.A.arr -> Device.A.arr
                                                      36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                      37. | LazyPrint of int option +Type (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape.Type)

                                                        Module Shape.Type

                                                        module Device : sig ... end
                                                        and block = E.Graph.Optimiser.Operator.Symbol.Shape.Type.block = {
                                                        1. size : int;
                                                        2. block_id : int;
                                                        3. mutable active : t option;
                                                        4. mutable memory : Device.value;
                                                        5. mutable nodes : t list;
                                                        }
                                                        and attr = E.Graph.Optimiser.Operator.Symbol.Shape.Type.attr = {
                                                        1. mutable op : op;
                                                        2. mutable freeze : bool;
                                                        3. mutable reuse : bool;
                                                        4. mutable state : state;
                                                        5. mutable shape : int array option array;
                                                        6. mutable value : Device.value array;
                                                        7. mutable block : block array option;
                                                        }
                                                        and op = E.Graph.Optimiser.Operator.Symbol.Shape.Type.op =
                                                        1. | Noop
                                                        2. | Var
                                                        3. | Const
                                                        4. | Empty of int array
                                                        5. | Zeros of int array
                                                        6. | Ones of int array
                                                        7. | Create of int array
                                                        8. | Sequential of int array
                                                        9. | Uniform of int array
                                                        10. | Gaussian of int array
                                                        11. | Bernoulli of int array
                                                        12. | Init of int array * int -> elt
                                                        13. | Get of int array
                                                        14. | Set of int array
                                                        15. | GetSlice of int list list
                                                        16. | SetSlice of int list list
                                                        17. | GetFancy of Owl_types_common.index list
                                                        18. | SetFancy of Owl_types_common.index list
                                                        19. | Copy
                                                        20. | Reset
                                                        21. | Reshape of int array
                                                        22. | Reverse
                                                        23. | Tile of int array
                                                        24. | Repeat of int array
                                                        25. | Pad of elt * int list list
                                                        26. | Concatenate of int
                                                        27. | Stack of int
                                                        28. | Split of int * int array
                                                        29. | Draw of int * int
                                                        30. | Map of elt -> elt
                                                        31. | Fold of int * elt -> elt -> elt
                                                        32. | Scan of int * elt -> elt -> elt
                                                        33. | OneHot of int
                                                        34. | OfArray of int array
                                                        35. | Delay of Device.A.arr -> Device.A.arr
                                                        36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                        37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                                        38. | Abs
                                                        39. | Neg
                                                        40. | Floor
                                                        41. | Ceil
                                                        42. | Round
                                                        43. | Sqr
                                                        44. | Sqrt
                                                        45. | Log
                                                        46. | Log2
                                                        47. | Log10
                                                        48. | Exp
                                                        49. | Sin
                                                        50. | Cos
                                                        51. | Tan
                                                        52. | Sinh
                                                        53. | Cosh
                                                        54. | Tanh
                                                        55. | Asin
                                                        56. | Acos
                                                        57. | Atan
                                                        58. | Asinh
                                                        59. | Acosh
                                                        60. | Atanh
                                                        61. | Min of bool * int
                                                        62. | Max of bool * int
                                                        63. | Sum of bool * int
                                                        64. | SumReduce of int array
                                                        65. | Signum
                                                        66. | Sigmoid
                                                        67. | Relu
                                                        68. | Dawsn
                                                        69. | Min'
                                                        70. | Max'
                                                        71. | Sum'
                                                        72. | LogSumExp'
                                                        73. | LogSumExp of bool * int
                                                        74. | L1norm'
                                                        75. | L2norm'
                                                        76. | L2NormSqr'
                                                        77. | ClipByValue
                                                        78. | ClipByL2norm
                                                        79. | Pow
                                                        80. | ScalarPow
                                                        81. | PowScalar
                                                        82. | Atan2
                                                        83. | ScalarAtan2
                                                        84. | Atan2Scalar
                                                        85. | Hypot
                                                        86. | Min2
                                                        87. | Max2
                                                        88. | Add
                                                        89. | Sub
                                                        90. | Mul
                                                        91. | Div
                                                        92. | AddScalar
                                                        93. | SubScalar
                                                        94. | MulScalar
                                                        95. | DivScalar
                                                        96. | ScalarAdd
                                                        97. | ScalarSub
                                                        98. | ScalarMul
                                                        99. | ScalarDiv
                                                        100. | FMA
                                                        101. | EltEqual
                                                        102. | EltNotEqual
                                                        103. | EltLess
                                                        104. | EltGreater
                                                        105. | EltLessEqual
                                                        106. | EltGreaterEqual
                                                        107. | EltEqualScalar
                                                        108. | EltNotEqualScalar
                                                        109. | EltLessScalar
                                                        110. | EltGreaterScalar
                                                        111. | EltLessEqualScalar
                                                        112. | EltGreaterEqualScalar
                                                        113. | Conv1d of Owl_types_common.padding * int array
                                                        114. | Conv2d of Owl_types_common.padding * int array
                                                        115. | Conv3d of Owl_types_common.padding * int array
                                                        116. | TransposeConv1d of Owl_types_common.padding * int array
                                                        117. | TransposeConv2d of Owl_types_common.padding * int array
                                                        118. | TransposeConv3d of Owl_types_common.padding * int array
                                                        119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                                        120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                                        121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                                        122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                                        123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                                        124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                                        125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                                        126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                                        127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                                        128. | UpSampling2d of int array
                                                        129. | Conv1dBackwardInput of int array
                                                        130. | Conv1dBackwardKernel of int array
                                                        131. | Conv2dBackwardInput of int array
                                                        132. | Conv2dBackwardKernel of int array
                                                        133. | Conv3dBackwardInput of int array
                                                        134. | Conv3dBackwardKernel of int array
                                                        135. | TransposeConv1dBackwardInput of int array
                                                        136. | TransposeConv1dBackwardKernel of int array
                                                        137. | TransposeConv2dBackwardInput of int array
                                                        138. | TransposeConv2dBackwardKernel of int array
                                                        139. | TransposeConv3dBackwardInput of int array
                                                        140. | TransposeConv3dBackwardKernel of int array
                                                        141. | DilatedConv1dBackwardInput of int array * int array
                                                        142. | DilatedConv1dBackwardKernel of int array * int array
                                                        143. | DilatedConv2dBackwardInput of int array * int array
                                                        144. | DilatedConv2dBackwardKernel of int array * int array
                                                        145. | DilatedConv3dBackwardInput of int array * int array
                                                        146. | DilatedConv3dBackwardKernel of int array * int array
                                                        147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                                        148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                                        149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                                        150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                                        151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                                        152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                                        153. | UpSampling2dBackward of int array
                                                        154. | RowNum
                                                        155. | ColNum
                                                        156. | Row
                                                        157. | Rows of int array
                                                        158. | CopyRowTo
                                                        159. | CopyColTo
                                                        160. | Dot of bool * bool * elt * elt
                                                        161. | Inv
                                                        162. | Trace
                                                        163. | Transpose of int array
                                                        164. | ToRows
                                                        165. | OfRows
                                                        166. | Scalar_Add
                                                        167. | Scalar_Sub
                                                        168. | Scalar_Mul
                                                        169. | Scalar_Div
                                                        170. | Scalar_Pow
                                                        171. | Scalar_Atan2
                                                        172. | Scalar_Abs
                                                        173. | Scalar_Neg
                                                        174. | Scalar_Sqr
                                                        175. | Scalar_Sqrt
                                                        176. | Scalar_Exp
                                                        177. | Scalar_Log
                                                        178. | Scalar_Log2
                                                        179. | Scalar_Log10
                                                        180. | Scalar_Signum
                                                        181. | Scalar_Floor
                                                        182. | Scalar_Ceil
                                                        183. | Scalar_Round
                                                        184. | Scalar_Sin
                                                        185. | Scalar_Cos
                                                        186. | Scalar_Tan
                                                        187. | Scalar_Sinh
                                                        188. | Scalar_Cosh
                                                        189. | Scalar_Tanh
                                                        190. | Scalar_Asin
                                                        191. | Scalar_Acos
                                                        192. | Scalar_Atan
                                                        193. | Scalar_Asinh
                                                        194. | Scalar_Acosh
                                                        195. | Scalar_Atanh
                                                        196. | Scalar_Relu
                                                        197. | Scalar_Dawsn
                                                        198. | Scalar_Sigmoid
                                                        199. | Fused_Adagrad of float * float
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/index.html index f57f792ae..cf13a7fe8 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape)

                                                        Module Symbol.Shape

                                                        module Type : sig ... end
                                                        val infer_shape : +Shape (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol.Shape)

                                                        Module Symbol.Shape

                                                        module Type : sig ... end
                                                        val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/index.html index ecb22cfe8..318b4c30f 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol)

                                                        Module Operator.Symbol

                                                        module Shape : sig ... end
                                                        val op_to_str : Shape.Type.op -> string
                                                        val is_random_variable : Shape.Type.op -> bool
                                                        val refnum : 'a Owl_graph.node -> int
                                                        val node_shape : Shape.Type.attr Owl_graph.node -> int array
                                                        val node_numel : Shape.Type.attr Owl_graph.node -> int
                                                        val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool
                                                        val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit
                                                        val shape_to_str : int array option array -> string
                                                        val node_to_str : Shape.Type.attr Owl_graph.node -> string
                                                        val node_to_arr : Shape.Type.t -> Shape.Type.arr
                                                        val arr_to_node : Shape.Type.arr -> Shape.Type.t
                                                        val node_to_elt : Shape.Type.t -> Shape.Type.elt
                                                        val elt_to_node : Shape.Type.elt -> Shape.Type.t
                                                        val make_node : +Symbol (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator.Symbol)

                                                        Module Operator.Symbol

                                                        module Shape : sig ... end
                                                        val op_to_str : Shape.Type.op -> string
                                                        val is_random_variable : Shape.Type.op -> bool
                                                        val refnum : 'a Owl_graph.node -> int
                                                        val node_shape : Shape.Type.attr Owl_graph.node -> int array
                                                        val node_numel : Shape.Type.attr Owl_graph.node -> int
                                                        val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool
                                                        val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit
                                                        val shape_to_str : int array option array -> string
                                                        val node_to_str : Shape.Type.attr Owl_graph.node -> string
                                                        val node_to_arr : Shape.Type.t -> Shape.Type.arr
                                                        val arr_to_node : Shape.Type.arr -> Shape.Type.t
                                                        val node_to_elt : Shape.Type.t -> Shape.Type.elt
                                                        val elt_to_node : Shape.Type.elt -> Shape.Type.t
                                                        val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/index.html index 8240bb425..6ac9742b3 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/Operator/index.html @@ -1,5 +1,5 @@ -Operator (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator)

                                                        Module Optimiser.Operator

                                                        module Symbol : sig ... end
                                                        val empty : int array -> Symbol.Shape.Type.arr
                                                        val zeros : int array -> Symbol.Shape.Type.arr
                                                        val ones : int array -> Symbol.Shape.Type.arr
                                                        val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr
                                                        val sequential : +Operator (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser.Operator)

                                                        Module Optimiser.Operator

                                                        module Symbol : sig ... end
                                                        val empty : int array -> Symbol.Shape.Type.arr
                                                        val zeros : int array -> Symbol.Shape.Type.arr
                                                        val ones : int array -> Symbol.Shape.Type.arr
                                                        val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr
                                                        val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/index.html index 78d033c9e..b42ece0f0 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser)

                                                        Module Graph.Optimiser

                                                        module Operator : sig ... end
                                                        val estimate_complexity : 'a Owl_graph.node array -> int * int
                                                        val optimise_nodes : +Optimiser (owl-base.Owl_neural_compiler.Make.Engine.Graph.Optimiser)

                                                        Module Graph.Optimiser

                                                        module Operator : sig ... end
                                                        val estimate_complexity : 'a Owl_graph.node array -> int * int
                                                        val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/index.html index adb0e01d5..59d8b9df2 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_neural_compiler.Make.Engine.Graph)

                                                        Module Engine.Graph

                                                        module Optimiser : sig ... end
                                                        type graph = E.Graph.graph
                                                        val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string
                                                        val graph_to_dot : graph -> string
                                                        val graph_to_trace : graph -> string
                                                        val save_graph : 'a -> string -> unit
                                                        val load_graph : string -> 'a * 'b
                                                        val collect_rvs : +Graph (owl-base.Owl_neural_compiler.Make.Engine.Graph)

                                                        Module Engine.Graph

                                                        module Optimiser : sig ... end
                                                        type graph = E.Graph.graph
                                                        val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string
                                                        val graph_to_dot : graph -> string
                                                        val graph_to_trace : graph -> string
                                                        val save_graph : 'a -> string -> unit
                                                        val load_graph : string -> 'a * 'b
                                                        val invalidate_rvs : graph -> unit
                                                        val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Engine/index.html b/docs/owl-base/Owl_neural_compiler/Make/Engine/index.html index d851ad82a..707a75eca 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Engine/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Engine/index.html @@ -1,5 +1,5 @@ -Engine (owl-base.Owl_neural_compiler.Make.Engine)

                                                        Module Make.Engine

                                                        module Graph : sig ... end
                                                        val eval_graph : Graph.graph -> unit
                                                        module Optimiser = Graph.Optimiser
                                                        type graph = E.Graph.graph
                                                        val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string
                                                        val graph_to_dot : graph -> string
                                                        val graph_to_trace : graph -> string
                                                        val save_graph : 'a -> string -> unit
                                                        val load_graph : string -> 'a * 'b
                                                        val collect_rvs : +Engine (owl-base.Owl_neural_compiler.Make.Engine)

                                                        Module Make.Engine

                                                        module Graph : sig ... end
                                                        val eval_graph : Graph.graph -> unit
                                                        module Optimiser = Graph.Optimiser
                                                        type graph = E.Graph.graph
                                                        val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string
                                                        val graph_to_dot : graph -> string
                                                        val graph_to_trace : graph -> string
                                                        val save_graph : 'a -> string -> unit
                                                        val load_graph : string -> 'a * 'b
                                                        val invalidate_rvs : graph -> unit
                                                        val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Activation/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Activation/index.html index 53c81403a..3719808c0 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Activation/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Activation/index.html @@ -1,4 +1,4 @@ -Activation (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Activation)

                                                        Module Neuron.Activation

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Activation.typ =
                                                        1. | Elu
                                                        2. | Relu
                                                        3. | Sigmoid
                                                        4. | HardSigmoid
                                                        5. | Softmax of int
                                                        6. | Softplus
                                                        7. | Softsign
                                                        8. | Tanh
                                                        9. | Relu6
                                                        10. | LeakyRelu of float
                                                        11. | TRelu of float
                                                        12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                        13. | None
                                                        type neuron_typ = +Activation (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Activation)

                                                        Module Neuron.Activation

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Activation.typ =
                                                        1. | Elu
                                                        2. | Relu
                                                        3. | Sigmoid
                                                        4. | HardSigmoid
                                                        5. | Softmax of int
                                                        6. | Softplus
                                                        7. | Softsign
                                                        8. | Tanh
                                                        9. | Relu6
                                                        10. | LeakyRelu of float
                                                        11. | TRelu of float
                                                        12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                        13. | None
                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Activation.neuron_typ = {
                                                        1. mutable activation : typ;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : typ -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t
                                                        val copy : neuron_typ -> neuron_typ
                                                        val activation_to_string : typ -> string
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Add/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Add/index.html index 745d9c08f..1719860b6 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Add/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Add/index.html @@ -1,4 +1,4 @@ -Add (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Add)

                                                        Module Neuron.Add

                                                        type neuron_typ = +Add (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Add)

                                                        Module Neuron.Add

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Add.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AlphaDropout/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AlphaDropout/index.html index 6e00d2bfe..03cc8fcaf 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AlphaDropout/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AlphaDropout/index.html @@ -1,4 +1,4 @@ -AlphaDropout (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.AlphaDropout)

                                                        Module Neuron.AlphaDropout

                                                        type neuron_typ = +AlphaDropout (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.AlphaDropout)

                                                        Module Neuron.AlphaDropout

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.AlphaDropout.neuron_typ = {
                                                        1. mutable rate : float;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : float -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Average/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Average/index.html index 8f889d8be..b8d64ebb4 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Average/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Average/index.html @@ -1,4 +1,4 @@ -Average (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Average)

                                                        Module Neuron.Average

                                                        type neuron_typ = +Average (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Average)

                                                        Module Neuron.Average

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Average.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AvgPool1D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AvgPool1D/index.html index 2d22f3b7c..e3e2ffe58 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AvgPool1D/index.html @@ -1,4 +1,4 @@ -AvgPool1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.AvgPool1D)

                                                        Module Neuron.AvgPool1D

                                                        type neuron_typ = +AvgPool1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.AvgPool1D)

                                                        Module Neuron.AvgPool1D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.AvgPool1D.neuron_typ = {
                                                        1. mutable padding : Owl_types.padding;
                                                        2. mutable kernel : int array;
                                                        3. mutable stride : int array;
                                                        4. mutable in_shape : int array;
                                                        5. mutable out_shape : int array;
                                                        }
                                                        val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AvgPool2D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AvgPool2D/index.html index 59904673c..d01ff3eb3 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/AvgPool2D/index.html @@ -1,4 +1,4 @@ -AvgPool2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.AvgPool2D)

                                                        Module Neuron.AvgPool2D

                                                        type neuron_typ = +AvgPool2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.AvgPool2D)

                                                        Module Neuron.AvgPool2D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.AvgPool2D.neuron_typ = {
                                                        1. mutable padding : Owl_types.padding;
                                                        2. mutable kernel : int array;
                                                        3. mutable stride : int array;
                                                        4. mutable in_shape : int array;
                                                        5. mutable out_shape : int array;
                                                        }
                                                        val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Concatenate/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Concatenate/index.html index 2b34e1da6..b7baa5e1a 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Concatenate/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Concatenate/index.html @@ -1,4 +1,4 @@ -Concatenate (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Concatenate)

                                                        Module Neuron.Concatenate

                                                        type neuron_typ = +Concatenate (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Concatenate)

                                                        Module Neuron.Concatenate

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Concatenate.neuron_typ = {
                                                        1. mutable axis : int;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : int -> neuron_typ
                                                        val connect : int array array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv1D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv1D/index.html index 0c403e074..90ab39108 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv1D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Conv1D)

                                                        Module Neuron.Conv1D

                                                        type neuron_typ = +Conv1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Conv1D)

                                                        Module Neuron.Conv1D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Conv1D.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable kernel : int array;
                                                        4. mutable stride : int array;
                                                        5. mutable padding : Owl_types.padding;
                                                        6. mutable init_typ : Init.typ;
                                                        7. mutable in_shape : int array;
                                                        8. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv2D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv2D/index.html index 24501bc7e..18c79489c 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv2D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Conv2D)

                                                        Module Neuron.Conv2D

                                                        type neuron_typ = +Conv2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Conv2D)

                                                        Module Neuron.Conv2D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Conv2D.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable kernel : int array;
                                                        4. mutable stride : int array;
                                                        5. mutable padding : Owl_types.padding;
                                                        6. mutable init_typ : Init.typ;
                                                        7. mutable in_shape : int array;
                                                        8. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv3D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv3D/index.html index a41792e4e..03a426eab 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv3D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Conv3D)

                                                        Module Neuron.Conv3D

                                                        type neuron_typ = +Conv3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Conv3D)

                                                        Module Neuron.Conv3D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Conv3D.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable kernel : int array;
                                                        4. mutable stride : int array;
                                                        5. mutable padding : Owl_types.padding;
                                                        6. mutable init_typ : Init.typ;
                                                        7. mutable in_shape : int array;
                                                        8. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv1D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv1D/index.html index 536a20d63..02bd88c93 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv1D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.DilatedConv1D)

                                                        Module Neuron.DilatedConv1D

                                                        type neuron_typ = +DilatedConv1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.DilatedConv1D)

                                                        Module Neuron.DilatedConv1D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.DilatedConv1D.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable kernel : int array;
                                                        4. mutable stride : int array;
                                                        5. mutable rate : int array;
                                                        6. mutable padding : Owl_types.padding;
                                                        7. mutable init_typ : Init.typ;
                                                        8. mutable in_shape : int array;
                                                        9. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv2D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv2D/index.html index 96ea3b4ca..a590c225e 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv2D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.DilatedConv2D)

                                                        Module Neuron.DilatedConv2D

                                                        type neuron_typ = +DilatedConv2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.DilatedConv2D)

                                                        Module Neuron.DilatedConv2D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.DilatedConv2D.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable kernel : int array;
                                                        4. mutable stride : int array;
                                                        5. mutable rate : int array;
                                                        6. mutable padding : Owl_types.padding;
                                                        7. mutable init_typ : Init.typ;
                                                        8. mutable in_shape : int array;
                                                        9. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv3D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv3D/index.html index 8a4ecc828..b9ba35050 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv3D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.DilatedConv3D)

                                                        Module Neuron.DilatedConv3D

                                                        type neuron_typ = +DilatedConv3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.DilatedConv3D)

                                                        Module Neuron.DilatedConv3D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.DilatedConv3D.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable kernel : int array;
                                                        4. mutable stride : int array;
                                                        5. mutable rate : int array;
                                                        6. mutable padding : Owl_types.padding;
                                                        7. mutable init_typ : Init.typ;
                                                        8. mutable in_shape : int array;
                                                        9. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Dot/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Dot/index.html index 2fa985a77..42c99b118 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Dot/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Dot/index.html @@ -1,4 +1,4 @@ -Dot (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Dot)

                                                        Module Neuron.Dot

                                                        type neuron_typ = +Dot (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Dot)

                                                        Module Neuron.Dot

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Dot.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Dropout/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Dropout/index.html index fe1f6c623..0daa97fbd 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Dropout/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Dropout/index.html @@ -1,4 +1,4 @@ -Dropout (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Dropout)

                                                        Module Neuron.Dropout

                                                        type neuron_typ = +Dropout (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Dropout)

                                                        Module Neuron.Dropout

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Dropout.neuron_typ = {
                                                        1. mutable rate : float;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : float -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Embedding/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Embedding/index.html index ba6221c6b..9309ac20a 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Embedding/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Embedding/index.html @@ -1,4 +1,4 @@ -Embedding (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Embedding)

                                                        Module Neuron.Embedding

                                                        type neuron_typ = +Embedding (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Embedding)

                                                        Module Neuron.Embedding

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Embedding.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable init_typ : Init.typ;
                                                        3. mutable in_dim : int;
                                                        4. mutable in_shape : int array;
                                                        5. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val init : neuron_typ -> unit
                                                        val reset : neuron_typ -> unit
                                                        val mktag : int -> neuron_typ -> unit
                                                        val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                        val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Flatten/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Flatten/index.html index f935b62c2..b89ffbe60 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Flatten/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Flatten/index.html @@ -1,4 +1,4 @@ -Flatten (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Flatten)

                                                        Module Neuron.Flatten

                                                        type neuron_typ = +Flatten (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Flatten)

                                                        Module Neuron.Flatten

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Flatten.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/FullyConnected/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/FullyConnected/index.html index 3595dc6aa..528763c99 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/FullyConnected/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/FullyConnected/index.html @@ -1,4 +1,4 @@ -FullyConnected (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.FullyConnected)

                                                        Module Neuron.FullyConnected

                                                        type neuron_typ = +FullyConnected (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.FullyConnected)

                                                        Module Neuron.FullyConnected

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.FullyConnected.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable init_typ : Init.typ;
                                                        4. mutable in_shape : int array;
                                                        5. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val init : neuron_typ -> unit
                                                        val reset : neuron_typ -> unit
                                                        val mktag : int -> neuron_typ -> unit
                                                        val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                        val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GRU/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GRU/index.html index a3bf8aeb0..875869b01 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GRU/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GRU/index.html @@ -1,4 +1,4 @@ -GRU (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GRU)

                                                        Module Neuron.GRU

                                                        type neuron_typ = +GRU (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GRU)

                                                        Module Neuron.GRU

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.GRU.neuron_typ = {
                                                        1. mutable wxz : Optimise.Algodiff.t;
                                                        2. mutable whz : Optimise.Algodiff.t;
                                                        3. mutable wxr : Optimise.Algodiff.t;
                                                        4. mutable whr : Optimise.Algodiff.t;
                                                        5. mutable wxh : Optimise.Algodiff.t;
                                                        6. mutable whh : Optimise.Algodiff.t;
                                                        7. mutable bz : Optimise.Algodiff.t;
                                                        8. mutable br : Optimise.Algodiff.t;
                                                        9. mutable bh : Optimise.Algodiff.t;
                                                        10. mutable h : Optimise.Algodiff.t;
                                                        11. mutable init_typ : Init.typ;
                                                        12. mutable in_shape : int array;
                                                        13. mutable out_shape : int array;
                                                        }
                                                        val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val init : neuron_typ -> unit
                                                        val reset : neuron_typ -> unit
                                                        val mktag : int -> neuron_typ -> unit
                                                        val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                        val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GaussianDropout/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GaussianDropout/index.html index 05627f923..bd37568ab 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GaussianDropout/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GaussianDropout/index.html @@ -1,4 +1,4 @@ -GaussianDropout (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GaussianDropout)

                                                        Module Neuron.GaussianDropout

                                                        type neuron_typ = +GaussianDropout (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GaussianDropout)

                                                        Module Neuron.GaussianDropout

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.GaussianDropout.neuron_typ = {
                                                        1. mutable rate : float;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : float -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GaussianNoise/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GaussianNoise/index.html index 32ca2684d..a2b8cb241 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GaussianNoise/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GaussianNoise/index.html @@ -1,4 +1,4 @@ -GaussianNoise (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GaussianNoise)

                                                        Module Neuron.GaussianNoise

                                                        type neuron_typ = +GaussianNoise (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GaussianNoise)

                                                        Module Neuron.GaussianNoise

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.GaussianNoise.neuron_typ = {
                                                        1. mutable sigma : float;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : float -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalAvgPool1D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalAvgPool1D/index.html index 613a1eadc..c9cb0de28 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalAvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalAvgPool1D/index.html @@ -1,4 +1,4 @@ -GlobalAvgPool1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GlobalAvgPool1D)

                                                        Module Neuron.GlobalAvgPool1D

                                                        type neuron_typ = +GlobalAvgPool1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GlobalAvgPool1D)

                                                        Module Neuron.GlobalAvgPool1D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.GlobalAvgPool1D.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalAvgPool2D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalAvgPool2D/index.html index 4df3fcf00..e063f206a 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalAvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalAvgPool2D/index.html @@ -1,4 +1,4 @@ -GlobalAvgPool2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GlobalAvgPool2D)

                                                        Module Neuron.GlobalAvgPool2D

                                                        type neuron_typ = +GlobalAvgPool2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GlobalAvgPool2D)

                                                        Module Neuron.GlobalAvgPool2D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.GlobalAvgPool2D.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalMaxPool1D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalMaxPool1D/index.html index 672063721..5488c32ad 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalMaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalMaxPool1D/index.html @@ -1,4 +1,4 @@ -GlobalMaxPool1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GlobalMaxPool1D)

                                                        Module Neuron.GlobalMaxPool1D

                                                        type neuron_typ = +GlobalMaxPool1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GlobalMaxPool1D)

                                                        Module Neuron.GlobalMaxPool1D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.GlobalMaxPool1D.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalMaxPool2D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalMaxPool2D/index.html index 5d5d835a6..fd0fd339f 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalMaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/GlobalMaxPool2D/index.html @@ -1,4 +1,4 @@ -GlobalMaxPool2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GlobalMaxPool2D)

                                                        Module Neuron.GlobalMaxPool2D

                                                        type neuron_typ = +GlobalMaxPool2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.GlobalMaxPool2D)

                                                        Module Neuron.GlobalMaxPool2D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.GlobalMaxPool2D.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Init/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Init/index.html index a5e5fded2..5757b8f18 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Init/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Init/index.html @@ -1,2 +1,2 @@ -Init (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Init)

                                                        Module Neuron.Init

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Init.typ =
                                                        1. | Uniform of float * float
                                                        2. | Gaussian of float * float
                                                        3. | Standard
                                                        4. | Tanh
                                                        5. | GlorotNormal
                                                        6. | GlorotUniform
                                                        7. | LecunNormal
                                                        8. | HeNormal
                                                        9. | Custom of int array -> Optimise.Algodiff.t
                                                        val calc_fans : int array -> float * float
                                                        val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                        val to_string : typ -> string
                                                        val to_name : unit -> string
                                                        +Init (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Init)

                                                        Module Neuron.Init

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Init.typ =
                                                        1. | Uniform of float * float
                                                        2. | Gaussian of float * float
                                                        3. | Standard
                                                        4. | Tanh
                                                        5. | GlorotNormal
                                                        6. | GlorotUniform
                                                        7. | LecunNormal
                                                        8. | HeNormal
                                                        9. | Custom of int array -> Optimise.Algodiff.t
                                                        val calc_fans : int array -> float * float
                                                        val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                        val to_string : typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Input/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Input/index.html index 995e9c4dc..7ddee9a8f 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Input/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Input/index.html @@ -1,4 +1,4 @@ -Input (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Input)

                                                        Module Neuron.Input

                                                        type neuron_typ = +Input (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Input)

                                                        Module Neuron.Input

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Input.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : int array -> neuron_typ
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LSTM/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LSTM/index.html index 9149937be..502e49ed7 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LSTM/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LSTM/index.html @@ -1,4 +1,4 @@ -LSTM (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.LSTM)

                                                        Module Neuron.LSTM

                                                        type neuron_typ = +LSTM (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.LSTM)

                                                        Module Neuron.LSTM

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.LSTM.neuron_typ = {
                                                        1. mutable wxi : Optimise.Algodiff.t;
                                                        2. mutable whi : Optimise.Algodiff.t;
                                                        3. mutable wxc : Optimise.Algodiff.t;
                                                        4. mutable whc : Optimise.Algodiff.t;
                                                        5. mutable wxf : Optimise.Algodiff.t;
                                                        6. mutable whf : Optimise.Algodiff.t;
                                                        7. mutable wxo : Optimise.Algodiff.t;
                                                        8. mutable who : Optimise.Algodiff.t;
                                                        9. mutable bi : Optimise.Algodiff.t;
                                                        10. mutable bc : Optimise.Algodiff.t;
                                                        11. mutable bf : Optimise.Algodiff.t;
                                                        12. mutable bo : Optimise.Algodiff.t;
                                                        13. mutable c : Optimise.Algodiff.t;
                                                        14. mutable h : Optimise.Algodiff.t;
                                                        15. mutable init_typ : Init.typ;
                                                        16. mutable in_shape : int array;
                                                        17. mutable out_shape : int array;
                                                        }
                                                        val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val init : neuron_typ -> unit
                                                        val reset : neuron_typ -> unit
                                                        val mktag : int -> neuron_typ -> unit
                                                        val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                        val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Lambda/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Lambda/index.html index 896190bf2..a30ec426c 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Lambda/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Lambda)

                                                        Module Neuron.Lambda

                                                        type neuron_typ = +Lambda (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Lambda)

                                                        Module Neuron.Lambda

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Lambda.neuron_typ = {
                                                        1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : ?out_shape:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LambdaArray/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LambdaArray/index.html index ebbe5180a..049e7fdc2 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LambdaArray/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.LambdaArray)

                                                        Module Neuron.LambdaArray

                                                        type neuron_typ = +LambdaArray (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.LambdaArray)

                                                        Module Neuron.LambdaArray

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.LambdaArray.neuron_typ = {
                                                        1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Linear/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Linear/index.html index 57ce702cf..31aeb4048 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Linear/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Linear/index.html @@ -1,4 +1,4 @@ -Linear (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Linear)

                                                        Module Neuron.Linear

                                                        type neuron_typ = +Linear (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Linear)

                                                        Module Neuron.Linear

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Linear.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable init_typ : Init.typ;
                                                        4. mutable in_shape : int array;
                                                        5. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val init : neuron_typ -> unit
                                                        val reset : neuron_typ -> unit
                                                        val mktag : int -> neuron_typ -> unit
                                                        val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                        val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LinearNoBias/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LinearNoBias/index.html index 14b599d0e..b54a48245 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LinearNoBias/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/LinearNoBias/index.html @@ -1,4 +1,4 @@ -LinearNoBias (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.LinearNoBias)

                                                        Module Neuron.LinearNoBias

                                                        type neuron_typ = +LinearNoBias (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.LinearNoBias)

                                                        Module Neuron.LinearNoBias

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.LinearNoBias.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable init_typ : Init.typ;
                                                        3. mutable in_shape : int array;
                                                        4. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val init : neuron_typ -> unit
                                                        val reset : neuron_typ -> unit
                                                        val mktag : int -> neuron_typ -> unit
                                                        val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                        val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                        val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Masking/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Masking/index.html index eff0d65c5..2c6024ac6 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Masking/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Masking)

                                                        Module Neuron.Masking

                                                        +Masking (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Masking)

                                                        Module Neuron.Masking

                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Max/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Max/index.html index 00d8ae57b..571f0e36c 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Max/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Max/index.html @@ -1,4 +1,4 @@ -Max (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Max)

                                                        Module Neuron.Max

                                                        type neuron_typ = +Max (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Max)

                                                        Module Neuron.Max

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Max.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/MaxPool1D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/MaxPool1D/index.html index d4a022f90..77d7012eb 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/MaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/MaxPool1D/index.html @@ -1,4 +1,4 @@ -MaxPool1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.MaxPool1D)

                                                        Module Neuron.MaxPool1D

                                                        type neuron_typ = +MaxPool1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.MaxPool1D)

                                                        Module Neuron.MaxPool1D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.MaxPool1D.neuron_typ = {
                                                        1. mutable padding : Owl_types.padding;
                                                        2. mutable kernel : int array;
                                                        3. mutable stride : int array;
                                                        4. mutable in_shape : int array;
                                                        5. mutable out_shape : int array;
                                                        }
                                                        val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/MaxPool2D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/MaxPool2D/index.html index 02e608e2e..450012760 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/MaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/MaxPool2D/index.html @@ -1,4 +1,4 @@ -MaxPool2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.MaxPool2D)

                                                        Module Neuron.MaxPool2D

                                                        type neuron_typ = +MaxPool2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.MaxPool2D)

                                                        Module Neuron.MaxPool2D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.MaxPool2D.neuron_typ = {
                                                        1. mutable padding : Owl_types.padding;
                                                        2. mutable kernel : int array;
                                                        3. mutable stride : int array;
                                                        4. mutable in_shape : int array;
                                                        5. mutable out_shape : int array;
                                                        }
                                                        val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Mul/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Mul/index.html index e2f6d8f8f..d29c79130 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Mul/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Mul/index.html @@ -1,4 +1,4 @@ -Mul (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Mul)

                                                        Module Neuron.Mul

                                                        type neuron_typ = +Mul (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Mul)

                                                        Module Neuron.Mul

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Mul.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : unit -> neuron_typ
                                                        val connect : int array array -> neuron_typ -> unit
                                                        val copy : 'a -> neuron_typ
                                                        val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Normalisation/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Normalisation/index.html index 4bce76b9b..556d0ede2 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Normalisation/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Normalisation)

                                                        Module Neuron.Normalisation

                                                        type neuron_typ = +Normalisation (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Normalisation)

                                                        Module Neuron.Normalisation

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Normalisation.neuron_typ = {
                                                        1. mutable axis : int;
                                                        2. mutable beta : Optimise.Algodiff.t;
                                                        3. mutable gamma : Optimise.Algodiff.t;
                                                        4. mutable mu : Optimise.Algodiff.t;
                                                        5. mutable var : Optimise.Algodiff.t;
                                                        6. mutable decay : Optimise.Algodiff.t;
                                                        7. mutable training : bool;
                                                        8. mutable in_shape : int array;
                                                        9. mutable out_shape : int array;
                                                        }
                                                        val create : ?training:bool -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html index da368378b..77414cd2a 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                        Module A.Linalg

                                                        val inv : arr -> arr
                                                        val logdet : arr -> elt
                                                        val chol : ?upper:bool -> arr -> arr
                                                        val svd : ?thin:bool -> arr -> arr * arr * arr
                                                        val qr : arr -> arr * arr
                                                        val lq : arr -> arr * arr
                                                        val sylvester : arr -> arr -> arr -> arr
                                                        val lyapunov : arr -> arr -> arr
                                                        val discrete_lyapunov : +Linalg (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                        Module A.Linalg

                                                        val inv : arr -> arr
                                                        val logdet : arr -> elt
                                                        val chol : ?upper:bool -> arr -> arr
                                                        val svd : ?thin:bool -> arr -> arr * arr * arr
                                                        val qr : arr -> arr * arr
                                                        val lq : arr -> arr * arr
                                                        val sylvester : arr -> arr -> arr -> arr
                                                        val lyapunov : arr -> arr -> arr
                                                        val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html index 6b77308bb..ef1eb3c79 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                        Module A.Mat

                                                        val diagm : ?k:int -> arr -> arr
                                                        val triu : ?k:int -> arr -> arr
                                                        val tril : ?k:int -> arr -> arr
                                                        val eye : int -> arr
                                                        +Mat (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                        Module A.Mat

                                                        val diagm : ?k:int -> arr -> arr
                                                        val triu : ?k:int -> arr -> arr
                                                        val tril : ?k:int -> arr -> arr
                                                        val eye : int -> arr
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html index 69282f533..4b4d523fb 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                        Module A.Scalar

                                                        val add : elt -> elt -> elt
                                                        val sub : elt -> elt -> elt
                                                        val mul : elt -> elt -> elt
                                                        val div : elt -> elt -> elt
                                                        val pow : elt -> elt -> elt
                                                        val atan2 : elt -> elt -> elt
                                                        val abs : elt -> elt
                                                        val neg : elt -> elt
                                                        val sqr : elt -> elt
                                                        val sqrt : elt -> elt
                                                        val exp : elt -> elt
                                                        val log : elt -> elt
                                                        val log2 : elt -> elt
                                                        val log10 : elt -> elt
                                                        val signum : elt -> elt
                                                        val floor : elt -> elt
                                                        val ceil : elt -> elt
                                                        val round : elt -> elt
                                                        val sin : elt -> elt
                                                        val cos : elt -> elt
                                                        val tan : elt -> elt
                                                        val sinh : elt -> elt
                                                        val cosh : elt -> elt
                                                        val tanh : elt -> elt
                                                        val asin : elt -> elt
                                                        val acos : elt -> elt
                                                        val atan : elt -> elt
                                                        val asinh : elt -> elt
                                                        val acosh : elt -> elt
                                                        val atanh : elt -> elt
                                                        val relu : elt -> elt
                                                        val dawsn : elt -> elt
                                                        val sigmoid : elt -> elt
                                                        +Scalar (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                        Module A.Scalar

                                                        val add : elt -> elt -> elt
                                                        val sub : elt -> elt -> elt
                                                        val mul : elt -> elt -> elt
                                                        val div : elt -> elt -> elt
                                                        val pow : elt -> elt -> elt
                                                        val atan2 : elt -> elt -> elt
                                                        val abs : elt -> elt
                                                        val neg : elt -> elt
                                                        val sqr : elt -> elt
                                                        val sqrt : elt -> elt
                                                        val exp : elt -> elt
                                                        val log : elt -> elt
                                                        val log2 : elt -> elt
                                                        val log10 : elt -> elt
                                                        val signum : elt -> elt
                                                        val floor : elt -> elt
                                                        val ceil : elt -> elt
                                                        val round : elt -> elt
                                                        val sin : elt -> elt
                                                        val cos : elt -> elt
                                                        val tan : elt -> elt
                                                        val sinh : elt -> elt
                                                        val cosh : elt -> elt
                                                        val tanh : elt -> elt
                                                        val asin : elt -> elt
                                                        val acos : elt -> elt
                                                        val atan : elt -> elt
                                                        val asinh : elt -> elt
                                                        val acosh : elt -> elt
                                                        val atanh : elt -> elt
                                                        val relu : elt -> elt
                                                        val dawsn : elt -> elt
                                                        val sigmoid : elt -> elt
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/index.html index 58dd2eb00..1725833a2 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.A)

                                                        Module Algodiff.A

                                                        type arr = +A (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.A)

                                                        Module Algodiff.A

                                                        val empty : int array -> arr
                                                        val zeros : int array -> arr
                                                        val ones : int array -> arr
                                                        val create : int array -> elt -> arr
                                                        val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                        val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                        val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                        val bernoulli : ?p:elt -> int array -> arr
                                                        val init : int array -> (int -> elt) -> arr
                                                        val init_nd : int array -> (int array -> elt) -> arr
                                                        val shape : arr -> int array
                                                        val numel : arr -> int
                                                        val get : arr -> int array -> elt
                                                        val set : arr -> int array -> elt -> unit
                                                        val get_slice : int list list -> arr -> arr
                                                        val set_slice : int list list -> arr -> arr -> unit
                                                        val get_fancy : Owl_types_common.index list -> arr -> arr
                                                        val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                        val copy : arr -> arr
                                                        val copy_ : out:arr -> arr -> unit
                                                        val reset : arr -> unit
                                                        val reshape : arr -> int array -> arr
                                                        val reverse : arr -> arr
                                                        val tile : arr -> int array -> arr
                                                        val repeat : arr -> int array -> arr
                                                        val concatenate : ?axis:int -> arr array -> arr
                                                        val stack : ?axis:int -> arr array -> arr
                                                        val split : ?axis:int -> int array -> arr -> arr array
                                                        val expand : ?hi:bool -> arr -> int -> arr
                                                        val squeeze : ?axis:int array -> arr -> arr
                                                        val draw : ?axis:int -> arr -> int -> arr * int array
                                                        val map : (elt -> elt) -> arr -> arr
                                                        val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                        val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                        val one_hot : int -> arr -> arr
                                                        val pad : ?v:elt -> int list list -> arr -> arr
                                                        val print : ?max_row:int -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Arr/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Arr/index.html index 185fcd7c6..31096fa60 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Arr)

                                                        Module Algodiff.Arr

                                                        val empty : int array -> t
                                                        val zeros : int array -> t
                                                        val ones : int array -> t
                                                        val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                        val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                        val shape : t -> int array
                                                        val numel : t -> int
                                                        val reset : t -> unit
                                                        val reshape : t -> int array -> t
                                                        val add : t -> t -> t
                                                        val sub : t -> t -> t
                                                        val mul : t -> t -> t
                                                        val div : t -> t -> t
                                                        val dot : t -> t -> t
                                                        +Arr (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Arr)

                                                        Module Algodiff.Arr

                                                        val empty : int array -> t
                                                        val zeros : int array -> t
                                                        val ones : int array -> t
                                                        val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                        val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                        val shape : t -> int array
                                                        val numel : t -> int
                                                        val reset : t -> unit
                                                        val reshape : t -> int array -> t
                                                        val add : t -> t -> t
                                                        val sub : t -> t -> t
                                                        val mul : t -> t -> t
                                                        val div : t -> t -> t
                                                        val dot : t -> t -> t
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/index.html index bb8be6130..cd756f15f 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder)

                                                        Module Algodiff.Builder

                                                        module type Siso = sig ... end
                                                        val build_siso : (module Siso) -> t -> t
                                                        module type Sipo = sig ... end
                                                        val build_sipo : (module Sipo) -> t -> t * t
                                                        module type Sito = sig ... end
                                                        val build_sito : (module Sito) -> t -> t * t * t
                                                        module type Siao = sig ... end
                                                        val build_siao : (module Siao) -> t -> t array
                                                        module type Piso = sig ... end
                                                        val build_piso : (module Piso) -> t -> t -> t
                                                        module type Aiso = sig ... end
                                                        val build_aiso : (module Aiso) -> t array -> t
                                                        +Builder (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder)

                                                        Module Algodiff.Builder

                                                        module type Siso = sig ... end
                                                        val build_siso : (module Siso) -> t -> t
                                                        module type Sipo = sig ... end
                                                        val build_sipo : (module Sipo) -> t -> t * t
                                                        module type Sito = sig ... end
                                                        val build_sito : (module Sito) -> t -> t * t * t
                                                        module type Siao = sig ... end
                                                        val build_siao : (module Siao) -> t -> t array
                                                        module type Piso = sig ... end
                                                        val build_piso : (module Piso) -> t -> t -> t
                                                        module type Aiso = sig ... end
                                                        val build_aiso : (module Aiso) -> t array -> t
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html index 7bbb48f90..5edaa9309 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                        Module type Builder.Aiso

                                                        val label : string
                                                        val ff : t array -> t
                                                        val df : int list -> t -> t array -> t array -> t
                                                        val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                        +Aiso (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                        Module type Builder.Aiso

                                                        val label : string
                                                        val ff : t array -> t
                                                        val df : int list -> t -> t array -> t array -> t
                                                        val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html index be3b34d2d..cd748c4fb 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                        Module type Builder.Piso

                                                        val label : string
                                                        val ff_aa : A.elt -> A.elt -> t
                                                        val ff_ab : A.elt -> A.arr -> t
                                                        val ff_ba : A.arr -> A.elt -> t
                                                        val ff_bb : A.arr -> A.arr -> t
                                                        val df_da : t -> t -> t -> t -> t
                                                        val df_db : t -> t -> t -> t -> t
                                                        val df_dab : t -> t -> t -> t -> t -> t
                                                        val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                        val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                        val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                        +Piso (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                        Module type Builder.Piso

                                                        val label : string
                                                        val ff_aa : A.elt -> A.elt -> t
                                                        val ff_ab : A.elt -> A.arr -> t
                                                        val ff_ba : A.arr -> A.elt -> t
                                                        val ff_bb : A.arr -> A.arr -> t
                                                        val df_da : t -> t -> t -> t -> t
                                                        val df_db : t -> t -> t -> t -> t
                                                        val df_dab : t -> t -> t -> t -> t -> t
                                                        val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                        val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                        val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html index 4d1317f55..61a9e3d34 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                        Module type Builder.Siao

                                                        val label : string
                                                        val ff_f : A.elt -> t array
                                                        val ff_arr : A.arr -> t array
                                                        val df : t array -> t -> t -> t array
                                                        val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                        +Siao (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                        Module type Builder.Siao

                                                        val label : string
                                                        val ff_f : A.elt -> t array
                                                        val ff_arr : A.arr -> t array
                                                        val df : t array -> t -> t -> t array
                                                        val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html index a0636ea03..b5fecee96 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                        Module type Builder.Sipo

                                                        val label : string
                                                        val ff_f : A.elt -> t * t
                                                        val ff_arr : A.arr -> t * t
                                                        val df : t -> t -> t -> t
                                                        val dr : +Sipo (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                        Module type Builder.Sipo

                                                        val label : string
                                                        val ff_f : A.elt -> t * t
                                                        val ff_arr : A.arr -> t * t
                                                        val df : t -> t -> t -> t
                                                        val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html index 0028a463a..65e4bcecb 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                        Module type Builder.Siso

                                                        val label : string
                                                        val ff_f : A.elt -> t
                                                        val ff_arr : A.arr -> t
                                                        val df : t -> t -> t -> t
                                                        val dr : t -> t -> t Stdlib.ref -> t
                                                        +Siso (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                        Module type Builder.Siso

                                                        val label : string
                                                        val ff_f : A.elt -> t
                                                        val ff_arr : A.arr -> t
                                                        val df : t -> t -> t -> t
                                                        val dr : t -> t -> t Stdlib.ref -> t
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html index 140708516..dd1e0a957 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                        Module type Builder.Sito

                                                        val label : string
                                                        val ff_f : A.elt -> t * t * t
                                                        val ff_arr : A.arr -> t * t * t
                                                        val df : t -> t -> t -> t
                                                        val dr : +Sito (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                        Module type Builder.Sito

                                                        val label : string
                                                        val ff_f : A.elt -> t * t * t
                                                        val ff_arr : A.arr -> t * t * t
                                                        val df : t -> t -> t -> t
                                                        val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Linalg/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Linalg/index.html index 3446d442c..22a769d75 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                        Module Algodiff.Linalg

                                                        val inv : t -> t
                                                        val logdet : t -> t
                                                        val chol : ?upper:bool -> t -> t
                                                        val qr : t -> t * t
                                                        val lq : t -> t * t
                                                        val svd : ?thin:bool -> t -> t * t * t
                                                        val sylvester : t -> t -> t -> t
                                                        val lyapunov : t -> t -> t
                                                        val discrete_lyapunov : +Linalg (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                        Module Algodiff.Linalg

                                                        val inv : t -> t
                                                        val logdet : t -> t
                                                        val chol : ?upper:bool -> t -> t
                                                        val qr : t -> t * t
                                                        val lq : t -> t * t
                                                        val svd : ?thin:bool -> t -> t * t * t
                                                        val sylvester : t -> t -> t -> t
                                                        val lyapunov : t -> t -> t
                                                        val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Mat/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Mat/index.html index 8fa893690..17205e684 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Mat)

                                                        Module Algodiff.Mat

                                                        val empty : int -> int -> t
                                                        val zeros : int -> int -> t
                                                        val eye : int -> t
                                                        val ones : int -> int -> t
                                                        val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                        val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                        val shape : t -> int * int
                                                        val numel : t -> int
                                                        val row_num : t -> int
                                                        val col_num : t -> int
                                                        val reset : t -> unit
                                                        val reshape : int -> int -> t -> t
                                                        val get : t -> int -> int -> t
                                                        val set : t -> int -> int -> t -> t
                                                        val row : t -> int -> t
                                                        val mean : t -> t
                                                        val add : t -> t -> t
                                                        val sub : t -> t -> t
                                                        val mul : t -> t -> t
                                                        val div : t -> t -> t
                                                        val dot : t -> t -> t
                                                        val map_by_row : (t -> t) -> t -> t
                                                        val of_arrays : A.elt array array -> t
                                                        val init_2d : int -> int -> (int -> int -> t) -> t
                                                        val print : t -> unit
                                                        +Mat (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Mat)

                                                        Module Algodiff.Mat

                                                        val empty : int -> int -> t
                                                        val zeros : int -> int -> t
                                                        val eye : int -> t
                                                        val ones : int -> int -> t
                                                        val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                        val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                        val shape : t -> int * int
                                                        val numel : t -> int
                                                        val row_num : t -> int
                                                        val col_num : t -> int
                                                        val reset : t -> unit
                                                        val reshape : int -> int -> t -> t
                                                        val get : t -> int -> int -> t
                                                        val set : t -> int -> int -> t -> t
                                                        val row : t -> int -> t
                                                        val mean : t -> t
                                                        val add : t -> t -> t
                                                        val sub : t -> t -> t
                                                        val mul : t -> t -> t
                                                        val div : t -> t -> t
                                                        val dot : t -> t -> t
                                                        val map_by_row : (t -> t) -> t -> t
                                                        val of_arrays : A.elt array array -> t
                                                        val init_2d : int -> int -> (int -> int -> t) -> t
                                                        val print : t -> unit
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Maths/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Maths/index.html index 33f813968..83256793f 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Maths)

                                                        Module Algodiff.Maths

                                                        val (+) : t -> t -> t
                                                        val (-) : t -> t -> t
                                                        val (*) : t -> t -> t
                                                        val (/) : t -> t -> t
                                                        val (*@) : t -> t -> t
                                                        val (**) : t -> t -> t
                                                        val add : t -> t -> t
                                                        val sub : t -> t -> t
                                                        val mul : t -> t -> t
                                                        val div : t -> t -> t
                                                        val kron : t -> t -> t
                                                        val dot : t -> t -> t
                                                        val pow : t -> t -> t
                                                        val atan2 : t -> t -> t
                                                        val min2 : t -> t -> t
                                                        val max2 : t -> t -> t
                                                        val cross_entropy : t -> t -> t
                                                        val inv : t -> t
                                                        val neg : t -> t
                                                        val abs : t -> t
                                                        val signum : t -> t
                                                        val floor : t -> t
                                                        val ceil : t -> t
                                                        val round : t -> t
                                                        val sqr : t -> t
                                                        val sqrt : t -> t
                                                        val log : t -> t
                                                        val log2 : t -> t
                                                        val log10 : t -> t
                                                        val exp : t -> t
                                                        val sin : t -> t
                                                        val cos : t -> t
                                                        val tan : t -> t
                                                        val sinh : t -> t
                                                        val cosh : t -> t
                                                        val tanh : t -> t
                                                        val asin : t -> t
                                                        val acos : t -> t
                                                        val atan : t -> t
                                                        val asinh : t -> t
                                                        val acosh : t -> t
                                                        val atanh : t -> t
                                                        val sum' : t -> t
                                                        val log_sum_exp' : t -> t
                                                        val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                        val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                        val sum_reduce : ?axis:int array -> t -> t
                                                        val mean : t -> t
                                                        val transpose : ?axis:int array -> t -> t
                                                        val swap : int -> int -> t -> t
                                                        val l1norm' : t -> t
                                                        val l2norm' : t -> t
                                                        val l2norm_sqr' : t -> t
                                                        val sigmoid : t -> t
                                                        val relu : t -> t
                                                        val dawsn : t -> t
                                                        val softplus : t -> t
                                                        val softsign : t -> t
                                                        val softmax : ?axis:int -> t -> t
                                                        val reshape : t -> int array -> t
                                                        val flatten : t -> t
                                                        val get_item : t -> int -> int -> t
                                                        val get_row : t -> int -> t
                                                        val concat : axis:int -> t -> t -> t
                                                        val split : axis:int -> int array -> t -> t array
                                                        val of_arrays : t array array -> t
                                                        val to_arrays : t -> t array array
                                                        val concatenate : axis:int -> t array -> t
                                                        val stack : axis:int -> t array -> t
                                                        val get_slice : int list list -> t -> t
                                                        val set_slice : int list list -> t -> t -> t
                                                        val get_fancy : Owl_types.index list -> t -> t
                                                        val set_fancy : Owl_types.index list -> t -> t -> t
                                                        val diag : ?k:int -> t -> t
                                                        val diagm : ?k:int -> t -> t
                                                        val trace : t -> t
                                                        val triu : ?k:int -> t -> t
                                                        val tril : ?k:int -> t -> t
                                                        +Maths (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.Maths)

                                                        Module Algodiff.Maths

                                                        val (+) : t -> t -> t
                                                        val (-) : t -> t -> t
                                                        val (*) : t -> t -> t
                                                        val (/) : t -> t -> t
                                                        val (*@) : t -> t -> t
                                                        val (**) : t -> t -> t
                                                        val add : t -> t -> t
                                                        val sub : t -> t -> t
                                                        val mul : t -> t -> t
                                                        val div : t -> t -> t
                                                        val kron : t -> t -> t
                                                        val dot : t -> t -> t
                                                        val pow : t -> t -> t
                                                        val atan2 : t -> t -> t
                                                        val min2 : t -> t -> t
                                                        val max2 : t -> t -> t
                                                        val cross_entropy : t -> t -> t
                                                        val inv : t -> t
                                                        val neg : t -> t
                                                        val abs : t -> t
                                                        val signum : t -> t
                                                        val floor : t -> t
                                                        val ceil : t -> t
                                                        val round : t -> t
                                                        val sqr : t -> t
                                                        val sqrt : t -> t
                                                        val log : t -> t
                                                        val log2 : t -> t
                                                        val log10 : t -> t
                                                        val exp : t -> t
                                                        val sin : t -> t
                                                        val cos : t -> t
                                                        val tan : t -> t
                                                        val sinh : t -> t
                                                        val cosh : t -> t
                                                        val tanh : t -> t
                                                        val asin : t -> t
                                                        val acos : t -> t
                                                        val atan : t -> t
                                                        val asinh : t -> t
                                                        val acosh : t -> t
                                                        val atanh : t -> t
                                                        val sum' : t -> t
                                                        val log_sum_exp' : t -> t
                                                        val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                        val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                        val sum_reduce : ?axis:int array -> t -> t
                                                        val mean : t -> t
                                                        val transpose : ?axis:int array -> t -> t
                                                        val swap : int -> int -> t -> t
                                                        val l1norm' : t -> t
                                                        val l2norm' : t -> t
                                                        val l2norm_sqr' : t -> t
                                                        val sigmoid : t -> t
                                                        val relu : t -> t
                                                        val dawsn : t -> t
                                                        val softplus : t -> t
                                                        val softsign : t -> t
                                                        val softmax : ?axis:int -> t -> t
                                                        val reshape : t -> int array -> t
                                                        val flatten : t -> t
                                                        val get_item : t -> int -> int -> t
                                                        val get_row : t -> int -> t
                                                        val concat : axis:int -> t -> t -> t
                                                        val split : axis:int -> int array -> t -> t array
                                                        val of_arrays : t array array -> t
                                                        val to_arrays : t -> t array array
                                                        val concatenate : axis:int -> t array -> t
                                                        val stack : axis:int -> t array -> t
                                                        val get_slice : int list list -> t -> t
                                                        val set_slice : int list list -> t -> t -> t
                                                        val get_fancy : Owl_types.index list -> t -> t
                                                        val set_fancy : Owl_types.index list -> t -> t -> t
                                                        val diag : ?k:int -> t -> t
                                                        val diagm : ?k:int -> t -> t
                                                        val trace : t -> t
                                                        val triu : ?k:int -> t -> t
                                                        val tril : ?k:int -> t -> t
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/NN/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/NN/index.html index daff12cb8..5f0571aef 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/NN/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.NN)

                                                        Module Algodiff.NN

                                                        val dropout : ?rate:float -> t -> t
                                                        val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                        val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                        val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                        val dilated_conv1d : +NN (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff.NN)

                                                        Module Algodiff.NN

                                                        val dropout : ?rate:float -> t -> t
                                                        val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                        val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                        val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                        val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/index.html index 43efcd751..323dbfda9 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Algodiff/index.html @@ -1,2 +1,2 @@ -Algodiff (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff)

                                                        Module Optimise.Algodiff

                                                        module A : sig ... end
                                                        type t = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Algodiff.t =
                                                        1. | F of A.elt
                                                        2. | Arr of A.arr
                                                        3. | DF of t * t * int
                                                        4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                        and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                        and register = t list -> t list
                                                        and label = string * t list
                                                        and op = adjoint * register * label
                                                        val tag : unit -> int
                                                        val primal : t -> t
                                                        val primal' : t -> t
                                                        val zero : t -> t
                                                        val reset_zero : t -> t
                                                        val tangent : t -> t
                                                        val adjref : t -> t Stdlib.ref
                                                        val adjval : t -> t
                                                        val shape : t -> int array
                                                        val is_float : t -> bool
                                                        val is_arr : t -> bool
                                                        val row_num : t -> int
                                                        val col_num : t -> int
                                                        val numel : t -> int
                                                        val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                        val clip_by_l2norm : A.elt -> t -> t
                                                        val copy_primal' : t -> t
                                                        val tile : t -> int array -> t
                                                        val repeat : t -> int array -> t
                                                        val pack_elt : A.elt -> t
                                                        val unpack_elt : t -> A.elt
                                                        val pack_flt : float -> t
                                                        val _f : float -> t
                                                        val unpack_flt : t -> float
                                                        val pack_arr : A.arr -> t
                                                        val unpack_arr : t -> A.arr
                                                        val deep_info : t -> string
                                                        val type_info : t -> string
                                                        val error_binop : string -> t -> t -> 'a
                                                        val error_uniop : string -> t -> 'a
                                                        val make_forward : t -> t -> int -> t
                                                        val make_reverse : t -> int -> t
                                                        val reverse_prop : t -> t -> unit
                                                        val diff : (t -> t) -> t -> t
                                                        val diff' : (t -> t) -> t -> t * t
                                                        val grad : (t -> t) -> t -> t
                                                        val grad' : (t -> t) -> t -> t * t
                                                        val jacobian : (t -> t) -> t -> t
                                                        val jacobian' : (t -> t) -> t -> t * t
                                                        val jacobianv : (t -> t) -> t -> t -> t
                                                        val jacobianv' : (t -> t) -> t -> t -> t * t
                                                        val jacobianTv : (t -> t) -> t -> t -> t
                                                        val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                        val hessian : (t -> t) -> t -> t
                                                        val hessian' : (t -> t) -> t -> t * t
                                                        val hessianv : (t -> t) -> t -> t -> t
                                                        val hessianv' : (t -> t) -> t -> t -> t * t
                                                        val laplacian : (t -> t) -> t -> t
                                                        val laplacian' : (t -> t) -> t -> t * t
                                                        val gradhessian : (t -> t) -> t -> t * t
                                                        val gradhessian' : (t -> t) -> t -> t * t * t
                                                        val gradhessianv : (t -> t) -> t -> t -> t * t
                                                        val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                        module Builder : sig ... end
                                                        module Maths : sig ... end
                                                        module Linalg : sig ... end
                                                        module NN : sig ... end
                                                        module Mat : sig ... end
                                                        module Arr : sig ... end
                                                        val to_trace : t list -> string
                                                        val to_dot : t list -> string
                                                        val pp_num : Stdlib.Format.formatter -> t -> unit
                                                        +Algodiff (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Algodiff)

                                                        Module Optimise.Algodiff

                                                        module A : sig ... end
                                                        type t = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Algodiff.t =
                                                        1. | F of A.elt
                                                        2. | Arr of A.arr
                                                        3. | DF of t * t * int
                                                        4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                        and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                        and register = t list -> t list
                                                        and label = string * t list
                                                        and op = adjoint * register * label
                                                        val tag : unit -> int
                                                        val primal : t -> t
                                                        val primal' : t -> t
                                                        val zero : t -> t
                                                        val reset_zero : t -> t
                                                        val tangent : t -> t
                                                        val adjref : t -> t Stdlib.ref
                                                        val adjval : t -> t
                                                        val shape : t -> int array
                                                        val is_float : t -> bool
                                                        val is_arr : t -> bool
                                                        val row_num : t -> int
                                                        val col_num : t -> int
                                                        val numel : t -> int
                                                        val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                        val clip_by_l2norm : A.elt -> t -> t
                                                        val copy_primal' : t -> t
                                                        val tile : t -> int array -> t
                                                        val repeat : t -> int array -> t
                                                        val pack_elt : A.elt -> t
                                                        val unpack_elt : t -> A.elt
                                                        val pack_flt : float -> t
                                                        val _f : float -> t
                                                        val unpack_flt : t -> float
                                                        val pack_arr : A.arr -> t
                                                        val unpack_arr : t -> A.arr
                                                        val deep_info : t -> string
                                                        val type_info : t -> string
                                                        val error_binop : string -> t -> t -> 'a
                                                        val error_uniop : string -> t -> 'a
                                                        val make_forward : t -> t -> int -> t
                                                        val make_reverse : t -> int -> t
                                                        val reverse_prop : t -> t -> unit
                                                        val diff : (t -> t) -> t -> t
                                                        val diff' : (t -> t) -> t -> t * t
                                                        val grad : (t -> t) -> t -> t
                                                        val grad' : (t -> t) -> t -> t * t
                                                        val jacobian : (t -> t) -> t -> t
                                                        val jacobian' : (t -> t) -> t -> t * t
                                                        val jacobianv : (t -> t) -> t -> t -> t
                                                        val jacobianv' : (t -> t) -> t -> t -> t * t
                                                        val jacobianTv : (t -> t) -> t -> t -> t
                                                        val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                        val hessian : (t -> t) -> t -> t
                                                        val hessian' : (t -> t) -> t -> t * t
                                                        val hessianv : (t -> t) -> t -> t -> t
                                                        val hessianv' : (t -> t) -> t -> t -> t * t
                                                        val laplacian : (t -> t) -> t -> t
                                                        val laplacian' : (t -> t) -> t -> t * t
                                                        val gradhessian : (t -> t) -> t -> t * t
                                                        val gradhessian' : (t -> t) -> t -> t * t * t
                                                        val gradhessianv : (t -> t) -> t -> t -> t * t
                                                        val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                        module Builder : sig ... end
                                                        module Maths : sig ... end
                                                        module Linalg : sig ... end
                                                        module NN : sig ... end
                                                        module Mat : sig ... end
                                                        module Arr : sig ... end
                                                        val to_trace : t list -> string
                                                        val to_dot : t list -> string
                                                        val pp_num : Stdlib.Format.formatter -> t -> unit
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Batch/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Batch/index.html index 655260f86..8c0887066 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Batch/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Batch)

                                                        Module Optimise.Batch

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Batch.typ =
                                                        1. | Full
                                                        2. | Mini of int
                                                        3. | Sample of int
                                                        4. | Stochastic
                                                        val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                        val batches : typ -> Algodiff.t -> int
                                                        val to_string : typ -> string
                                                        +Batch (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Batch)

                                                        Module Optimise.Batch

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Batch.typ =
                                                        1. | Full
                                                        2. | Mini of int
                                                        3. | Sample of int
                                                        4. | Stochastic
                                                        val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                        val batches : typ -> Algodiff.t -> int
                                                        val to_string : typ -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Checkpoint/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Checkpoint/index.html index 909a7f932..df478f318 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Checkpoint/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Checkpoint)

                                                        Module Optimise.Checkpoint

                                                        type state = +Checkpoint (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Checkpoint)

                                                        Module Optimise.Checkpoint

                                                        type state = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Checkpoint.state = {
                                                        1. mutable current_batch : int;
                                                        2. mutable batches_per_epoch : int;
                                                        3. mutable epochs : float;
                                                        4. mutable batches : int;
                                                        5. mutable loss : Algodiff.t array;
                                                        6. mutable start_at : float;
                                                        7. mutable stop : bool;
                                                        8. mutable gs : Algodiff.t array array;
                                                        9. mutable ps : Algodiff.t array array;
                                                        10. mutable us : Algodiff.t array array;
                                                        11. mutable ch : Algodiff.t array array array;
                                                        }
                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Checkpoint.typ = diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Clipping/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Clipping/index.html index acbb7eb7f..fcf6bf650 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Clipping/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Clipping/index.html @@ -1,4 +1,4 @@ -Clipping (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Clipping)

                                                        Module Optimise.Clipping

                                                        type typ = +Clipping (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Clipping)

                                                        Module Optimise.Clipping

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Clipping.typ =
                                                        1. | L2norm of float
                                                        2. | Value of float * float
                                                        3. | None
                                                        val run : typ -> Algodiff.t -> Algodiff.t
                                                        val default : typ -> typ
                                                        val to_string : typ -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Gradient/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Gradient/index.html index 4ed9d51e6..1e0ded9fe 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Gradient/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Gradient)

                                                        Module Optimise.Gradient

                                                        type typ = +Gradient (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Gradient)

                                                        Module Optimise.Gradient

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Gradient.typ =
                                                        1. | GD
                                                        2. | CG
                                                        3. | CD
                                                        4. | NonlinearCG
                                                        5. | DaiYuanCG
                                                        6. | NewtonCG
                                                        7. | Newton
                                                        val run : typ -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Learning_Rate/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Learning_Rate/index.html index a9bd92c44..6f9a351da 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Learning_Rate/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Learning_Rate)

                                                        Module Optimise.Learning_Rate

                                                        type typ = +Learning_Rate (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Learning_Rate)

                                                        Module Optimise.Learning_Rate

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Learning_Rate.typ =
                                                        1. | Adagrad of float
                                                        2. | Const of float
                                                        3. | Decay of float * float
                                                        4. | Exp_decay of float * float
                                                        5. | RMSprop of float * float
                                                        6. | Adam of float * float * float
                                                        7. | Schedule of float array
                                                        val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                        val default : typ -> typ
                                                        val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                        val to_string : typ -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Loss/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Loss/index.html index 8ccf64c9e..b19ca03cb 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Loss/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Loss)

                                                        Module Optimise.Loss

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Loss.typ =
                                                        1. | Hinge
                                                        2. | L1norm
                                                        3. | L2norm
                                                        4. | Quadratic
                                                        5. | Cross_entropy
                                                        6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                        val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                        val to_string : typ -> string
                                                        +Loss (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Loss)

                                                        Module Optimise.Loss

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Loss.typ =
                                                        1. | Hinge
                                                        2. | L1norm
                                                        3. | L2norm
                                                        4. | Quadratic
                                                        5. | Cross_entropy
                                                        6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                        val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                        val to_string : typ -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Momentum/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Momentum/index.html index bfc557bac..d29232ec2 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Momentum/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Momentum/index.html @@ -1,4 +1,4 @@ -Momentum (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Momentum)

                                                        Module Optimise.Momentum

                                                        type typ = +Momentum (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Momentum)

                                                        Module Optimise.Momentum

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Momentum.typ =
                                                        1. | Standard of float
                                                        2. | Nesterov of float
                                                        3. | None
                                                        val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                        val default : typ -> typ
                                                        val to_string : typ -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Params/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Params/index.html index 6a53e6129..7c1c19b43 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Params/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Params)

                                                        Module Optimise.Params

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Params.typ = +Params (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Params)

                                                        Module Optimise.Params

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Params.typ = {
                                                        1. mutable epochs : float;
                                                        2. mutable batch : Batch.typ;
                                                        3. mutable gradient : Gradient.typ;
                                                        4. mutable loss : Loss.typ;
                                                        5. mutable learning_rate : Learning_Rate.typ;
                                                        6. mutable regularisation : Regularisation.typ;
                                                        7. mutable momentum : Momentum.typ;
                                                        8. mutable clipping : Clipping.typ;
                                                        9. mutable stopping : Stopping.typ;
                                                        10. mutable checkpoint : Checkpoint.typ;
                                                        11. mutable verbosity : bool;
                                                        }
                                                        val default : unit -> typ
                                                        val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Regularisation/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Regularisation/index.html index 5a945a9a9..cfc3a48bf 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Regularisation/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Regularisation)

                                                        Module Optimise.Regularisation

                                                        type typ = +Regularisation (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Regularisation)

                                                        Module Optimise.Regularisation

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Regularisation.typ =
                                                        1. | L1norm of float
                                                        2. | L2norm of float
                                                        3. | Elastic_net of float * float
                                                        4. | None
                                                        val run : typ -> Algodiff.t -> Algodiff.t
                                                        val to_string : typ -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Stopping/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Stopping/index.html index 4c948ea1c..958b9d52f 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Stopping/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Stopping/index.html @@ -1,4 +1,4 @@ -Stopping (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Stopping)

                                                        Module Optimise.Stopping

                                                        type typ = +Stopping (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Stopping)

                                                        Module Optimise.Stopping

                                                        type typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Optimise.Stopping.typ =
                                                        1. | Const of float
                                                        2. | Early of int * int
                                                        3. | None
                                                        val run : typ -> float -> bool
                                                        val default : typ -> typ
                                                        val to_string : typ -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Utils/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Utils/index.html index e056b49d0..1b7f6e512 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Utils/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Utils)

                                                        Module Optimise.Utils

                                                        val sample_num : Algodiff.t -> int
                                                        val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                        val get_chunk : +Utils (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise.Utils)

                                                        Module Optimise.Utils

                                                        val sample_num : Algodiff.t -> int
                                                        val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                        val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/index.html index c51db26fa..fd04dbd76 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise)

                                                        Module Neuron.Optimise

                                                        module Algodiff : sig ... end
                                                        module Utils : sig ... end
                                                        module Learning_Rate : sig ... end
                                                        module Batch : sig ... end
                                                        module Loss : sig ... end
                                                        module Gradient : sig ... end
                                                        module Momentum : sig ... end
                                                        module Regularisation : sig ... end
                                                        module Clipping : sig ... end
                                                        module Stopping : sig ... end
                                                        module Checkpoint : sig ... end
                                                        module Params : sig ... end
                                                        val minimise_weight : +Optimise (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Optimise)

                                                        Module Neuron.Optimise

                                                        module Algodiff : sig ... end
                                                        module Utils : sig ... end
                                                        module Learning_Rate : sig ... end
                                                        module Batch : sig ... end
                                                        module Loss : sig ... end
                                                        module Gradient : sig ... end
                                                        module Momentum : sig ... end
                                                        module Regularisation : sig ... end
                                                        module Clipping : sig ... end
                                                        module Stopping : sig ... end
                                                        module Checkpoint : sig ... end
                                                        module Params : sig ... end
                                                        val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding1D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding1D/index.html index 8a23828cc..cae834545 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding1D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Padding1D)

                                                        Module Neuron.Padding1D

                                                        +Padding1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Padding1D)

                                                        Module Neuron.Padding1D

                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding2D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding2D/index.html index 4d7d9439e..3d850044a 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding2D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding2D/index.html @@ -1,4 +1,4 @@ -Padding2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Padding2D)

                                                        Module Neuron.Padding2D

                                                        type neuron_typ = +Padding2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Padding2D)

                                                        Module Neuron.Padding2D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Padding2D.neuron_typ = {
                                                        1. mutable padding : int array array;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : int array array -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding3D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding3D/index.html index 6bd421031..efd65a78a 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding3D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Padding3D)

                                                        Module Neuron.Padding3D

                                                        +Padding3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Padding3D)

                                                        Module Neuron.Padding3D

                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Recurrent/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Recurrent/index.html index 7a322e3c4..981fa3770 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Recurrent/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Recurrent)

                                                        Module Neuron.Recurrent

                                                        type neuron_typ = +Recurrent (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Recurrent)

                                                        Module Neuron.Recurrent

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Recurrent.neuron_typ = {
                                                        1. mutable whh : Optimise.Algodiff.t;
                                                        2. mutable wxh : Optimise.Algodiff.t;
                                                        3. mutable why : Optimise.Algodiff.t;
                                                        4. mutable bh : Optimise.Algodiff.t;
                                                        5. mutable by : Optimise.Algodiff.t;
                                                        6. mutable h : Optimise.Algodiff.t;
                                                        7. mutable hiddens : int;
                                                        8. mutable act : Activation.typ;
                                                        9. mutable init_typ : Init.typ;
                                                        10. mutable in_shape : int array;
                                                        11. mutable out_shape : int array;
                                                        }
                                                        val create : ?time_steps:int -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Reshape/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Reshape/index.html index 9bbe7aca2..ddfb6573f 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Reshape/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Reshape/index.html @@ -1,4 +1,4 @@ -Reshape (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Reshape)

                                                        Module Neuron.Reshape

                                                        type neuron_typ = +Reshape (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Reshape)

                                                        Module Neuron.Reshape

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Reshape.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> int array -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Slice/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Slice/index.html index f3ebf156c..b070db803 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Slice/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/Slice/index.html @@ -1,4 +1,4 @@ -Slice (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Slice)

                                                        Module Neuron.Slice

                                                        type neuron_typ = +Slice (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.Slice)

                                                        Module Neuron.Slice

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.Slice.neuron_typ = {
                                                        1. mutable in_shape : int array;
                                                        2. mutable out_shape : int array;
                                                        3. mutable slice : int list list;
                                                        }
                                                        val create : int list list -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv1D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv1D/index.html index 7fe7063fc..29a44d225 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv1D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.TransposeConv1D)

                                                        Module Neuron.TransposeConv1D

                                                        type neuron_typ = +TransposeConv1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.TransposeConv1D)

                                                        Module Neuron.TransposeConv1D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.TransposeConv1D.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable kernel : int array;
                                                        4. mutable stride : int array;
                                                        5. mutable padding : Owl_types.padding;
                                                        6. mutable init_typ : Init.typ;
                                                        7. mutable in_shape : int array;
                                                        8. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv2D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv2D/index.html index 1b539ae75..d4c9950f6 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv2D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.TransposeConv2D)

                                                        Module Neuron.TransposeConv2D

                                                        type neuron_typ = +TransposeConv2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.TransposeConv2D)

                                                        Module Neuron.TransposeConv2D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.TransposeConv2D.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable kernel : int array;
                                                        4. mutable stride : int array;
                                                        5. mutable padding : Owl_types.padding;
                                                        6. mutable init_typ : Init.typ;
                                                        7. mutable in_shape : int array;
                                                        8. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv3D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv3D/index.html index a72a2b1f1..d0c89026a 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv3D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.TransposeConv3D)

                                                        Module Neuron.TransposeConv3D

                                                        type neuron_typ = +TransposeConv3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.TransposeConv3D)

                                                        Module Neuron.TransposeConv3D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.TransposeConv3D.neuron_typ = {
                                                        1. mutable w : Optimise.Algodiff.t;
                                                        2. mutable b : Optimise.Algodiff.t;
                                                        3. mutable kernel : int array;
                                                        4. mutable stride : int array;
                                                        5. mutable padding : Owl_types.padding;
                                                        6. mutable init_typ : Init.typ;
                                                        7. mutable in_shape : int array;
                                                        8. mutable out_shape : int array;
                                                        }
                                                        val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling1D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling1D/index.html index 58c4ece46..8df67c7c1 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling1D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.UpSampling1D)

                                                        Module Neuron.UpSampling1D

                                                        +UpSampling1D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.UpSampling1D)

                                                        Module Neuron.UpSampling1D

                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling2D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling2D/index.html index 2efb86a08..973402b33 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling2D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling2D/index.html @@ -1,4 +1,4 @@ -UpSampling2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.UpSampling2D)

                                                        Module Neuron.UpSampling2D

                                                        type neuron_typ = +UpSampling2D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.UpSampling2D)

                                                        Module Neuron.UpSampling2D

                                                        type neuron_typ = Owl_neural_generic.Make_Embedded(Engine).Neuron.UpSampling2D.neuron_typ = {
                                                        1. mutable size : int array;
                                                        2. mutable in_shape : int array;
                                                        3. mutable out_shape : int array;
                                                        }
                                                        val create : int array -> neuron_typ
                                                        val connect : int array -> neuron_typ -> unit
                                                        val copy : neuron_typ -> neuron_typ
                                                        val to_string : neuron_typ -> string
                                                        val to_name : unit -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling3D/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling3D/index.html index c23ceb04d..07d2eab69 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling3D/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.UpSampling3D)

                                                        Module Neuron.UpSampling3D

                                                        +UpSampling3D (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron.UpSampling3D)

                                                        Module Neuron.UpSampling3D

                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/index.html index 21a2d1efb..ae4371eb7 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/Neuron/index.html @@ -1,2 +1,2 @@ -Neuron (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron)

                                                        Module Graph.Neuron

                                                        module Optimise : sig ... end
                                                        module Init : sig ... end
                                                        module Input : sig ... end
                                                        module Activation : sig ... end
                                                        module Linear : sig ... end
                                                        module LinearNoBias : sig ... end
                                                        module Recurrent : sig ... end
                                                        module LSTM : sig ... end
                                                        module GRU : sig ... end
                                                        module Conv1D : sig ... end
                                                        module Conv2D : sig ... end
                                                        module Conv3D : sig ... end
                                                        module DilatedConv1D : sig ... end
                                                        module DilatedConv2D : sig ... end
                                                        module DilatedConv3D : sig ... end
                                                        module TransposeConv1D : sig ... end
                                                        module TransposeConv2D : sig ... end
                                                        module TransposeConv3D : sig ... end
                                                        module FullyConnected : sig ... end
                                                        module MaxPool1D : sig ... end
                                                        module MaxPool2D : sig ... end
                                                        module AvgPool1D : sig ... end
                                                        module AvgPool2D : sig ... end
                                                        module GlobalMaxPool1D : sig ... end
                                                        module GlobalMaxPool2D : sig ... end
                                                        module GlobalAvgPool1D : sig ... end
                                                        module GlobalAvgPool2D : sig ... end
                                                        module UpSampling1D : sig ... end
                                                        module UpSampling2D : sig ... end
                                                        module UpSampling3D : sig ... end
                                                        module Padding1D : sig ... end
                                                        module Padding2D : sig ... end
                                                        module Padding3D : sig ... end
                                                        module Lambda : sig ... end
                                                        module LambdaArray : sig ... end
                                                        module Dropout : sig ... end
                                                        module Reshape : sig ... end
                                                        module Flatten : sig ... end
                                                        module Slice : sig ... end
                                                        module Add : sig ... end
                                                        module Mul : sig ... end
                                                        module Dot : sig ... end
                                                        module Max : sig ... end
                                                        module Average : sig ... end
                                                        module Concatenate : sig ... end
                                                        module Normalisation : sig ... end
                                                        module GaussianNoise : sig ... end
                                                        module GaussianDropout : sig ... end
                                                        module AlphaDropout : sig ... end
                                                        module Embedding : sig ... end
                                                        module Masking : sig ... end
                                                        type neuron = Owl_neural_generic.Make_Embedded(Engine).Neuron.neuron =
                                                        1. | Input of Input.neuron_typ
                                                        2. | Linear of Linear.neuron_typ
                                                        3. | LinearNoBias of LinearNoBias.neuron_typ
                                                        4. | Embedding of Embedding.neuron_typ
                                                        5. | LSTM of LSTM.neuron_typ
                                                        6. | GRU of GRU.neuron_typ
                                                        7. | Recurrent of Recurrent.neuron_typ
                                                        8. | Conv1D of Conv1D.neuron_typ
                                                        9. | Conv2D of Conv2D.neuron_typ
                                                        10. | Conv3D of Conv3D.neuron_typ
                                                        11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                        12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                        13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                        14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                        15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                        16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                        17. | FullyConnected of FullyConnected.neuron_typ
                                                        18. | MaxPool1D of MaxPool1D.neuron_typ
                                                        19. | MaxPool2D of MaxPool2D.neuron_typ
                                                        20. | AvgPool1D of AvgPool1D.neuron_typ
                                                        21. | AvgPool2D of AvgPool2D.neuron_typ
                                                        22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                        23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                        24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                        25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                        26. | UpSampling2D of UpSampling2D.neuron_typ
                                                        27. | Padding2D of Padding2D.neuron_typ
                                                        28. | Dropout of Dropout.neuron_typ
                                                        29. | Reshape of Reshape.neuron_typ
                                                        30. | Flatten of Flatten.neuron_typ
                                                        31. | Slice of Slice.neuron_typ
                                                        32. | Lambda of Lambda.neuron_typ
                                                        33. | LambdaArray of LambdaArray.neuron_typ
                                                        34. | Activation of Activation.neuron_typ
                                                        35. | GaussianNoise of GaussianNoise.neuron_typ
                                                        36. | GaussianDropout of GaussianDropout.neuron_typ
                                                        37. | AlphaDropout of AlphaDropout.neuron_typ
                                                        38. | Normalisation of Normalisation.neuron_typ
                                                        39. | Add of Add.neuron_typ
                                                        40. | Mul of Mul.neuron_typ
                                                        41. | Dot of Dot.neuron_typ
                                                        42. | Max of Max.neuron_typ
                                                        43. | Average of Average.neuron_typ
                                                        44. | Concatenate of Concatenate.neuron_typ
                                                        val get_in_out_shape : neuron -> int array * int array
                                                        val get_in_shape : neuron -> int array
                                                        val get_out_shape : neuron -> int array
                                                        val connect : int array array -> neuron -> unit
                                                        val init : neuron -> unit
                                                        val reset : neuron -> unit
                                                        val mktag : int -> neuron -> unit
                                                        val mkpar : neuron -> Optimise.Algodiff.t array
                                                        val mkpri : neuron -> Optimise.Algodiff.t array
                                                        val mkadj : neuron -> Optimise.Algodiff.t array
                                                        val update : neuron -> Optimise.Algodiff.t array -> unit
                                                        val load_weights : neuron -> Optimise.Algodiff.t array -> unit
                                                        val save_weights : neuron -> Optimise.Algodiff.t array
                                                        val copy : neuron -> neuron
                                                        val to_string : neuron -> string
                                                        val to_name : neuron -> string
                                                        +Neuron (owl-base.Owl_neural_compiler.Make.Neural.Graph.Neuron)

                                                        Module Graph.Neuron

                                                        module Optimise : sig ... end
                                                        module Init : sig ... end
                                                        module Input : sig ... end
                                                        module Activation : sig ... end
                                                        module Linear : sig ... end
                                                        module LinearNoBias : sig ... end
                                                        module Recurrent : sig ... end
                                                        module LSTM : sig ... end
                                                        module GRU : sig ... end
                                                        module Conv1D : sig ... end
                                                        module Conv2D : sig ... end
                                                        module Conv3D : sig ... end
                                                        module DilatedConv1D : sig ... end
                                                        module DilatedConv2D : sig ... end
                                                        module DilatedConv3D : sig ... end
                                                        module TransposeConv1D : sig ... end
                                                        module TransposeConv2D : sig ... end
                                                        module TransposeConv3D : sig ... end
                                                        module FullyConnected : sig ... end
                                                        module MaxPool1D : sig ... end
                                                        module MaxPool2D : sig ... end
                                                        module AvgPool1D : sig ... end
                                                        module AvgPool2D : sig ... end
                                                        module GlobalMaxPool1D : sig ... end
                                                        module GlobalMaxPool2D : sig ... end
                                                        module GlobalAvgPool1D : sig ... end
                                                        module GlobalAvgPool2D : sig ... end
                                                        module UpSampling1D : sig ... end
                                                        module UpSampling2D : sig ... end
                                                        module UpSampling3D : sig ... end
                                                        module Padding1D : sig ... end
                                                        module Padding2D : sig ... end
                                                        module Padding3D : sig ... end
                                                        module Lambda : sig ... end
                                                        module LambdaArray : sig ... end
                                                        module Dropout : sig ... end
                                                        module Reshape : sig ... end
                                                        module Flatten : sig ... end
                                                        module Slice : sig ... end
                                                        module Add : sig ... end
                                                        module Mul : sig ... end
                                                        module Dot : sig ... end
                                                        module Max : sig ... end
                                                        module Average : sig ... end
                                                        module Concatenate : sig ... end
                                                        module Normalisation : sig ... end
                                                        module GaussianNoise : sig ... end
                                                        module GaussianDropout : sig ... end
                                                        module AlphaDropout : sig ... end
                                                        module Embedding : sig ... end
                                                        module Masking : sig ... end
                                                        type neuron = Owl_neural_generic.Make_Embedded(Engine).Neuron.neuron =
                                                        1. | Input of Input.neuron_typ
                                                        2. | Linear of Linear.neuron_typ
                                                        3. | LinearNoBias of LinearNoBias.neuron_typ
                                                        4. | Embedding of Embedding.neuron_typ
                                                        5. | LSTM of LSTM.neuron_typ
                                                        6. | GRU of GRU.neuron_typ
                                                        7. | Recurrent of Recurrent.neuron_typ
                                                        8. | Conv1D of Conv1D.neuron_typ
                                                        9. | Conv2D of Conv2D.neuron_typ
                                                        10. | Conv3D of Conv3D.neuron_typ
                                                        11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                        12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                        13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                        14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                        15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                        16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                        17. | FullyConnected of FullyConnected.neuron_typ
                                                        18. | MaxPool1D of MaxPool1D.neuron_typ
                                                        19. | MaxPool2D of MaxPool2D.neuron_typ
                                                        20. | AvgPool1D of AvgPool1D.neuron_typ
                                                        21. | AvgPool2D of AvgPool2D.neuron_typ
                                                        22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                        23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                        24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                        25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                        26. | UpSampling2D of UpSampling2D.neuron_typ
                                                        27. | Padding2D of Padding2D.neuron_typ
                                                        28. | Dropout of Dropout.neuron_typ
                                                        29. | Reshape of Reshape.neuron_typ
                                                        30. | Flatten of Flatten.neuron_typ
                                                        31. | Slice of Slice.neuron_typ
                                                        32. | Lambda of Lambda.neuron_typ
                                                        33. | LambdaArray of LambdaArray.neuron_typ
                                                        34. | Activation of Activation.neuron_typ
                                                        35. | GaussianNoise of GaussianNoise.neuron_typ
                                                        36. | GaussianDropout of GaussianDropout.neuron_typ
                                                        37. | AlphaDropout of AlphaDropout.neuron_typ
                                                        38. | Normalisation of Normalisation.neuron_typ
                                                        39. | Add of Add.neuron_typ
                                                        40. | Mul of Mul.neuron_typ
                                                        41. | Dot of Dot.neuron_typ
                                                        42. | Max of Max.neuron_typ
                                                        43. | Average of Average.neuron_typ
                                                        44. | Concatenate of Concatenate.neuron_typ
                                                        val get_in_out_shape : neuron -> int array * int array
                                                        val get_in_shape : neuron -> int array
                                                        val get_out_shape : neuron -> int array
                                                        val connect : int array array -> neuron -> unit
                                                        val init : neuron -> unit
                                                        val reset : neuron -> unit
                                                        val mktag : int -> neuron -> unit
                                                        val mkpar : neuron -> Optimise.Algodiff.t array
                                                        val mkpri : neuron -> Optimise.Algodiff.t array
                                                        val mkadj : neuron -> Optimise.Algodiff.t array
                                                        val update : neuron -> Optimise.Algodiff.t array -> unit
                                                        val load_weights : neuron -> Optimise.Algodiff.t array -> unit
                                                        val save_weights : neuron -> Optimise.Algodiff.t array
                                                        val copy : neuron -> neuron
                                                        val to_string : neuron -> string
                                                        val to_name : neuron -> string
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/index.html index 690e70feb..1f7c34166 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_neural_compiler.Make.Neural.Graph)

                                                        Module Neural.Graph

                                                        module Neuron : sig ... end
                                                        type node = Owl_neural_generic.Make_Embedded(Engine).node = {
                                                        1. mutable name : string;
                                                        2. mutable prev : node array;
                                                        3. mutable next : node array;
                                                        4. mutable neuron : Neuron.neuron;
                                                        5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                        6. mutable network : network;
                                                        7. mutable train : bool;
                                                        }
                                                        and network = Owl_neural_generic.Make_Embedded(Engine).network = {
                                                        1. mutable nnid : string;
                                                        2. mutable size : int;
                                                        3. mutable roots : node array;
                                                        4. mutable outputs : node array;
                                                        5. mutable topo : node array;
                                                        }
                                                        val make_network : ?nnid:string -> int -> node array -> node array -> network
                                                        val make_node : +Graph (owl-base.Owl_neural_compiler.Make.Neural.Graph)

                                                        Module Neural.Graph

                                                        module Neuron : sig ... end
                                                        type node = Owl_neural_generic.Make_Embedded(Engine).node = {
                                                        1. mutable name : string;
                                                        2. mutable prev : node array;
                                                        3. mutable next : node array;
                                                        4. mutable neuron : Neuron.neuron;
                                                        5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                        6. mutable network : network;
                                                        7. mutable train : bool;
                                                        }
                                                        and network = Owl_neural_generic.Make_Embedded(Engine).network = {
                                                        1. mutable nnid : string;
                                                        2. mutable size : int;
                                                        3. mutable roots : node array;
                                                        4. mutable outputs : node array;
                                                        5. mutable topo : node array;
                                                        }
                                                        val make_network : ?nnid:string -> int -> node array -> node array -> network
                                                        val make_node : ?name:string -> ?train:bool -> node array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/Neural/index.html b/docs/owl-base/Owl_neural_compiler/Make/Neural/index.html index d65125cdf..0de78320e 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/Neural/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/Neural/index.html @@ -1,2 +1,2 @@ -Neural (owl-base.Owl_neural_compiler.Make.Neural)

                                                        Module Make.Neural

                                                        module Graph : sig ... end
                                                        module Optimise = Graph.Neuron.Optimise
                                                        module Init = Graph.Neuron.Init
                                                        module Activation = Graph.Neuron.Activation
                                                        module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                        +Neural (owl-base.Owl_neural_compiler.Make.Neural)

                                                        Module Make.Neural

                                                        module Graph : sig ... end
                                                        module Optimise = Graph.Neuron.Optimise
                                                        module Init = Graph.Neuron.Init
                                                        module Activation = Graph.Neuron.Activation
                                                        module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Linalg/index.html index aefb30a8a..dd6cd31a6 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Linalg)

                                                        Module Operator.Linalg

                                                        val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                        TODO

                                                        val svd : +Linalg (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Linalg)

                                                        Module Operator.Linalg

                                                        inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

                                                        logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

                                                        val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                        chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

                                                        • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

                                                        qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

                                                        lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

                                                        svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

                                                        • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
                                                        val lyapunov : + Symbol.Shape.Type.arr

                                                        sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

                                                        val discrete_lyapunov : + Symbol.Shape.Type.arr

                                                        lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

                                                        val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                        TODO

                                                        val linsolve : + Symbol.Shape.Type.arr

                                                        discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

                                                        • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
                                                        val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                        TODO

                                                        linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

                                                        • trans specifies whether to transpose the matrix A.
                                                        • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

                                                        care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

                                                        • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                                        + Symbol.Shape.Type.arr

                                                        dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

                                                        • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Mat/index.html index a8f947b2b..86d132a9a 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Mat)

                                                        Module Operator.Mat

                                                        val eye : int -> Symbol.Shape.Type.arr

                                                        TODO

                                                        TODO

                                                        TODO

                                                        TODO

                                                        +Mat (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Mat)

                                                        Module Operator.Mat

                                                        val eye : int -> Symbol.Shape.Type.arr

                                                        eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

                                                        diagm ?k v creates a diagonal matrix from the array v.

                                                        • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

                                                        triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

                                                        tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Scalar/index.html index c3ae19351..90672a552 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Scalar)

                                                        Module Operator.Scalar

                                                        val add : +Scalar (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Scalar)

                                                        Module Operator.Scalar

                                                        add a b returns the sum of the scalars a and b.

                                                        sub a b returns the difference of the scalars a and b.

                                                        mul a b returns the product of the scalars a and b.

                                                        div a b returns the quotient of the scalars a and b.

                                                        val atan2 : + Symbol.Shape.Type.elt

                                                        pow a b returns the scalar a raised to the power of b.

                                                        + Symbol.Shape.Type.elt

                                                        atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                                                        abs a returns the absolute value of the scalar a.

                                                        neg a returns the negation of the scalar a.

                                                        sqr a returns the square of the scalar a.

                                                        sqrt a returns the square root of the scalar a.

                                                        exp a returns the exponential of the scalar a.

                                                        log a returns the natural logarithm of the scalar a.

                                                        log2 a returns the base-2 logarithm of the scalar a.

                                                        log10 a returns the base-10 logarithm of the scalar a.

                                                        signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                                                        floor a returns the greatest integer less than or equal to the scalar a.

                                                        ceil a returns the smallest integer greater than or equal to the scalar a.

                                                        round a returns the nearest integer to the scalar a.

                                                        sin a returns the sine of the scalar a.

                                                        cos a returns the cosine of the scalar a.

                                                        tan a returns the tangent of the scalar a.

                                                        sinh a returns the hyperbolic sine of the scalar a.

                                                        cosh a returns the hyperbolic cosine of the scalar a.

                                                        tanh a returns the hyperbolic tangent of the scalar a.

                                                        asin a returns the arcsine of the scalar a.

                                                        acos a returns the arccosine of the scalar a.

                                                        atan a returns the arctangent of the scalar a.

                                                        asinh a returns the inverse hyperbolic sine of the scalar a.

                                                        acosh a returns the inverse hyperbolic cosine of the scalar a.

                                                        atanh a returns the inverse hyperbolic tangent of the scalar a.

                                                        relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                                                        dawsn a returns Dawson's function of the scalar a.

                                                        sigmoid a returns the sigmoid function of the scalar a.

                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index b0370e60f..085fb2961 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                                        Module A.Linalg

                                                        val inv : arr -> arr
                                                        val logdet : arr -> elt
                                                        val chol : ?upper:bool -> arr -> arr
                                                        val svd : ?thin:bool -> arr -> arr * arr * arr
                                                        val qr : arr -> arr * arr
                                                        val lq : arr -> arr * arr
                                                        val sylvester : arr -> arr -> arr -> arr
                                                        val lyapunov : arr -> arr -> arr
                                                        val discrete_lyapunov : +Linalg (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                                        Module A.Linalg

                                                        val inv : arr -> arr
                                                        val logdet : arr -> elt
                                                        val chol : ?upper:bool -> arr -> arr
                                                        val svd : ?thin:bool -> arr -> arr * arr * arr
                                                        val qr : arr -> arr * arr
                                                        val lq : arr -> arr * arr
                                                        val sylvester : arr -> arr -> arr -> arr
                                                        val lyapunov : arr -> arr -> arr
                                                        val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index dd897959c..6e860ec2e 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                                                        Module A.Mat

                                                        val diagm : ?k:int -> arr -> arr
                                                        val triu : ?k:int -> arr -> arr
                                                        val tril : ?k:int -> arr -> arr
                                                        val eye : int -> arr
                                                        +Mat (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                                                        Module A.Mat

                                                        val diagm : ?k:int -> arr -> arr
                                                        val triu : ?k:int -> arr -> arr
                                                        val tril : ?k:int -> arr -> arr
                                                        val eye : int -> arr
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index 0acdd19f3..3228fe114 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                                        Module A.Scalar

                                                        val add : elt -> elt -> elt
                                                        val sub : elt -> elt -> elt
                                                        val mul : elt -> elt -> elt
                                                        val div : elt -> elt -> elt
                                                        val pow : elt -> elt -> elt
                                                        val atan2 : elt -> elt -> elt
                                                        val abs : elt -> elt
                                                        val neg : elt -> elt
                                                        val sqr : elt -> elt
                                                        val sqrt : elt -> elt
                                                        val exp : elt -> elt
                                                        val log : elt -> elt
                                                        val log2 : elt -> elt
                                                        val log10 : elt -> elt
                                                        val signum : elt -> elt
                                                        val floor : elt -> elt
                                                        val ceil : elt -> elt
                                                        val round : elt -> elt
                                                        val sin : elt -> elt
                                                        val cos : elt -> elt
                                                        val tan : elt -> elt
                                                        val sinh : elt -> elt
                                                        val cosh : elt -> elt
                                                        val tanh : elt -> elt
                                                        val asin : elt -> elt
                                                        val acos : elt -> elt
                                                        val atan : elt -> elt
                                                        val asinh : elt -> elt
                                                        val acosh : elt -> elt
                                                        val atanh : elt -> elt
                                                        val relu : elt -> elt
                                                        val dawsn : elt -> elt
                                                        val sigmoid : elt -> elt
                                                        +Scalar (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                                        Module A.Scalar

                                                        val add : elt -> elt -> elt
                                                        val sub : elt -> elt -> elt
                                                        val mul : elt -> elt -> elt
                                                        val div : elt -> elt -> elt
                                                        val pow : elt -> elt -> elt
                                                        val atan2 : elt -> elt -> elt
                                                        val abs : elt -> elt
                                                        val neg : elt -> elt
                                                        val sqr : elt -> elt
                                                        val sqrt : elt -> elt
                                                        val exp : elt -> elt
                                                        val log : elt -> elt
                                                        val log2 : elt -> elt
                                                        val log10 : elt -> elt
                                                        val signum : elt -> elt
                                                        val floor : elt -> elt
                                                        val ceil : elt -> elt
                                                        val round : elt -> elt
                                                        val sin : elt -> elt
                                                        val cos : elt -> elt
                                                        val tan : elt -> elt
                                                        val sinh : elt -> elt
                                                        val cosh : elt -> elt
                                                        val tanh : elt -> elt
                                                        val asin : elt -> elt
                                                        val acos : elt -> elt
                                                        val atan : elt -> elt
                                                        val asinh : elt -> elt
                                                        val acosh : elt -> elt
                                                        val atanh : elt -> elt
                                                        val relu : elt -> elt
                                                        val dawsn : elt -> elt
                                                        val sigmoid : elt -> elt
                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index 94999b2e1..33daf8913 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                                                        Module Device.A

                                                        include Owl_types_ndarray_algodiff.Sig
                                                        include Owl_types_ndarray_eltcmp.Sig
                                                        include Owl_types_ndarray_basic.Sig
                                                        type arr
                                                        type elt
                                                        val empty : int array -> arr
                                                        val zeros : int array -> arr
                                                        val ones : int array -> arr
                                                        val create : int array -> elt -> arr
                                                        val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                        val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                        val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                        val bernoulli : ?p:elt -> int array -> arr
                                                        val init : int array -> (int -> elt) -> arr
                                                        val init_nd : int array -> (int array -> elt) -> arr
                                                        val shape : arr -> int array
                                                        val numel : arr -> int
                                                        val get : arr -> int array -> elt
                                                        val set : arr -> int array -> elt -> unit
                                                        val get_slice : int list list -> arr -> arr
                                                        val set_slice : int list list -> arr -> arr -> unit
                                                        val get_fancy : Owl_types_common.index list -> arr -> arr
                                                        val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                        val copy : arr -> arr
                                                        val copy_ : out:arr -> arr -> unit
                                                        val reset : arr -> unit
                                                        val reshape : arr -> int array -> arr
                                                        val reverse : arr -> arr
                                                        val tile : arr -> int array -> arr
                                                        val repeat : arr -> int array -> arr
                                                        val concatenate : ?axis:int -> arr array -> arr
                                                        val stack : ?axis:int -> arr array -> arr
                                                        val split : ?axis:int -> int array -> arr -> arr array
                                                        val expand : ?hi:bool -> arr -> int -> arr
                                                        val squeeze : ?axis:int array -> arr -> arr
                                                        val draw : ?axis:int -> arr -> int -> arr * int array
                                                        val map : (elt -> elt) -> arr -> arr
                                                        val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                        val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                        val one_hot : int -> arr -> arr
                                                        val pad : ?v:elt -> int list list -> arr -> arr
                                                        val print : +A (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                                                        Module Device.A

                                                        include Owl_types_ndarray_algodiff.Sig
                                                        include Owl_types_ndarray_eltcmp.Sig
                                                        include Owl_types_ndarray_basic.Sig
                                                        type arr
                                                        type elt
                                                        val empty : int array -> arr
                                                        val zeros : int array -> arr
                                                        val ones : int array -> arr
                                                        val create : int array -> elt -> arr
                                                        val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                        val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                        val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                        val bernoulli : ?p:elt -> int array -> arr
                                                        val init : int array -> (int -> elt) -> arr
                                                        val init_nd : int array -> (int array -> elt) -> arr
                                                        val shape : arr -> int array
                                                        val numel : arr -> int
                                                        val get : arr -> int array -> elt
                                                        val set : arr -> int array -> elt -> unit
                                                        val get_slice : int list list -> arr -> arr
                                                        val set_slice : int list list -> arr -> arr -> unit
                                                        val get_fancy : Owl_types_common.index list -> arr -> arr
                                                        val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                        val copy : arr -> arr
                                                        val copy_ : out:arr -> arr -> unit
                                                        val reset : arr -> unit
                                                        val reshape : arr -> int array -> arr
                                                        val reverse : arr -> arr
                                                        val tile : arr -> int array -> arr
                                                        val repeat : arr -> int array -> arr
                                                        val concatenate : ?axis:int -> arr array -> arr
                                                        val stack : ?axis:int -> arr array -> arr
                                                        val split : ?axis:int -> int array -> arr -> arr array
                                                        val expand : ?hi:bool -> arr -> int -> arr
                                                        val squeeze : ?axis:int array -> arr -> arr
                                                        val draw : ?axis:int -> arr -> int -> arr * int array
                                                        val map : (elt -> elt) -> arr -> arr
                                                        val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                        val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                        val one_hot : int -> arr -> arr
                                                        val pad : ?v:elt -> int list list -> arr -> arr
                                                        val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index dd364115a..411d00aad 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

                                                        Module Type.Device

                                                        Type definition
                                                        type device

                                                        TODO

                                                        type value

                                                        TODO

                                                        Core functions
                                                        val make_device : unit -> device

                                                        TODO

                                                        val arr_to_value : A.arr -> value

                                                        TODO

                                                        val value_to_arr : value -> A.arr

                                                        TODO

                                                        val elt_to_value : A.elt -> value

                                                        TODO

                                                        val value_to_elt : value -> A.elt

                                                        TODO

                                                        val value_to_float : value -> float

                                                        TODO

                                                        val is_arr : value -> bool

                                                        TODO

                                                        val is_elt : value -> bool

                                                        TODO

                                                        +Device (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

                                                        Module Type.Device

                                                        Type definition
                                                        type device

                                                        TODO

                                                        type value

                                                        TODO

                                                        Core functions
                                                        val make_device : unit -> device

                                                        TODO

                                                        val arr_to_value : A.arr -> value

                                                        TODO

                                                        val value_to_arr : value -> A.arr

                                                        TODO

                                                        val elt_to_value : A.elt -> value

                                                        TODO

                                                        val value_to_elt : value -> A.elt

                                                        TODO

                                                        val value_to_float : value -> float

                                                        TODO

                                                        val is_arr : value -> bool

                                                        TODO

                                                        val is_elt : value -> bool

                                                        TODO

                                                        diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index fd80e3e0a..65d9934b8 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type)

                                                        Module Shape.Type

                                                        Type definition
                                                        type state =
                                                        1. | Valid
                                                        2. | Invalid
                                                          (*

                                                          TODO

                                                          *)

                                                        TODO

                                                        and block = {
                                                        1. size : int;
                                                        2. block_id : int;
                                                        3. mutable active : t option;
                                                        4. mutable memory : Device.value;
                                                        5. mutable nodes : t list;
                                                        }

                                                        block type keeps a reference to a block of memory and to the nodes sharing that block.

                                                        and attr = {
                                                        1. mutable op : op;
                                                        2. mutable freeze : bool;
                                                        3. mutable reuse : bool;
                                                        4. mutable state : state;
                                                        5. mutable shape : int array option array;
                                                        6. mutable value : Device.value array;
                                                        7. mutable block : block array option;
                                                        }

                                                        TODO

                                                        and arr =
                                                        1. | Arr of t
                                                        and elt =
                                                        1. | Elt of t
                                                        and op =
                                                        1. | Noop
                                                        2. | Var
                                                        3. | Const
                                                        4. | Empty of int array
                                                        5. | Zeros of int array
                                                        6. | Ones of int array
                                                        7. | Create of int array
                                                        8. | Sequential of int array
                                                        9. | Uniform of int array
                                                        10. | Gaussian of int array
                                                        11. | Bernoulli of int array
                                                        12. | Init of int array * int -> elt
                                                        13. | Get of int array
                                                        14. | Set of int array
                                                        15. | GetSlice of int list list
                                                        16. | SetSlice of int list list
                                                        17. | GetFancy of Owl_types_common.index list
                                                        18. | SetFancy of Owl_types_common.index list
                                                        19. | Copy
                                                        20. | Reset
                                                        21. | Reshape of int array
                                                        22. | Reverse
                                                        23. | Tile of int array
                                                        24. | Repeat of int array
                                                        25. | Pad of elt * int list list
                                                        26. | Concatenate of int
                                                        27. | Stack of int
                                                        28. | Split of int * int array
                                                        29. | Draw of int * int
                                                        30. | Map of elt -> elt
                                                        31. | Fold of int * elt -> elt -> elt
                                                        32. | Scan of int * elt -> elt -> elt
                                                        33. | OneHot of int
                                                        34. | OfArray of int array
                                                        35. | Delay of Device.A.arr -> Device.A.arr
                                                        36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                        37. | LazyPrint of int option +Type (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape.Type)

                                                          Module Shape.Type

                                                          Type definition
                                                          type state =
                                                          1. | Valid
                                                          2. | Invalid
                                                            (*

                                                            TODO

                                                            *)

                                                          TODO

                                                          and block = {
                                                          1. size : int;
                                                          2. block_id : int;
                                                          3. mutable active : t option;
                                                          4. mutable memory : Device.value;
                                                          5. mutable nodes : t list;
                                                          }

                                                          block type keeps a reference to a block of memory and to the nodes sharing that block.

                                                          and attr = {
                                                          1. mutable op : op;
                                                          2. mutable freeze : bool;
                                                          3. mutable reuse : bool;
                                                          4. mutable state : state;
                                                          5. mutable shape : int array option array;
                                                          6. mutable value : Device.value array;
                                                          7. mutable block : block array option;
                                                          }

                                                          TODO

                                                          and arr =
                                                          1. | Arr of t
                                                          and elt =
                                                          1. | Elt of t
                                                          and op =
                                                          1. | Noop
                                                          2. | Var
                                                          3. | Const
                                                          4. | Empty of int array
                                                          5. | Zeros of int array
                                                          6. | Ones of int array
                                                          7. | Create of int array
                                                          8. | Sequential of int array
                                                          9. | Uniform of int array
                                                          10. | Gaussian of int array
                                                          11. | Bernoulli of int array
                                                          12. | Init of int array * int -> elt
                                                          13. | Get of int array
                                                          14. | Set of int array
                                                          15. | GetSlice of int list list
                                                          16. | SetSlice of int list list
                                                          17. | GetFancy of Owl_types_common.index list
                                                          18. | SetFancy of Owl_types_common.index list
                                                          19. | Copy
                                                          20. | Reset
                                                          21. | Reshape of int array
                                                          22. | Reverse
                                                          23. | Tile of int array
                                                          24. | Repeat of int array
                                                          25. | Pad of elt * int list list
                                                          26. | Concatenate of int
                                                          27. | Stack of int
                                                          28. | Split of int * int array
                                                          29. | Draw of int * int
                                                          30. | Map of elt -> elt
                                                          31. | Fold of int * elt -> elt -> elt
                                                          32. | Scan of int * elt -> elt -> elt
                                                          33. | OneHot of int
                                                          34. | OfArray of int array
                                                          35. | Delay of Device.A.arr -> Device.A.arr
                                                          36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                          37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                                          38. | Abs
                                                          39. | Neg
                                                          40. | Floor
                                                          41. | Ceil
                                                          42. | Round
                                                          43. | Sqr
                                                          44. | Sqrt
                                                          45. | Log
                                                          46. | Log2
                                                          47. | Log10
                                                          48. | Exp
                                                          49. | Sin
                                                          50. | Cos
                                                          51. | Tan
                                                          52. | Sinh
                                                          53. | Cosh
                                                          54. | Tanh
                                                          55. | Asin
                                                          56. | Acos
                                                          57. | Atan
                                                          58. | Asinh
                                                          59. | Acosh
                                                          60. | Atanh
                                                          61. | Min of bool * int
                                                          62. | Max of bool * int
                                                          63. | Sum of bool * int
                                                          64. | SumReduce of int array
                                                          65. | Signum
                                                          66. | Sigmoid
                                                          67. | Relu
                                                          68. | Dawsn
                                                          69. | Min'
                                                          70. | Max'
                                                          71. | Sum'
                                                          72. | LogSumExp'
                                                          73. | LogSumExp of bool * int
                                                          74. | L1norm'
                                                          75. | L2norm'
                                                          76. | L2NormSqr'
                                                          77. | ClipByValue
                                                          78. | ClipByL2norm
                                                          79. | Pow
                                                          80. | ScalarPow
                                                          81. | PowScalar
                                                          82. | Atan2
                                                          83. | ScalarAtan2
                                                          84. | Atan2Scalar
                                                          85. | Hypot
                                                          86. | Min2
                                                          87. | Max2
                                                          88. | Add
                                                          89. | Sub
                                                          90. | Mul
                                                          91. | Div
                                                          92. | AddScalar
                                                          93. | SubScalar
                                                          94. | MulScalar
                                                          95. | DivScalar
                                                          96. | ScalarAdd
                                                          97. | ScalarSub
                                                          98. | ScalarMul
                                                          99. | ScalarDiv
                                                          100. | FMA
                                                          101. | EltEqual
                                                          102. | EltNotEqual
                                                          103. | EltLess
                                                          104. | EltGreater
                                                          105. | EltLessEqual
                                                          106. | EltGreaterEqual
                                                          107. | EltEqualScalar
                                                          108. | EltNotEqualScalar
                                                          109. | EltLessScalar
                                                          110. | EltGreaterScalar
                                                          111. | EltLessEqualScalar
                                                          112. | EltGreaterEqualScalar
                                                          113. | Conv1d of Owl_types_common.padding * int array
                                                          114. | Conv2d of Owl_types_common.padding * int array
                                                          115. | Conv3d of Owl_types_common.padding * int array
                                                          116. | TransposeConv1d of Owl_types_common.padding * int array
                                                          117. | TransposeConv2d of Owl_types_common.padding * int array
                                                          118. | TransposeConv3d of Owl_types_common.padding * int array
                                                          119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                                          120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                                          121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                                          122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                                          123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                                          124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                                          125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                                          126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                                          127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                                          128. | UpSampling2d of int array
                                                          129. | Conv1dBackwardInput of int array
                                                          130. | Conv1dBackwardKernel of int array
                                                          131. | Conv2dBackwardInput of int array
                                                          132. | Conv2dBackwardKernel of int array
                                                          133. | Conv3dBackwardInput of int array
                                                          134. | Conv3dBackwardKernel of int array
                                                          135. | TransposeConv1dBackwardInput of int array
                                                          136. | TransposeConv1dBackwardKernel of int array
                                                          137. | TransposeConv2dBackwardInput of int array
                                                          138. | TransposeConv2dBackwardKernel of int array
                                                          139. | TransposeConv3dBackwardInput of int array
                                                          140. | TransposeConv3dBackwardKernel of int array
                                                          141. | DilatedConv1dBackwardInput of int array * int array
                                                          142. | DilatedConv1dBackwardKernel of int array * int array
                                                          143. | DilatedConv2dBackwardInput of int array * int array
                                                          144. | DilatedConv2dBackwardKernel of int array * int array
                                                          145. | DilatedConv3dBackwardInput of int array * int array
                                                          146. | DilatedConv3dBackwardKernel of int array * int array
                                                          147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                                          148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                                          149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                                          150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                                          151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                                          152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                                          153. | UpSampling2dBackward of int array
                                                          154. | RowNum
                                                          155. | ColNum
                                                          156. | Row
                                                          157. | Rows of int array
                                                          158. | CopyRowTo
                                                          159. | CopyColTo
                                                          160. | Dot of bool * bool * elt * elt
                                                          161. | Inv
                                                          162. | Trace
                                                          163. | Transpose of int array
                                                          164. | ToRows
                                                          165. | OfRows
                                                          166. | Scalar_Add
                                                          167. | Scalar_Sub
                                                          168. | Scalar_Mul
                                                          169. | Scalar_Div
                                                          170. | Scalar_Pow
                                                          171. | Scalar_Atan2
                                                          172. | Scalar_Abs
                                                          173. | Scalar_Neg
                                                          174. | Scalar_Sqr
                                                          175. | Scalar_Sqrt
                                                          176. | Scalar_Exp
                                                          177. | Scalar_Log
                                                          178. | Scalar_Log2
                                                          179. | Scalar_Log10
                                                          180. | Scalar_Signum
                                                          181. | Scalar_Floor
                                                          182. | Scalar_Ceil
                                                          183. | Scalar_Round
                                                          184. | Scalar_Sin
                                                          185. | Scalar_Cos
                                                          186. | Scalar_Tan
                                                          187. | Scalar_Sinh
                                                          188. | Scalar_Cosh
                                                          189. | Scalar_Tanh
                                                          190. | Scalar_Asin
                                                          191. | Scalar_Acos
                                                          192. | Scalar_Atan
                                                          193. | Scalar_Asinh
                                                          194. | Scalar_Acosh
                                                          195. | Scalar_Atanh
                                                          196. | Scalar_Relu
                                                          197. | Scalar_Dawsn
                                                          198. | Scalar_Sigmoid
                                                          199. | Fused_Adagrad of float * float
                                                            (*

                                                            TODO

                                                            *)
                                                          diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/index.html index 2caabb3ea..35a02d96e 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape)

                                                          Module Symbol.Shape

                                                          Core functions
                                                          val infer_shape : +Shape (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol.Shape)

                                                          Module Symbol.Shape

                                                          Core functions
                                                          val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                                          TODO

                                                          diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/index.html index 221979b8a..80338cd9b 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol)

                                                          Module Operator.Symbol

                                                          Core functions
                                                          val op_to_str : Shape.Type.op -> string

                                                          TODO

                                                          val is_random_variable : Shape.Type.op -> bool

                                                          TODO

                                                          val refnum : 'a Owl_graph.node -> int

                                                          TODO

                                                          val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                                          TODO

                                                          val node_numel : Shape.Type.attr Owl_graph.node -> int

                                                          TODO

                                                          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                                          TODO

                                                          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                                          TODO

                                                          val shape_to_str : int array option array -> string

                                                          TODO

                                                          val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                                          TODO

                                                          val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                                          TODO

                                                          val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                                          TODO

                                                          val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                                          TODO

                                                          val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                                          TODO

                                                          val make_node : +Symbol (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator.Symbol)

                                                          Module Operator.Symbol

                                                          Core functions
                                                          val op_to_str : Shape.Type.op -> string

                                                          TODO

                                                          val is_random_variable : Shape.Type.op -> bool

                                                          TODO

                                                          val refnum : 'a Owl_graph.node -> int

                                                          TODO

                                                          val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                                          TODO

                                                          val node_numel : Shape.Type.attr Owl_graph.node -> int

                                                          TODO

                                                          val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                                          TODO

                                                          val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                                          TODO

                                                          val shape_to_str : int array option array -> string

                                                          TODO

                                                          val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                                          TODO

                                                          val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                                          TODO

                                                          val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                                          TODO

                                                          val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                                          TODO

                                                          val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                                          TODO

                                                          val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/index.html index ca1cbf954..08e9c5393 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator)

                                                          Module Optimiser.Operator

                                                          Vectorised functions
                                                          val empty : int array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val zeros : int array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val ones : int array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val sequential : +Operator (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser.Operator)

                                                          Module Optimiser.Operator

                                                          Vectorised functions

                                                          noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                                                          val empty : int array -> Symbol.Shape.Type.arr

                                                          empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                                                          val zeros : int array -> Symbol.Shape.Type.arr

                                                          zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                                                          val ones : int array -> Symbol.Shape.Type.arr

                                                          ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                                                          val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                                          create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                                                          val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val uniform : + Symbol.Shape.Type.arr

                                                          sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                                                          val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val gaussian : + Symbol.Shape.Type.arr

                                                          uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                                                          val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val init_nd : + Symbol.Shape.Type.arr

                                                          gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                                                          val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                                          bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                                                          val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                                          init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                                                          val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val shape : Symbol.Shape.Type.arr -> int array

                                                          TODO

                                                          val numel : Symbol.Shape.Type.arr -> int

                                                          TODO

                                                          TODO

                                                          val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                                          TODO

                                                          val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val set_slice : + Symbol.Shape.Type.arr

                                                          init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                                                          val shape : Symbol.Shape.Type.arr -> int array

                                                          shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                                                          val numel : Symbol.Shape.Type.arr -> int

                                                          numel arr returns the total number of elements in the array arr.

                                                          get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                                                          val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                                          set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                                                          val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                          get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                                                          val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                                          TODO

                                                          val get_fancy : + unit

                                                          set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                                                          val set_fancy : + Symbol.Shape.Type.arr

                                                          get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                                                          val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                                          TODO

                                                          val copy_ : out:'a -> 'b -> 'c

                                                          TODO

                                                          val reset : Symbol.Shape.Type.arr -> unit

                                                          TODO

                                                          val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val pad : + unit

                                                          set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                                                          copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                                                          val copy_ : out:'a -> 'b -> 'c

                                                          copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                                                          val reset : Symbol.Shape.Type.arr -> unit

                                                          reset arr sets all elements of the array arr to zero.

                                                          val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                                                          reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                                                          val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                                                          val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                                                          TODO

                                                          val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val concatenate : + Symbol.Shape.Type.arr

                                                          pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                                                          val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                                          expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                                                          val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                          squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                                                          val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val concat : + Symbol.Shape.Type.arr

                                                          concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                                                          val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                                          stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                                                          val split : ?axis:int -> 'a -> 'b -> 'c

                                                          TODO

                                                          concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                                                          val split : ?axis:int -> 'a -> 'b -> 'c

                                                          split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                                                          • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                                                          val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                                                          TODO

                                                          val map : + Symbol.Shape.Type.arr * 'a array

                                                          draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                                                          map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                                                          fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                                                          TODO

                                                          val delay : + Symbol.Shape.Type.arr

                                                          scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                                                          one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                                                          delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                                                          val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                                          val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                                          TODO

                                                          lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                                          val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                                          print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                                                          • max_row is an optional parameter specifying the maximum number of rows to print.
                                                          • max_col is an optional parameter specifying the maximum number of columns to print.
                                                          • header is an optional parameter to include a header in the output.
                                                          • fmt is an optional parameter to specify the format of the output.

                                                          abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                                                          neg arr negates each element in the array arr. Returns a new array with each element negated.

                                                          floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                                                          ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                                                          round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                                                          sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                                                          sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                                                          log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                                                          log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                                                          log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                                                          exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                                                          sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                                                          cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                                                          tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                                                          sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                                                          cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                                                          tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                                                          asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                                                          acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                                                          atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                                                          asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                                                          acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                                                          atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                                                          val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                                                          • axis specifies the axis along which to compute the minimum.
                                                          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                                                          val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                                                          • axis specifies the axis along which to compute the maximum.
                                                          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                                                          val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val sum_reduce : + Symbol.Shape.Type.arr

                                                          sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                                                          • axis specifies the axis along which to compute the sum.
                                                          • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                                                          val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val log_sum_exp : + Symbol.Shape.Type.arr

                                                          sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                                                          • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                                                          signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                                                          sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                                                          relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                                                          dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                                                          min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                                                          max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                                                          sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                                                          log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val clip_by_value : + Symbol.Shape.Type.arr

                                                          log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                                                          • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                                                          • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                                                          l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                                                          l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                                                          l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                                                          val clip_by_l2norm : + Symbol.Shape.Type.arr

                                                          clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                                                          • amin specifies the minimum value to clip to.
                                                          • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                                                          clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                                                          val scalar_pow : + Symbol.Shape.Type.arr

                                                          pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                                                          val pow_scalar : + Symbol.Shape.Type.arr

                                                          scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                                                          val atan2 : + Symbol.Shape.Type.arr

                                                          pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                                                          val scalar_atan2 : + Symbol.Shape.Type.arr

                                                          atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                                                          val atan2_scalar : + Symbol.Shape.Type.arr

                                                          scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                                                          val hypot : + Symbol.Shape.Type.arr

                                                          atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                                                          hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                                                          min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                                                          max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                                                          add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                                                          sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                                                          mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                                                          val add_scalar : + Symbol.Shape.Type.arr

                                                          div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                                                          val sub_scalar : + Symbol.Shape.Type.arr

                                                          add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                                          val mul_scalar : + Symbol.Shape.Type.arr

                                                          sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                                                          val div_scalar : + Symbol.Shape.Type.arr

                                                          mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                                          val scalar_add : + Symbol.Shape.Type.arr

                                                          div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                                          val scalar_sub : + Symbol.Shape.Type.arr

                                                          scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                                          val scalar_mul : + Symbol.Shape.Type.arr

                                                          scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                                                          val scalar_div : + Symbol.Shape.Type.arr

                                                          scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                                          scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                                                          val elt_equal : + Symbol.Shape.Type.arr

                                                          fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                                                          val elt_not_equal : + Symbol.Shape.Type.arr

                                                          elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                                                          val elt_less : + Symbol.Shape.Type.arr

                                                          elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                                                          val elt_greater : + Symbol.Shape.Type.arr

                                                          elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                                                          val elt_less_equal : + Symbol.Shape.Type.arr

                                                          elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                                                          val elt_greater_equal : + Symbol.Shape.Type.arr

                                                          elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                                                          val elt_equal_scalar : + Symbol.Shape.Type.arr

                                                          elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                                                          val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                                                          elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                                                          val elt_less_scalar : + Symbol.Shape.Type.arr

                                                          elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                                                          val elt_greater_scalar : + Symbol.Shape.Type.arr

                                                          elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                                                          val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                                                          elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                                                          TODO

                                                          val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                                                          elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                                                          TODO

                                                          val conv1d : + Symbol.Shape.Type.arr

                                                          elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                                                          val conv2d : + Symbol.Shape.Type.arr

                                                          conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • strides specifies the stride length. Returns a new array with the result of the convolution.
                                                          val conv3d : + Symbol.Shape.Type.arr

                                                          conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • strides specifies the stride length. Returns a new array with the result of the convolution.
                                                          val transpose_conv1d : + Symbol.Shape.Type.arr

                                                          conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • strides specifies the stride length. Returns a new array with the result of the convolution.
                                                          val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val transpose_conv2d : + Symbol.Shape.Type.arr

                                                          transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                                          val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val transpose_conv3d : + Symbol.Shape.Type.arr

                                                          transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                                          val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val dilated_conv1d : + Symbol.Shape.Type.arr

                                                          transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                                          val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val dilated_conv2d : + Symbol.Shape.Type.arr

                                                          dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • strides specifies the stride length.
                                                          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                                          val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val dilated_conv3d : + Symbol.Shape.Type.arr

                                                          dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • strides specifies the stride length.
                                                          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                                          val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val max_pool1d : + Symbol.Shape.Type.arr

                                                          dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • strides specifies the stride length.
                                                          • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                                          val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val max_pool2d : + Symbol.Shape.Type.arr

                                                          max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                                          val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val max_pool3d : + Symbol.Shape.Type.arr

                                                          max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                                          val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val avg_pool1d : + Symbol.Shape.Type.arr

                                                          max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                                          val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val avg_pool2d : + Symbol.Shape.Type.arr

                                                          avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                                          val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val avg_pool3d : + Symbol.Shape.Type.arr

                                                          avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                                          val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val conv1d_backward_input : + Symbol.Shape.Type.arr

                                                          avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                                                          • padding specifies the padding strategy (default is "valid").
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                                          val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          upsampling2d input size performs a 2-dimensional upsampling on the input array.

                                                          • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                                                          TODO

                                                          val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                                          conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                                                          • input is the original input array.
                                                          • kernel is the convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                                          val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val conv2d_backward_input : + Symbol.Shape.Type.arr

                                                          conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                                                          • input is the original input array.
                                                          • kernel is the convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                                          TODO

                                                          val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                                          conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                                                          • input is the original input array.
                                                          • kernel is the convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                                          val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val conv3d_backward_input : + Symbol.Shape.Type.arr

                                                          conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                                                          • input is the original input array.
                                                          • kernel is the convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                                          TODO

                                                          val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                                          conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                                                          • input is the original input array.
                                                          • kernel is the convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                                          val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                                                          conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                                                          • input is the original input array.
                                                          • kernel is the convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                                                          val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                                          transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                                                          • input is the original input array.
                                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                                          val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                                                          transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                                                          • input is the original input array.
                                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                                          val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                                          transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                                                          • input is the original input array.
                                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                                          val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                                                          transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                                                          • input is the original input array.
                                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                                          val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                                          transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                                                          • input is the original input array.
                                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                                          val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                                                          transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                                                          • input is the original input array.
                                                          • kernel is the transposed convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                                          val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                                          dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                                                          • input is the original input array.
                                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • dilations specifies the dilation rate.
                                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                                          val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                                                          dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                                                          • input is the original input array.
                                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • dilations specifies the dilation rate.
                                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                                          val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                                          dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                                                          • input is the original input array.
                                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • dilations specifies the dilation rate.
                                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                                          val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                                                          dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                                                          • input is the original input array.
                                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • dilations specifies the dilation rate.
                                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                                          val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                                          dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                                                          • input is the original input array.
                                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • dilations specifies the dilation rate.
                                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                                          val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val max_pool1d_backward : + Symbol.Shape.Type.arr

                                                          dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                                                          • input is the original input array.
                                                          • kernel is the dilated convolutional kernel used during the forward pass.
                                                          • strides specifies the stride length.
                                                          • dilations specifies the dilation rate.
                                                          • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                                          val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val max_pool2d_backward : + Symbol.Shape.Type.arr

                                                          max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                                                          • padding specifies the padding strategy used during the forward pass.
                                                          • input is the original input array.
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                                          val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val max_pool3d_backward : + Symbol.Shape.Type.arr

                                                          max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                                                          • padding specifies the padding strategy used during the forward pass.
                                                          • input is the original input array.
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                                          val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val avg_pool1d_backward : + Symbol.Shape.Type.arr

                                                          max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                                                          • padding specifies the padding strategy used during the forward pass.
                                                          • input is the original input array.
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                                          val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val avg_pool2d_backward : + Symbol.Shape.Type.arr

                                                          avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                                                          • padding specifies the padding strategy used during the forward pass.
                                                          • input is the original input array.
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                                          val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val avg_pool3d_backward : + Symbol.Shape.Type.arr

                                                          avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                                                          • padding specifies the padding strategy used during the forward pass.
                                                          • input is the original input array.
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                                          val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val upsampling2d_backward : + Symbol.Shape.Type.arr

                                                          avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                                                          • padding specifies the padding strategy used during the forward pass.
                                                          • input is the original input array.
                                                          • pool_size specifies the size of the pooling window.
                                                          • strides specifies the stride length.
                                                          • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                                          val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val row_num : Symbol.Shape.Type.arr -> int

                                                          TODO

                                                          val col_num : Symbol.Shape.Type.arr -> int

                                                          TODO

                                                          val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                                          TODO

                                                          val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                                          TODO

                                                          TODO

                                                          upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                                                          • input is the original input array.
                                                          • size specifies the upsampling factors for each dimension.
                                                          • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                                                          val row_num : Symbol.Shape.Type.arr -> int

                                                          row_num arr returns the number of rows in the array arr.

                                                          val col_num : Symbol.Shape.Type.arr -> int

                                                          col_num arr returns the number of columns in the array arr.

                                                          row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                                                          val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                          rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                                                          val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                                          copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                                                          val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                                          copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                                                          diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                                                          trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                                                          val transpose : + Symbol.Shape.Type.arr

                                                          dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                                                          val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val to_rows : Symbol.Shape.Type.arr -> 'a array

                                                          TODO

                                                          TODO

                                                          val to_cols : Symbol.Shape.Type.arr -> 'a array

                                                          TODO

                                                          TODO

                                                          val of_array : + Symbol.Shape.Type.arr

                                                          transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                                                          val to_rows : Symbol.Shape.Type.arr -> 'a array

                                                          to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                                                          of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                                                          val to_cols : Symbol.Shape.Type.arr -> 'a array

                                                          to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                                                          of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                                                          val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                                          TODO

                                                          Scalar functions
                                                          module Scalar : sig ... end
                                                          module Mat : sig ... end
                                                          module Linalg : sig ... end
                                                          + Symbol.Shape.Type.arr

                                                          of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                                                          val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                                          of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                                                          val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                                          to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                                                          Scalar functions
                                                          module Scalar : sig ... end
                                                          module Mat : sig ... end
                                                          module Linalg : sig ... end
                                                          diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/index.html index 259d3e902..533e42730 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser)

                                                          Module Graph.Optimiser

                                                          Core functions
                                                          val estimate_complexity : 'a Owl_graph.node array -> int * int

                                                          TODO

                                                          val optimise_nodes : +Optimiser (owl-base.Owl_neural_compiler.Make.E.Graph.Optimiser)

                                                          Module Graph.Optimiser

                                                          Core functions
                                                          val estimate_complexity : 'a Owl_graph.node array -> int * int

                                                          TODO

                                                          val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

                                                          TODO

                                                          diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/index.html index adab5c0f5..babb9ad75 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_neural_compiler.Make.E.Graph)

                                                          Module E.Graph

                                                          Type definition
                                                          type graph

                                                          TODO

                                                          Core functions
                                                          val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                                                          TODO

                                                          val graph_to_dot : graph -> string

                                                          TODO

                                                          val graph_to_trace : graph -> string

                                                          TODO

                                                          val save_graph : 'a -> string -> unit

                                                          TODO

                                                          val load_graph : string -> 'a * 'b

                                                          TODO

                                                          val collect_rvs : +Graph (owl-base.Owl_neural_compiler.Make.E.Graph)

                                                          Module E.Graph

                                                          Type definition
                                                          type graph

                                                          TODO

                                                          Core functions
                                                          val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                                                          TODO

                                                          val graph_to_dot : graph -> string

                                                          TODO

                                                          val graph_to_trace : graph -> string

                                                          TODO

                                                          val save_graph : 'a -> string -> unit

                                                          TODO

                                                          val load_graph : string -> 'a * 'b

                                                          TODO

                                                          val invalidate_rvs : graph -> unit

                                                          TODO

                                                          val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/index.html b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/index.html index effa7965c..3723a5dd1 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/argument-1-E/index.html @@ -1,2 +1,2 @@ -E (owl-base.Owl_neural_compiler.Make.E)

                                                          Parameter Make.E

                                                          Core evaluation functions of the engine

                                                          TODO

                                                          TODO

                                                          val eval_graph : Graph.graph -> unit

                                                          TODO

                                                          +E (owl-base.Owl_neural_compiler.Make.E)

                                                          Parameter Make.E

                                                          Core evaluation functions of the engine

                                                          TODO

                                                          TODO

                                                          val eval_graph : Graph.graph -> unit

                                                          TODO

                                                          diff --git a/docs/owl-base/Owl_neural_compiler/Make/index.html b/docs/owl-base/Owl_neural_compiler/Make/index.html index 324a66ff5..6151addd7 100644 --- a/docs/owl-base/Owl_neural_compiler/Make/index.html +++ b/docs/owl-base/Owl_neural_compiler/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_neural_compiler.Make)

                                                          Module Owl_neural_compiler.Make

                                                          Parameters

                                                          Signature

                                                          module Engine : sig ... end
                                                          module Neural : sig ... end

                                                          Naive compilation functions, need to pass in loss function

                                                          val compile_simple : +Make (owl-base.Owl_neural_compiler.Make)

                                                          Module Owl_neural_compiler.Make

                                                          Parameters

                                                          Signature

                                                          module Engine : sig ... end
                                                          module Neural : sig ... end

                                                          Naive compilation functions, need to pass in loss function

                                                          val compile_simple : Neural.Graph.network -> int array -> (Neural.Algodiff.t -> diff --git a/docs/owl-base/Owl_neural_compiler/index.html b/docs/owl-base/Owl_neural_compiler/index.html index 3467d643f..a940b61ef 100644 --- a/docs/owl-base/Owl_neural_compiler/index.html +++ b/docs/owl-base/Owl_neural_compiler/index.html @@ -1,2 +1,2 @@ -Owl_neural_compiler (owl-base.Owl_neural_compiler)

                                                          Module Owl_neural_compiler

                                                          module Make (E : Owl_types_computation_engine.Sig) : sig ... end
                                                          +Owl_neural_compiler (owl-base.Owl_neural_compiler)

                                                          Module Owl_neural_compiler

                                                          module Make (E : Owl_types_computation_engine.Sig) : sig ... end
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Activation/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Activation/index.html index 53e9a7c93..10353e088 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Activation/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Activation/index.html @@ -1,2 +1,2 @@ -Activation (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                            (*

                                                            Types of activation functions.

                                                            *)
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t

                                                          Run one specific activation function.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val activation_to_string : typ -> string

                                                          Return the name of a specific activation function.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Activation (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                            (*

                                                            Types of activation functions.

                                                            *)
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t

                                                          Run one specific activation function.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val activation_to_string : typ -> string

                                                          Return the name of a specific activation function.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Add/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Add/index.html index c3aa4a7b2..b36995c90 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Add/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Add/index.html @@ -1,2 +1,2 @@ -Add (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Add (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AlphaDropout/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AlphaDropout/index.html index 1ffb76456..6934c9284 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AlphaDropout/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AlphaDropout/index.html @@ -1,2 +1,2 @@ -AlphaDropout (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AlphaDropout (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Average/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Average/index.html index 4516f369b..ffe3938a6 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Average/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Average/index.html @@ -1,2 +1,2 @@ -Average (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Average (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AvgPool1D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AvgPool1D/index.html index a07db896c..2a6fbfc4c 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AvgPool1D/index.html @@ -1,2 +1,2 @@ -AvgPool1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AvgPool1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AvgPool2D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AvgPool2D/index.html index e4114d70f..bb432294d 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/AvgPool2D/index.html @@ -1,2 +1,2 @@ -AvgPool2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AvgPool2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Concatenate/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Concatenate/index.html index ecf52ab88..179faa254 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Concatenate/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Concatenate/index.html @@ -1,2 +1,2 @@ -Concatenate (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Concatenate (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv1D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv1D/index.html index 9998c62f3..3023db988 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv2D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv2D/index.html index d4ed78ea1..a2a6950e9 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv3D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv3D/index.html index 0dd539b93..26a9e6ac8 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv1D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv1D/index.html index 8e60ba86a..fc0a2ec74 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv2D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv2D/index.html index cb07fcd13..a2ebeb1a6 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv3D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv3D/index.html index 2831b1c51..388b42bc3 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Dot/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Dot/index.html index 3a381d8ce..db38e1071 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Dot/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Dot/index.html @@ -1,2 +1,2 @@ -Dot (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Dot (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Dropout/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Dropout/index.html index 53160573a..e2bd77c66 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Dropout/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Dropout/index.html @@ -1,2 +1,2 @@ -Dropout (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Dropout (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Embedding/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Embedding/index.html index c81bfdc01..db49cd693 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Embedding/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Embedding/index.html @@ -1,2 +1,2 @@ -Embedding (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Embedding (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Flatten/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Flatten/index.html index 002c81b65..2f6ad4c71 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Flatten/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Flatten/index.html @@ -1,2 +1,2 @@ -Flatten (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Flatten (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/FullyConnected/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/FullyConnected/index.html index 6e903340e..632c90eae 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/FullyConnected/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/FullyConnected/index.html @@ -1,2 +1,2 @@ -FullyConnected (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +FullyConnected (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GRU/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GRU/index.html index e1cb1c8da..b9b977eee 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GRU/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GRU/index.html @@ -1,2 +1,2 @@ -GRU (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GRU (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GaussianDropout/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GaussianDropout/index.html index 02f604072..a9381b72e 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GaussianDropout/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GaussianDropout/index.html @@ -1,2 +1,2 @@ -GaussianDropout (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GaussianDropout (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GaussianNoise/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GaussianNoise/index.html index 64c09dd05..f404ff574 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GaussianNoise/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GaussianNoise/index.html @@ -1,2 +1,2 @@ -GaussianNoise (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GaussianNoise (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalAvgPool1D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalAvgPool1D/index.html index bb3958257..53c93e9c2 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalAvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalAvgPool1D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalAvgPool1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalAvgPool2D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalAvgPool2D/index.html index d8a806006..4bbac782b 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalAvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalAvgPool2D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalAvgPool2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalMaxPool1D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalMaxPool1D/index.html index 73488beb7..c9ea9e8bc 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalMaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalMaxPool1D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalMaxPool1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalMaxPool2D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalMaxPool2D/index.html index acc0c746d..566211bb1 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalMaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/GlobalMaxPool2D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalMaxPool2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Init/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Init/index.html index 52fcf9355..21f5f95f5 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Init/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Init/index.html @@ -1,2 +1,2 @@ -Init (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                            (*

                                                            Initialisation types

                                                            *)
                                                          val calc_fans : int array -> float * float

                                                          Calculate fan-in and fan-out of weights.

                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Init (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                            (*

                                                            Initialisation types

                                                            *)
                                                          val calc_fans : int array -> float * float

                                                          Calculate fan-in and fan-out of weights.

                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Input/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Input/index.html index a5cce957c..3f97447d8 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Input/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Input/index.html @@ -1,2 +1,2 @@ -Input (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Input (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LSTM/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LSTM/index.html index d29ef0041..9b08b0099 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LSTM/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LSTM/index.html @@ -1,2 +1,2 @@ -LSTM (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +LSTM (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Lambda/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Lambda/index.html index 670e5fe81..e2149e038 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Lambda/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Lambda (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?out_shape:int array -> (Optimise.Algodiff.t -> Optimise.Algodiff.t) -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LambdaArray/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LambdaArray/index.html index 153aa2ccd..8a87d59fb 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LambdaArray/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +LambdaArray (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> (Optimise.Algodiff.t array -> Optimise.Algodiff.t) -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Linear/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Linear/index.html index e2353fbe1..b0a32e6ac 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Linear/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Linear/index.html @@ -1,2 +1,2 @@ -Linear (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Linear (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LinearNoBias/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LinearNoBias/index.html index b583648c6..77777a945 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LinearNoBias/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/LinearNoBias/index.html @@ -1,2 +1,2 @@ -LinearNoBias (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +LinearNoBias (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Masking/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Masking/index.html index c2fd0c770..60deeee07 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Masking/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          +Masking (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Max/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Max/index.html index 080c2daa2..e22ebd02a 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Max/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Max/index.html @@ -1,2 +1,2 @@ -Max (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Max (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/MaxPool1D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/MaxPool1D/index.html index 35b15ff4a..c1e780cfa 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/MaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/MaxPool1D/index.html @@ -1,2 +1,2 @@ -MaxPool1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +MaxPool1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/MaxPool2D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/MaxPool2D/index.html index 73b49ab7e..2118beed5 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/MaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/MaxPool2D/index.html @@ -1,2 +1,2 @@ -MaxPool2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +MaxPool2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Mul/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Mul/index.html index 6f4ba5326..702f096e5 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Mul/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Mul/index.html @@ -1,2 +1,2 @@ -Mul (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Mul (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Normalisation/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Normalisation/index.html index 833956f12..f6b2662be 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Normalisation/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Normalisation (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?training:bool -> ?decay:float -> ?mu:Optimise.Algodiff.A.arr -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html index fbe8ccf12..2383d7c1d 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Mat/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Mat/index.html index 80949d241..7530c9c82 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html index d53f55151..772e45804 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/index.html index 90ff98937..9fde90b67 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Arr/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Arr/index.html index c46dd0682..0d3a072b8 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          +Arr (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/index.html index aeec677e7..9f8fcefbb 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          +Builder (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html index 5f107d682..82f7eaaec 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          +Aiso (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html index 707811633..b5d98e2d7 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          +Piso (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html index e7294f9eb..9383f446b 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          +Siao (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 22f7e6e56..89bb2e2d9 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sipo (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html index 7cdc688ba..865637e44 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          +Siso (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html index ea141d1ea..2a74baf01 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sito (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Linalg/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Linalg/index.html index 673cc7830..e291bb378 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Mat/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Mat/index.html index 5d55d8c50..a28d4a55d 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          +Mat (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Maths/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Maths/index.html index 76f60a7c0..5aa340bab 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          +Maths (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/NN/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/NN/index.html index e6217e5bb..b976629b9 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/NN/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : +NN (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/index.html index e7a2dbfa3..ca1e3605b 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Algodiff/index.html @@ -1,5 +1,5 @@ -Algodiff (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig +Algodiff (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Batch/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Batch/index.html index e4270439d..bc7314ac9 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Batch/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Batch (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Checkpoint/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Checkpoint/index.html index 8aa600c76..72ceb8399 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Checkpoint/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Checkpoint (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Clipping/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Clipping/index.html index 2ec116c5f..43b639d70 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Clipping/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Clipping (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Gradient/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Gradient/index.html index c6c957313..f8772dfb7 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Gradient/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : +Gradient (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Learning_Rate/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Learning_Rate/index.html index dd840e0b7..5ca319917 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Learning_Rate/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Learning_Rate (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Loss/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Loss/index.html index c0555839a..c06b8bc2e 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Loss/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Loss (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Momentum/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Momentum/index.html index 1fcd3957b..1ed9f97ff 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Momentum/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Momentum (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Params/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Params/index.html index ef27f831f..a11da0a98 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Params/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : +Params (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Regularisation/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Regularisation/index.html index b0743b366..cf61579ee 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Regularisation/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Regularisation (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Stopping/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Stopping/index.html index 5271e9454..7a8f8c9fc 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Stopping/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Stopping (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Utils/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Utils/index.html index d6cab4a04..d14906faf 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Utils/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : +Utils (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/index.html index ba34bf0f7..b62f8d673 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : +Optimise (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> @@ -28,4 +28,4 @@ (string -> unit) -> Algodiff.t -> Algodiff.t -> - Checkpoint.state

                                                          TODO

                                                          + Checkpoint.state

                                                          This function is minimize the weights in a compiled neural network of graph structure.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding1D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding1D/index.html index 71e937232..f17c23a5e 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          +Padding1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding2D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding2D/index.html index e0baec205..950cdf9db 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding2D/index.html @@ -1,2 +1,2 @@ -Padding2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Padding2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding3D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding3D/index.html index 7c00a9291..534547c1b 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          +Padding3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Recurrent/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Recurrent/index.html index eaab9ead0..835746e46 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Recurrent/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Recurrent (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Reshape/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Reshape/index.html index caea5dbf1..03c89e331 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Reshape/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Reshape/index.html @@ -1,2 +1,2 @@ -Reshape (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Reshape (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Slice/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Slice/index.html index 5d7854b57..8315a7517 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Slice/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/Slice/index.html @@ -1,2 +1,2 @@ -Slice (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }

                                                          Neuron type definition.

                                                          val create : int list list -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Slice (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }

                                                          Neuron type definition.

                                                          val create : int list list -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv1D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv1D/index.html index 48daf4d95..d15577e59 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv2D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv2D/index.html index 31fcc753e..7d219ccce 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv3D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv3D/index.html index b1053412a..3ba3a180a 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling1D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling1D/index.html index c6935a46a..d7abc3844 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          +UpSampling1D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling2D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling2D/index.html index 84aa409db..c2cf77fd3 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling2D/index.html @@ -1,2 +1,2 @@ -UpSampling2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +UpSampling2D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling3D/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling3D/index.html index cd0709cda..64a4a317d 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          +UpSampling3D (owl-base.Owl_neural_generic.Flatten.Graph.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/index.html index 4f964855f..9504e7ba0 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/Neuron/index.html @@ -1,2 +1,2 @@ -Neuron (owl-base.Owl_neural_generic.Flatten.Graph.Neuron)

                                                          Module Graph.Neuron

                                                          Init neuron
                                                          module Init : sig ... end
                                                          Input neuron
                                                          module Input : sig ... end
                                                          Activation neuron
                                                          module Activation : sig ... end
                                                          Linear neuron
                                                          module Linear : sig ... end
                                                          LinearNoBias neuron
                                                          module LinearNoBias : sig ... end
                                                          Recurrent neuron
                                                          module Recurrent : sig ... end
                                                          LSTM neuron
                                                          module LSTM : sig ... end
                                                          GRU neuron
                                                          module GRU : sig ... end
                                                          Conv1D neuron
                                                          module Conv1D : sig ... end
                                                          Conv2D neuron
                                                          module Conv2D : sig ... end
                                                          Conv3D neuron
                                                          module Conv3D : sig ... end
                                                          DilatedConv1D neuron
                                                          module DilatedConv1D : sig ... end
                                                          DilatedConv2D neuron
                                                          module DilatedConv2D : sig ... end
                                                          DilatedConv3D neuron
                                                          module DilatedConv3D : sig ... end
                                                          TransposeConv1D neuron
                                                          module TransposeConv1D : sig ... end
                                                          TransposeConv2D neuron
                                                          module TransposeConv2D : sig ... end
                                                          TransposeConv3D neuron
                                                          module TransposeConv3D : sig ... end
                                                          FullyConnected neuron
                                                          module FullyConnected : sig ... end
                                                          MaxPool1D neuron
                                                          module MaxPool1D : sig ... end
                                                          MaxPool2D neuron
                                                          module MaxPool2D : sig ... end
                                                          AvgPool1D neuron
                                                          module AvgPool1D : sig ... end
                                                          AvgPool2D neuron
                                                          module AvgPool2D : sig ... end
                                                          GlobalMaxPool1D neuron
                                                          module GlobalMaxPool1D : sig ... end
                                                          GlobalMaxPool2D neuron
                                                          module GlobalMaxPool2D : sig ... end
                                                          GlobalAvgPool1D neuron
                                                          module GlobalAvgPool1D : sig ... end
                                                          GlobalAvgPool2D neuron
                                                          module GlobalAvgPool2D : sig ... end
                                                          UpSampling1D neuron
                                                          module UpSampling1D : sig ... end
                                                          UpSampling2D neuron
                                                          module UpSampling2D : sig ... end
                                                          UpSampling3D neuron
                                                          module UpSampling3D : sig ... end
                                                          Padding1D neuron
                                                          module Padding1D : sig ... end
                                                          Padding2D neuron
                                                          module Padding2D : sig ... end
                                                          Padding3D neuron
                                                          module Padding3D : sig ... end
                                                          Lambda neuron
                                                          module Lambda : sig ... end
                                                          LambdaArray neuron
                                                          module LambdaArray : sig ... end
                                                          Dropout neuron
                                                          module Dropout : sig ... end
                                                          Reshape neuron
                                                          module Reshape : sig ... end
                                                          Flatten neuron
                                                          module Flatten : sig ... end
                                                          Slice neuron
                                                          module Slice : sig ... end
                                                          Add neuron
                                                          module Add : sig ... end
                                                          Mul neuron
                                                          module Mul : sig ... end
                                                          Dot neuron
                                                          module Dot : sig ... end
                                                          Max neuron
                                                          module Max : sig ... end
                                                          Average neuron
                                                          module Average : sig ... end
                                                          Concatenate neuron
                                                          module Concatenate : sig ... end
                                                          Normalisation neuron
                                                          module Normalisation : sig ... end
                                                          GaussianNoise neuron
                                                          module GaussianNoise : sig ... end
                                                          GaussianDropout neuron
                                                          module GaussianDropout : sig ... end
                                                          AlphaDropout neuron
                                                          module AlphaDropout : sig ... end
                                                          Embedding neuron
                                                          module Embedding : sig ... end
                                                          Masking neuron
                                                          module Masking : sig ... end
                                                          Core functions
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                            (*

                                                            Types of neuron.

                                                            *)
                                                          val get_in_out_shape : neuron -> int array * int array

                                                          Get both input and output shapes of a neuron.

                                                          val get_in_shape : neuron -> int array

                                                          Get the input shape of a neuron.

                                                          val get_out_shape : neuron -> int array

                                                          Get the output shape of a neuron.

                                                          val connect : int array array -> neuron -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the trainable parameters in an array, used by Optimise module.

                                                          val mkpri : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the primal values in an array, used by Optimise module.

                                                          val mkadj : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron -> Optimise.Algodiff.t array -> unit

                                                          Update trainable parameters in a neuron, used by Optimise module.

                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit

                                                          Load both trainable and non-trainable parameters into the neuron.

                                                          val save_weights : neuron -> Optimise.Algodiff.t array

                                                          Assemble both trainable and non-trainable parameters of the neuron.

                                                          val copy : neuron -> neuron

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : neuron -> string

                                                          Return the name of the neuron.

                                                          +Neuron (owl-base.Owl_neural_generic.Flatten.Graph.Neuron)

                                                          Module Graph.Neuron

                                                          Init neuron
                                                          module Init : sig ... end
                                                          Input neuron
                                                          module Input : sig ... end
                                                          Activation neuron
                                                          module Activation : sig ... end
                                                          Linear neuron
                                                          module Linear : sig ... end
                                                          LinearNoBias neuron
                                                          module LinearNoBias : sig ... end
                                                          Recurrent neuron
                                                          module Recurrent : sig ... end
                                                          LSTM neuron
                                                          module LSTM : sig ... end
                                                          GRU neuron
                                                          module GRU : sig ... end
                                                          Conv1D neuron
                                                          module Conv1D : sig ... end
                                                          Conv2D neuron
                                                          module Conv2D : sig ... end
                                                          Conv3D neuron
                                                          module Conv3D : sig ... end
                                                          DilatedConv1D neuron
                                                          module DilatedConv1D : sig ... end
                                                          DilatedConv2D neuron
                                                          module DilatedConv2D : sig ... end
                                                          DilatedConv3D neuron
                                                          module DilatedConv3D : sig ... end
                                                          TransposeConv1D neuron
                                                          module TransposeConv1D : sig ... end
                                                          TransposeConv2D neuron
                                                          module TransposeConv2D : sig ... end
                                                          TransposeConv3D neuron
                                                          module TransposeConv3D : sig ... end
                                                          FullyConnected neuron
                                                          module FullyConnected : sig ... end
                                                          MaxPool1D neuron
                                                          module MaxPool1D : sig ... end
                                                          MaxPool2D neuron
                                                          module MaxPool2D : sig ... end
                                                          AvgPool1D neuron
                                                          module AvgPool1D : sig ... end
                                                          AvgPool2D neuron
                                                          module AvgPool2D : sig ... end
                                                          GlobalMaxPool1D neuron
                                                          module GlobalMaxPool1D : sig ... end
                                                          GlobalMaxPool2D neuron
                                                          module GlobalMaxPool2D : sig ... end
                                                          GlobalAvgPool1D neuron
                                                          module GlobalAvgPool1D : sig ... end
                                                          GlobalAvgPool2D neuron
                                                          module GlobalAvgPool2D : sig ... end
                                                          UpSampling1D neuron
                                                          module UpSampling1D : sig ... end
                                                          UpSampling2D neuron
                                                          module UpSampling2D : sig ... end
                                                          UpSampling3D neuron
                                                          module UpSampling3D : sig ... end
                                                          Padding1D neuron
                                                          module Padding1D : sig ... end
                                                          Padding2D neuron
                                                          module Padding2D : sig ... end
                                                          Padding3D neuron
                                                          module Padding3D : sig ... end
                                                          Lambda neuron
                                                          module Lambda : sig ... end
                                                          LambdaArray neuron
                                                          module LambdaArray : sig ... end
                                                          Dropout neuron
                                                          module Dropout : sig ... end
                                                          Reshape neuron
                                                          module Reshape : sig ... end
                                                          Flatten neuron
                                                          module Flatten : sig ... end
                                                          Slice neuron
                                                          module Slice : sig ... end
                                                          Add neuron
                                                          module Add : sig ... end
                                                          Mul neuron
                                                          module Mul : sig ... end
                                                          Dot neuron
                                                          module Dot : sig ... end
                                                          Max neuron
                                                          module Max : sig ... end
                                                          Average neuron
                                                          module Average : sig ... end
                                                          Concatenate neuron
                                                          module Concatenate : sig ... end
                                                          Normalisation neuron
                                                          module Normalisation : sig ... end
                                                          GaussianNoise neuron
                                                          module GaussianNoise : sig ... end
                                                          GaussianDropout neuron
                                                          module GaussianDropout : sig ... end
                                                          AlphaDropout neuron
                                                          module AlphaDropout : sig ... end
                                                          Embedding neuron
                                                          module Embedding : sig ... end
                                                          Masking neuron
                                                          module Masking : sig ... end
                                                          Core functions
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                            (*

                                                            Types of neuron.

                                                            *)
                                                          val get_in_out_shape : neuron -> int array * int array

                                                          Get both input and output shapes of a neuron.

                                                          val get_in_shape : neuron -> int array

                                                          Get the input shape of a neuron.

                                                          val get_out_shape : neuron -> int array

                                                          Get the output shape of a neuron.

                                                          val connect : int array array -> neuron -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the trainable parameters in an array, used by Optimise module.

                                                          val mkpri : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the primal values in an array, used by Optimise module.

                                                          val mkadj : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron -> Optimise.Algodiff.t array -> unit

                                                          Update trainable parameters in a neuron, used by Optimise module.

                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit

                                                          Load both trainable and non-trainable parameters into the neuron.

                                                          val save_weights : neuron -> Optimise.Algodiff.t array

                                                          Assemble both trainable and non-trainable parameters of the neuron.

                                                          val copy : neuron -> neuron

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : neuron -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/index.html b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/index.html index feaff5850..60cb12b7e 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/argument-1-Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_neural_generic.Flatten.Graph)

                                                          Parameter Flatten.Graph

                                                          Type definition
                                                          type node = {
                                                          1. mutable name : string;
                                                          2. mutable prev : node array;
                                                          3. mutable next : node array;
                                                          4. mutable neuron : Neuron.neuron;
                                                          5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                          6. mutable network : network;
                                                          7. mutable train : bool;
                                                          }
                                                          and network = {
                                                          1. mutable nnid : string;
                                                          2. mutable size : int;
                                                          3. mutable roots : node array;
                                                          4. mutable outputs : node array;
                                                          5. mutable topo : node array;
                                                          }

                                                          Type definition of a node and a neural network.

                                                          Manipulate networks
                                                          val make_network : ?nnid:string -> int -> node array -> node array -> network

                                                          Create an empty neural network.

                                                          val make_node : +Graph (owl-base.Owl_neural_generic.Flatten.Graph)

                                                          Parameter Flatten.Graph

                                                          Type definition
                                                          type node = {
                                                          1. mutable name : string;
                                                          2. mutable prev : node array;
                                                          3. mutable next : node array;
                                                          4. mutable neuron : Neuron.neuron;
                                                          5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                          6. mutable network : network;
                                                          7. mutable train : bool;
                                                          }
                                                          and network = {
                                                          1. mutable nnid : string;
                                                          2. mutable size : int;
                                                          3. mutable roots : node array;
                                                          4. mutable outputs : node array;
                                                          5. mutable topo : node array;
                                                          }

                                                          Type definition of a node and a neural network.

                                                          Manipulate networks
                                                          val make_network : ?nnid:string -> int -> node array -> node array -> network

                                                          Create an empty neural network.

                                                          val make_node : ?name:string -> ?train:bool -> node array -> diff --git a/docs/owl-base/Owl_neural_generic/Flatten/index.html b/docs/owl-base/Owl_neural_generic/Flatten/index.html index 353b12533..af30e307a 100644 --- a/docs/owl-base/Owl_neural_generic/Flatten/index.html +++ b/docs/owl-base/Owl_neural_generic/Flatten/index.html @@ -1,2 +1,2 @@ -Flatten (owl-base.Owl_neural_generic.Flatten)

                                                          Module Owl_neural_generic.Flatten

                                                          Parameters

                                                          Signature

                                                          module Graph = Graph
                                                          module Optimise = Graph.Neuron.Optimise
                                                          module Init = Graph.Neuron.Init
                                                          module Activation = Graph.Neuron.Activation
                                                          module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                          +Flatten (owl-base.Owl_neural_generic.Flatten)

                                                          Module Owl_neural_generic.Flatten

                                                          Parameters

                                                          Signature

                                                          module Graph = Graph
                                                          module Optimise = Graph.Neuron.Optimise
                                                          module Init = Graph.Neuron.Init
                                                          module Activation = Graph.Neuron.Activation
                                                          module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Activation/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Activation/index.html index 84547b324..f0fe13d74 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Activation/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Activation/index.html @@ -1,2 +1,2 @@ -Activation (owl-base.Owl_neural_generic.Make.Graph.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ = Make_Embedded(A).Neuron.Activation.typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                          type neuron_typ = Make_Embedded(A).Neuron.Activation.neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t
                                                          val copy : neuron_typ -> neuron_typ
                                                          val activation_to_string : typ -> string
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Activation (owl-base.Owl_neural_generic.Make.Graph.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ = Make_Embedded(A).Neuron.Activation.typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                          type neuron_typ = Make_Embedded(A).Neuron.Activation.neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t
                                                          val copy : neuron_typ -> neuron_typ
                                                          val activation_to_string : typ -> string
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Add/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Add/index.html index 676e4789b..c6477f3e4 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Add/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Add/index.html @@ -1,2 +1,2 @@ -Add (owl-base.Owl_neural_generic.Make.Graph.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = Make_Embedded(A).Neuron.Add.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Add (owl-base.Owl_neural_generic.Make.Graph.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = Make_Embedded(A).Neuron.Add.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AlphaDropout/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AlphaDropout/index.html index 0f6ee692b..7dbb86b25 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AlphaDropout/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AlphaDropout/index.html @@ -1,2 +1,2 @@ -AlphaDropout (owl-base.Owl_neural_generic.Make.Graph.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = Make_Embedded(A).Neuron.AlphaDropout.neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +AlphaDropout (owl-base.Owl_neural_generic.Make.Graph.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = Make_Embedded(A).Neuron.AlphaDropout.neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Average/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Average/index.html index bffc491c1..6f7c54a97 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Average/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Average/index.html @@ -1,2 +1,2 @@ -Average (owl-base.Owl_neural_generic.Make.Graph.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = Make_Embedded(A).Neuron.Average.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Average (owl-base.Owl_neural_generic.Make.Graph.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = Make_Embedded(A).Neuron.Average.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AvgPool1D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AvgPool1D/index.html index 1764b6bf9..ae6a663d8 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AvgPool1D/index.html @@ -1,2 +1,2 @@ -AvgPool1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.AvgPool1D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +AvgPool1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.AvgPool1D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AvgPool2D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AvgPool2D/index.html index 362c442da..5c29386c5 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/AvgPool2D/index.html @@ -1,2 +1,2 @@ -AvgPool2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.AvgPool2D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +AvgPool2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.AvgPool2D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Concatenate/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Concatenate/index.html index 22ef9d08f..42ba98c81 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Concatenate/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Concatenate/index.html @@ -1,2 +1,2 @@ -Concatenate (owl-base.Owl_neural_generic.Make.Graph.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = Make_Embedded(A).Neuron.Concatenate.neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Concatenate (owl-base.Owl_neural_generic.Make.Graph.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = Make_Embedded(A).Neuron.Concatenate.neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv1D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv1D/index.html index fb035a841..09b6e395b 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.Conv1D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +Conv1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.Conv1D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv2D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv2D/index.html index a921d6fa2..1ea082e41 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.Conv2D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +Conv2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.Conv2D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv3D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv3D/index.html index d06a5fc73..0bff9b87f 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = Make_Embedded(A).Neuron.Conv3D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +Conv3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = Make_Embedded(A).Neuron.Conv3D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv1D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv1D/index.html index 8cb3cf2f3..81e0094a2 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.DilatedConv1D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : +DilatedConv1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.DilatedConv1D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv2D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv2D/index.html index 8ae2770cc..ac35ec20b 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.DilatedConv2D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : +DilatedConv2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.DilatedConv2D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv3D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv3D/index.html index 1387bdc4f..510bcdfcf 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = Make_Embedded(A).Neuron.DilatedConv3D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : +DilatedConv3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = Make_Embedded(A).Neuron.DilatedConv3D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Dot/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Dot/index.html index 230808343..88e6cff7f 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Dot/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Dot/index.html @@ -1,2 +1,2 @@ -Dot (owl-base.Owl_neural_generic.Make.Graph.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = Make_Embedded(A).Neuron.Dot.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Dot (owl-base.Owl_neural_generic.Make.Graph.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = Make_Embedded(A).Neuron.Dot.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Dropout/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Dropout/index.html index 6ded63c15..5e5ffcc73 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Dropout/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Dropout/index.html @@ -1,2 +1,2 @@ -Dropout (owl-base.Owl_neural_generic.Make.Graph.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = Make_Embedded(A).Neuron.Dropout.neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Dropout (owl-base.Owl_neural_generic.Make.Graph.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = Make_Embedded(A).Neuron.Dropout.neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Embedding/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Embedding/index.html index 21c692148..d18755d76 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Embedding/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Embedding/index.html @@ -1,2 +1,2 @@ -Embedding (owl-base.Owl_neural_generic.Make.Graph.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = Make_Embedded(A).Neuron.Embedding.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Embedding (owl-base.Owl_neural_generic.Make.Graph.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = Make_Embedded(A).Neuron.Embedding.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Flatten/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Flatten/index.html index a68d24cfd..27fa5ba6b 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Flatten/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Flatten/index.html @@ -1,2 +1,2 @@ -Flatten (owl-base.Owl_neural_generic.Make.Graph.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = Make_Embedded(A).Neuron.Flatten.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Flatten (owl-base.Owl_neural_generic.Make.Graph.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = Make_Embedded(A).Neuron.Flatten.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/FullyConnected/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/FullyConnected/index.html index 30f407bc6..95ddf321c 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/FullyConnected/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/FullyConnected/index.html @@ -1,2 +1,2 @@ -FullyConnected (owl-base.Owl_neural_generic.Make.Graph.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = Make_Embedded(A).Neuron.FullyConnected.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +FullyConnected (owl-base.Owl_neural_generic.Make.Graph.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = Make_Embedded(A).Neuron.FullyConnected.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GRU/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GRU/index.html index 5e524f6ce..d0852a0de 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GRU/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GRU/index.html @@ -1,2 +1,2 @@ -GRU (owl-base.Owl_neural_generic.Make.Graph.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = Make_Embedded(A).Neuron.GRU.neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GRU (owl-base.Owl_neural_generic.Make.Graph.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = Make_Embedded(A).Neuron.GRU.neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GaussianDropout/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GaussianDropout/index.html index 1d4ea9628..31bc685d9 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GaussianDropout/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GaussianDropout/index.html @@ -1,2 +1,2 @@ -GaussianDropout (owl-base.Owl_neural_generic.Make.Graph.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = Make_Embedded(A).Neuron.GaussianDropout.neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GaussianDropout (owl-base.Owl_neural_generic.Make.Graph.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = Make_Embedded(A).Neuron.GaussianDropout.neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GaussianNoise/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GaussianNoise/index.html index f746da0b6..6b8e5829b 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GaussianNoise/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GaussianNoise/index.html @@ -1,2 +1,2 @@ -GaussianNoise (owl-base.Owl_neural_generic.Make.Graph.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = Make_Embedded(A).Neuron.GaussianNoise.neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GaussianNoise (owl-base.Owl_neural_generic.Make.Graph.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = Make_Embedded(A).Neuron.GaussianNoise.neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalAvgPool1D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalAvgPool1D/index.html index 8ccf880b9..907d4a7d5 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalAvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalAvgPool1D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.GlobalAvgPool1D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GlobalAvgPool1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.GlobalAvgPool1D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalAvgPool2D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalAvgPool2D/index.html index 69bc293b5..2c87bb933 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalAvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalAvgPool2D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.GlobalAvgPool2D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GlobalAvgPool2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.GlobalAvgPool2D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalMaxPool1D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalMaxPool1D/index.html index b820d1049..71115df8a 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalMaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalMaxPool1D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.GlobalMaxPool1D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GlobalMaxPool1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.GlobalMaxPool1D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalMaxPool2D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalMaxPool2D/index.html index 77c67b112..094337ce6 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalMaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/GlobalMaxPool2D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.GlobalMaxPool2D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GlobalMaxPool2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.GlobalMaxPool2D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Init/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Init/index.html index becfcfe74..2d73a4995 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Init/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Init/index.html @@ -1,2 +1,2 @@ -Init (owl-base.Owl_neural_generic.Make.Graph.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ = Make_Embedded(A).Neuron.Init.typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                          val calc_fans : int array -> float * float
                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          val to_string : typ -> string
                                                          val to_name : unit -> string
                                                          +Init (owl-base.Owl_neural_generic.Make.Graph.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ = Make_Embedded(A).Neuron.Init.typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                          val calc_fans : int array -> float * float
                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          val to_string : typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Input/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Input/index.html index b219ed32c..e88442a11 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Input/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Input/index.html @@ -1,2 +1,2 @@ -Input (owl-base.Owl_neural_generic.Make.Graph.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = Make_Embedded(A).Neuron.Input.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Input (owl-base.Owl_neural_generic.Make.Graph.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = Make_Embedded(A).Neuron.Input.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LSTM/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LSTM/index.html index a5df5f423..308a10963 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LSTM/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LSTM/index.html @@ -1,2 +1,2 @@ -LSTM (owl-base.Owl_neural_generic.Make.Graph.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = Make_Embedded(A).Neuron.LSTM.neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +LSTM (owl-base.Owl_neural_generic.Make.Graph.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = Make_Embedded(A).Neuron.LSTM.neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Lambda/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Lambda/index.html index dd4953ac2..4d2b66d54 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Lambda/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl-base.Owl_neural_generic.Make.Graph.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = Make_Embedded(A).Neuron.Lambda.neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : +Lambda (owl-base.Owl_neural_generic.Make.Graph.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = Make_Embedded(A).Neuron.Lambda.neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : ?out_shape:int array -> (Optimise.Algodiff.t -> Optimise.Algodiff.t) -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LambdaArray/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LambdaArray/index.html index 732483e31..c9700c6f8 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LambdaArray/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl-base.Owl_neural_generic.Make.Graph.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = Make_Embedded(A).Neuron.LambdaArray.neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : +LambdaArray (owl-base.Owl_neural_generic.Make.Graph.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = Make_Embedded(A).Neuron.LambdaArray.neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> (Optimise.Algodiff.t array -> Optimise.Algodiff.t) -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Linear/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Linear/index.html index 47682a0e7..4d2e752b4 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Linear/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Linear/index.html @@ -1,2 +1,2 @@ -Linear (owl-base.Owl_neural_generic.Make.Graph.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = Make_Embedded(A).Neuron.Linear.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Linear (owl-base.Owl_neural_generic.Make.Graph.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = Make_Embedded(A).Neuron.Linear.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LinearNoBias/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LinearNoBias/index.html index d01c0b3ab..d37aebc59 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LinearNoBias/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/LinearNoBias/index.html @@ -1,2 +1,2 @@ -LinearNoBias (owl-base.Owl_neural_generic.Make.Graph.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = Make_Embedded(A).Neuron.LinearNoBias.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +LinearNoBias (owl-base.Owl_neural_generic.Make.Graph.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = Make_Embedded(A).Neuron.LinearNoBias.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Masking/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Masking/index.html index 422c96df6..8a29da0d8 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Masking/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl-base.Owl_neural_generic.Make.Graph.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          +Masking (owl-base.Owl_neural_generic.Make.Graph.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Max/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Max/index.html index faa6086aa..3ac592676 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Max/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Max/index.html @@ -1,2 +1,2 @@ -Max (owl-base.Owl_neural_generic.Make.Graph.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = Make_Embedded(A).Neuron.Max.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Max (owl-base.Owl_neural_generic.Make.Graph.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = Make_Embedded(A).Neuron.Max.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/MaxPool1D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/MaxPool1D/index.html index c20157be6..9d159563c 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/MaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/MaxPool1D/index.html @@ -1,2 +1,2 @@ -MaxPool1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.MaxPool1D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +MaxPool1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.MaxPool1D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/MaxPool2D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/MaxPool2D/index.html index 6c2449943..3e1c0a220 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/MaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/MaxPool2D/index.html @@ -1,2 +1,2 @@ -MaxPool2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.MaxPool2D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +MaxPool2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.MaxPool2D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Mul/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Mul/index.html index 9428783c3..a4768e5ff 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Mul/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Mul/index.html @@ -1,2 +1,2 @@ -Mul (owl-base.Owl_neural_generic.Make.Graph.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = Make_Embedded(A).Neuron.Mul.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Mul (owl-base.Owl_neural_generic.Make.Graph.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = Make_Embedded(A).Neuron.Mul.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Normalisation/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Normalisation/index.html index 01837697a..1b36fa80b 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Normalisation/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl-base.Owl_neural_generic.Make.Graph.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = Make_Embedded(A).Neuron.Normalisation.neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : +Normalisation (owl-base.Owl_neural_generic.Make.Graph.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = Make_Embedded(A).Neuron.Normalisation.neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?training:bool -> ?decay:float -> ?mu:Optimise.Algodiff.A.arr -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html index 125c70cff..35eff2744 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html index 9175e7f0f..54307ad7c 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html index f8f823e4e..e9f3be16e 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/index.html index 4ae18667d..fea51fc7b 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Arr/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Arr/index.html index ff1b8ff39..b3ee24ef3 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          +Arr (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/index.html index e95d3ba11..53acf9ff4 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t
                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t
                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t
                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array
                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t
                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t
                                                          +Builder (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t
                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t
                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t
                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array
                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t
                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html index 2d9283e62..da491cf81 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          +Aiso (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html index e2cdff866..453336ace 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          +Piso (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html index 5c8441238..0ce454ed5 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          +Siao (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 1887b05c0..6f5cc3ae8 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sipo (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html index a9218060c..60c99dddf 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          +Siso (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html index 042448e6d..b91c743ef 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sito (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Linalg/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Linalg/index.html index 5c8c3b23c..cc0a80efc 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t
                                                          val logdet : t -> t
                                                          val chol : ?upper:bool -> t -> t
                                                          val qr : t -> t * t
                                                          val lq : t -> t * t
                                                          val svd : ?thin:bool -> t -> t * t * t
                                                          val sylvester : t -> t -> t -> t
                                                          val lyapunov : t -> t -> t
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t
                                                          val logdet : t -> t
                                                          val chol : ?upper:bool -> t -> t
                                                          val qr : t -> t * t
                                                          val lq : t -> t * t
                                                          val svd : ?thin:bool -> t -> t * t * t
                                                          val sylvester : t -> t -> t -> t
                                                          val lyapunov : t -> t -> t
                                                          val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Mat/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Mat/index.html index bb0eff6ac..a89e79cd0 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          +Mat (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Maths/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Maths/index.html index 0ed66a4a0..5de1acf03 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t
                                                          val (-) : t -> t -> t
                                                          val (*) : t -> t -> t
                                                          val (/) : t -> t -> t
                                                          val (*@) : t -> t -> t
                                                          val (**) : t -> t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val kron : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val pow : t -> t -> t
                                                          val atan2 : t -> t -> t
                                                          val min2 : t -> t -> t
                                                          val max2 : t -> t -> t
                                                          val cross_entropy : t -> t -> t
                                                          val inv : t -> t
                                                          val neg : t -> t
                                                          val abs : t -> t
                                                          val signum : t -> t
                                                          val floor : t -> t
                                                          val ceil : t -> t
                                                          val round : t -> t
                                                          val sqr : t -> t
                                                          val sqrt : t -> t
                                                          val log : t -> t
                                                          val log2 : t -> t
                                                          val log10 : t -> t
                                                          val exp : t -> t
                                                          val sin : t -> t
                                                          val cos : t -> t
                                                          val tan : t -> t
                                                          val sinh : t -> t
                                                          val cosh : t -> t
                                                          val tanh : t -> t
                                                          val asin : t -> t
                                                          val acos : t -> t
                                                          val atan : t -> t
                                                          val asinh : t -> t
                                                          val acosh : t -> t
                                                          val atanh : t -> t
                                                          val sum' : t -> t
                                                          val log_sum_exp' : t -> t
                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                          val sum_reduce : ?axis:int array -> t -> t
                                                          val mean : t -> t
                                                          val transpose : ?axis:int array -> t -> t
                                                          val swap : int -> int -> t -> t
                                                          val l1norm' : t -> t
                                                          val l2norm' : t -> t
                                                          val l2norm_sqr' : t -> t
                                                          val sigmoid : t -> t
                                                          val relu : t -> t
                                                          val dawsn : t -> t
                                                          val softplus : t -> t
                                                          val softsign : t -> t
                                                          val softmax : ?axis:int -> t -> t
                                                          val reshape : t -> int array -> t
                                                          val flatten : t -> t
                                                          val get_item : t -> int -> int -> t
                                                          val get_row : t -> int -> t
                                                          val concat : axis:int -> t -> t -> t
                                                          val split : axis:int -> int array -> t -> t array
                                                          val of_arrays : t array array -> t
                                                          val to_arrays : t -> t array array
                                                          val concatenate : axis:int -> t array -> t
                                                          val stack : axis:int -> t array -> t
                                                          val get_slice : int list list -> t -> t
                                                          val set_slice : int list list -> t -> t -> t
                                                          val get_fancy : Owl_types.index list -> t -> t
                                                          val set_fancy : Owl_types.index list -> t -> t -> t
                                                          val diag : ?k:int -> t -> t
                                                          val diagm : ?k:int -> t -> t
                                                          val trace : t -> t
                                                          val triu : ?k:int -> t -> t
                                                          val tril : ?k:int -> t -> t
                                                          +Maths (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t
                                                          val (-) : t -> t -> t
                                                          val (*) : t -> t -> t
                                                          val (/) : t -> t -> t
                                                          val (*@) : t -> t -> t
                                                          val (**) : t -> t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val kron : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val pow : t -> t -> t
                                                          val atan2 : t -> t -> t
                                                          val min2 : t -> t -> t
                                                          val max2 : t -> t -> t
                                                          val cross_entropy : t -> t -> t
                                                          val inv : t -> t
                                                          val neg : t -> t
                                                          val abs : t -> t
                                                          val signum : t -> t
                                                          val floor : t -> t
                                                          val ceil : t -> t
                                                          val round : t -> t
                                                          val sqr : t -> t
                                                          val sqrt : t -> t
                                                          val log : t -> t
                                                          val log2 : t -> t
                                                          val log10 : t -> t
                                                          val exp : t -> t
                                                          val sin : t -> t
                                                          val cos : t -> t
                                                          val tan : t -> t
                                                          val sinh : t -> t
                                                          val cosh : t -> t
                                                          val tanh : t -> t
                                                          val asin : t -> t
                                                          val acos : t -> t
                                                          val atan : t -> t
                                                          val asinh : t -> t
                                                          val acosh : t -> t
                                                          val atanh : t -> t
                                                          val sum' : t -> t
                                                          val log_sum_exp' : t -> t
                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                          val sum_reduce : ?axis:int array -> t -> t
                                                          val mean : t -> t
                                                          val transpose : ?axis:int array -> t -> t
                                                          val swap : int -> int -> t -> t
                                                          val l1norm' : t -> t
                                                          val l2norm' : t -> t
                                                          val l2norm_sqr' : t -> t
                                                          val sigmoid : t -> t
                                                          val relu : t -> t
                                                          val dawsn : t -> t
                                                          val softplus : t -> t
                                                          val softsign : t -> t
                                                          val softmax : ?axis:int -> t -> t
                                                          val reshape : t -> int array -> t
                                                          val flatten : t -> t
                                                          val get_item : t -> int -> int -> t
                                                          val get_row : t -> int -> t
                                                          val concat : axis:int -> t -> t -> t
                                                          val split : axis:int -> int array -> t -> t array
                                                          val of_arrays : t array array -> t
                                                          val to_arrays : t -> t array array
                                                          val concatenate : axis:int -> t array -> t
                                                          val stack : axis:int -> t array -> t
                                                          val get_slice : int list list -> t -> t
                                                          val set_slice : int list list -> t -> t -> t
                                                          val get_fancy : Owl_types.index list -> t -> t
                                                          val set_fancy : Owl_types.index list -> t -> t -> t
                                                          val diag : ?k:int -> t -> t
                                                          val diagm : ?k:int -> t -> t
                                                          val trace : t -> t
                                                          val triu : ?k:int -> t -> t
                                                          val tril : ?k:int -> t -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/NN/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/NN/index.html index b4c36f105..6cf772a1b 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/NN/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t
                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val dilated_conv1d : +NN (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t
                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/index.html index 6a368fe2f..c62d712dd 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Algodiff/index.html @@ -1,2 +1,2 @@ -Algodiff (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          module A : sig ... end
                                                          type t = Make_Embedded(A).Neuron.Optimise.Algodiff.t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          val tag : unit -> int
                                                          val primal : t -> t
                                                          val primal' : t -> t
                                                          val zero : t -> t
                                                          val reset_zero : t -> t
                                                          val tangent : t -> t
                                                          val adjref : t -> t Stdlib.ref
                                                          val adjval : t -> t
                                                          val shape : t -> int array
                                                          val is_float : t -> bool
                                                          val is_arr : t -> bool
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val numel : t -> int
                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                          val clip_by_l2norm : A.elt -> t -> t
                                                          val copy_primal' : t -> t
                                                          val tile : t -> int array -> t
                                                          val repeat : t -> int array -> t
                                                          val pack_elt : A.elt -> t
                                                          val unpack_elt : t -> A.elt
                                                          val pack_flt : float -> t
                                                          val _f : float -> t
                                                          val unpack_flt : t -> float
                                                          val pack_arr : A.arr -> t
                                                          val unpack_arr : t -> A.arr
                                                          val deep_info : t -> string
                                                          val type_info : t -> string
                                                          val error_binop : string -> t -> t -> 'a
                                                          val error_uniop : string -> t -> 'a
                                                          val make_forward : t -> t -> int -> t
                                                          val make_reverse : t -> int -> t
                                                          val reverse_prop : t -> t -> unit
                                                          val diff : (t -> t) -> t -> t
                                                          val diff' : (t -> t) -> t -> t * t
                                                          val grad : (t -> t) -> t -> t
                                                          val grad' : (t -> t) -> t -> t * t
                                                          val jacobian : (t -> t) -> t -> t
                                                          val jacobian' : (t -> t) -> t -> t * t
                                                          val jacobianv : (t -> t) -> t -> t -> t
                                                          val jacobianv' : (t -> t) -> t -> t -> t * t
                                                          val jacobianTv : (t -> t) -> t -> t -> t
                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                          val hessian : (t -> t) -> t -> t
                                                          val hessian' : (t -> t) -> t -> t * t
                                                          val hessianv : (t -> t) -> t -> t -> t
                                                          val hessianv' : (t -> t) -> t -> t -> t * t
                                                          val laplacian : (t -> t) -> t -> t
                                                          val laplacian' : (t -> t) -> t -> t * t
                                                          val gradhessian : (t -> t) -> t -> t * t
                                                          val gradhessian' : (t -> t) -> t -> t * t * t
                                                          val gradhessianv : (t -> t) -> t -> t -> t * t
                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                          module Builder : sig ... end
                                                          module Maths : sig ... end
                                                          module Linalg : sig ... end
                                                          module NN : sig ... end
                                                          module Mat : sig ... end
                                                          module Arr : sig ... end
                                                          val to_trace : t list -> string
                                                          val to_dot : t list -> string
                                                          val pp_num : Stdlib.Format.formatter -> t -> unit
                                                          +Algodiff (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          module A : sig ... end
                                                          type t = Make_Embedded(A).Neuron.Optimise.Algodiff.t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          val tag : unit -> int
                                                          val primal : t -> t
                                                          val primal' : t -> t
                                                          val zero : t -> t
                                                          val reset_zero : t -> t
                                                          val tangent : t -> t
                                                          val adjref : t -> t Stdlib.ref
                                                          val adjval : t -> t
                                                          val shape : t -> int array
                                                          val is_float : t -> bool
                                                          val is_arr : t -> bool
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val numel : t -> int
                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                          val clip_by_l2norm : A.elt -> t -> t
                                                          val copy_primal' : t -> t
                                                          val tile : t -> int array -> t
                                                          val repeat : t -> int array -> t
                                                          val pack_elt : A.elt -> t
                                                          val unpack_elt : t -> A.elt
                                                          val pack_flt : float -> t
                                                          val _f : float -> t
                                                          val unpack_flt : t -> float
                                                          val pack_arr : A.arr -> t
                                                          val unpack_arr : t -> A.arr
                                                          val deep_info : t -> string
                                                          val type_info : t -> string
                                                          val error_binop : string -> t -> t -> 'a
                                                          val error_uniop : string -> t -> 'a
                                                          val make_forward : t -> t -> int -> t
                                                          val make_reverse : t -> int -> t
                                                          val reverse_prop : t -> t -> unit
                                                          val diff : (t -> t) -> t -> t
                                                          val diff' : (t -> t) -> t -> t * t
                                                          val grad : (t -> t) -> t -> t
                                                          val grad' : (t -> t) -> t -> t * t
                                                          val jacobian : (t -> t) -> t -> t
                                                          val jacobian' : (t -> t) -> t -> t * t
                                                          val jacobianv : (t -> t) -> t -> t -> t
                                                          val jacobianv' : (t -> t) -> t -> t -> t * t
                                                          val jacobianTv : (t -> t) -> t -> t -> t
                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                          val hessian : (t -> t) -> t -> t
                                                          val hessian' : (t -> t) -> t -> t * t
                                                          val hessianv : (t -> t) -> t -> t -> t
                                                          val hessianv' : (t -> t) -> t -> t -> t * t
                                                          val laplacian : (t -> t) -> t -> t
                                                          val laplacian' : (t -> t) -> t -> t * t
                                                          val gradhessian : (t -> t) -> t -> t * t
                                                          val gradhessian' : (t -> t) -> t -> t * t * t
                                                          val gradhessianv : (t -> t) -> t -> t -> t * t
                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                          module Builder : sig ... end
                                                          module Maths : sig ... end
                                                          module Linalg : sig ... end
                                                          module NN : sig ... end
                                                          module Mat : sig ... end
                                                          module Arr : sig ... end
                                                          val to_trace : t list -> string
                                                          val to_dot : t list -> string
                                                          val pp_num : Stdlib.Format.formatter -> t -> unit
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Batch/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Batch/index.html index 210558ebd..b0b0300b8 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Batch/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Batch.typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val batches : typ -> Algodiff.t -> int
                                                          val to_string : typ -> string
                                                          +Batch (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Batch.typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val batches : typ -> Algodiff.t -> int
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Checkpoint/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Checkpoint/index.html index ce295cc33..ba3c12c2d 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Checkpoint/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          type state = Make_Embedded(A).Neuron.Optimise.Checkpoint.state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }
                                                          type typ = Make_Embedded(A).Neuron.Optimise.Checkpoint.typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None
                                                          val init_state : int -> float -> state
                                                          val default_checkpoint_fun : (string -> 'a) -> 'a
                                                          val print_state_info : state -> unit
                                                          val print_summary : state -> unit
                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit
                                                          val to_string : typ -> string
                                                          +Checkpoint (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          type state = Make_Embedded(A).Neuron.Optimise.Checkpoint.state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }
                                                          type typ = Make_Embedded(A).Neuron.Optimise.Checkpoint.typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None
                                                          val init_state : int -> float -> state
                                                          val default_checkpoint_fun : (string -> 'a) -> 'a
                                                          val print_state_info : state -> unit
                                                          val print_summary : state -> unit
                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Clipping/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Clipping/index.html index 1f6063a82..f175ae25a 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Clipping/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Clipping.typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          +Clipping (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Clipping.typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Gradient/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Gradient/index.html index 87c5afab3..a715751c6 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Gradient/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Gradient.typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton
                                                          val run : +Gradient (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Gradient.typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton
                                                          val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Learning_Rate/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Learning_Rate/index.html index d8f34ef27..a4fef6825 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Learning_Rate/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Learning_Rate.typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                          val default : typ -> typ
                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                          val to_string : typ -> string
                                                          +Learning_Rate (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Learning_Rate.typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                          val default : typ -> typ
                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Loss/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Loss/index.html index 2507ef8fb..760cca689 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Loss/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Loss.typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          +Loss (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Loss.typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Momentum/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Momentum/index.html index 2eda7e121..3663c6b87 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Momentum/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Momentum.typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          +Momentum (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Momentum.typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Params/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Params/index.html index 438ab0fa1..5f9b00246 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Params/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Params.typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }
                                                          val default : unit -> typ
                                                          val config : +Params (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Params.typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }
                                                          val default : unit -> typ
                                                          val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Regularisation/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Regularisation/index.html index c6e8016a6..a82709c08 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Regularisation/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Regularisation.typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          +Regularisation (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Regularisation.typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Stopping/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Stopping/index.html index 38addba18..35a5388b6 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Stopping/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Stopping.typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None
                                                          val run : typ -> float -> bool
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          +Stopping (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          type typ = Make_Embedded(A).Neuron.Optimise.Stopping.typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None
                                                          val run : typ -> float -> bool
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Utils/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Utils/index.html index f81485913..33d05fc25 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Utils/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          val sample_num : Algodiff.t -> int
                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val get_chunk : +Utils (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          val sample_num : Algodiff.t -> int
                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/index.html index 901dfdc16..58971aedc 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Algodiff : sig ... end
                                                          module Utils : sig ... end
                                                          module Learning_Rate : sig ... end
                                                          module Batch : sig ... end
                                                          module Loss : sig ... end
                                                          module Gradient : sig ... end
                                                          module Momentum : sig ... end
                                                          module Regularisation : sig ... end
                                                          module Clipping : sig ... end
                                                          module Stopping : sig ... end
                                                          module Checkpoint : sig ... end
                                                          module Params : sig ... end
                                                          val minimise_weight : +Optimise (owl-base.Owl_neural_generic.Make.Graph.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Algodiff : sig ... end
                                                          module Utils : sig ... end
                                                          module Learning_Rate : sig ... end
                                                          module Batch : sig ... end
                                                          module Loss : sig ... end
                                                          module Gradient : sig ... end
                                                          module Momentum : sig ... end
                                                          module Regularisation : sig ... end
                                                          module Clipping : sig ... end
                                                          module Stopping : sig ... end
                                                          module Checkpoint : sig ... end
                                                          module Params : sig ... end
                                                          val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding1D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding1D/index.html index c0fb5e4ea..a318017f1 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          +Padding1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding2D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding2D/index.html index de5297ec7..85bbdde71 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding2D/index.html @@ -1,2 +1,2 @@ -Padding2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.Padding2D.neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Padding2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.Padding2D.neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding3D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding3D/index.html index ec8955e40..762e6cc15 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          +Padding3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Recurrent/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Recurrent/index.html index d42a854cf..80a425f5f 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Recurrent/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl-base.Owl_neural_generic.Make.Graph.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = Make_Embedded(A).Neuron.Recurrent.neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }
                                                          val create : +Recurrent (owl-base.Owl_neural_generic.Make.Graph.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = Make_Embedded(A).Neuron.Recurrent.neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Reshape/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Reshape/index.html index 620775fd4..b72ff34e3 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Reshape/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Reshape/index.html @@ -1,2 +1,2 @@ -Reshape (owl-base.Owl_neural_generic.Make.Graph.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = Make_Embedded(A).Neuron.Reshape.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Reshape (owl-base.Owl_neural_generic.Make.Graph.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = Make_Embedded(A).Neuron.Reshape.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Slice/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Slice/index.html index 50818729b..5ca414a43 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Slice/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/Slice/index.html @@ -1,2 +1,2 @@ -Slice (owl-base.Owl_neural_generic.Make.Graph.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = Make_Embedded(A).Neuron.Slice.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }
                                                          val create : int list list -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Slice (owl-base.Owl_neural_generic.Make.Graph.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = Make_Embedded(A).Neuron.Slice.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }
                                                          val create : int list list -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv1D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv1D/index.html index 62168beda..02f760f9e 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.TransposeConv1D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +TransposeConv1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = Make_Embedded(A).Neuron.TransposeConv1D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv2D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv2D/index.html index c1180db5e..ee978efda 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.TransposeConv2D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +TransposeConv2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.TransposeConv2D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv3D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv3D/index.html index ed213e566..0b9e7e1d8 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = Make_Embedded(A).Neuron.TransposeConv3D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +TransposeConv3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = Make_Embedded(A).Neuron.TransposeConv3D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling1D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling1D/index.html index b40b6d11c..ddb66b3d8 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          +UpSampling1D (owl-base.Owl_neural_generic.Make.Graph.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling2D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling2D/index.html index e00e57ce8..8f12b9517 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling2D/index.html @@ -1,2 +1,2 @@ -UpSampling2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.UpSampling2D.neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +UpSampling2D (owl-base.Owl_neural_generic.Make.Graph.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = Make_Embedded(A).Neuron.UpSampling2D.neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling3D/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling3D/index.html index 6e1b5e659..c167bf9a0 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          +UpSampling3D (owl-base.Owl_neural_generic.Make.Graph.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/index.html index b723186ad..87a5e61c8 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/Neuron/index.html @@ -1,2 +1,2 @@ -Neuron (owl-base.Owl_neural_generic.Make.Graph.Neuron)

                                                          Module Graph.Neuron

                                                          module Optimise : sig ... end
                                                          module Init : sig ... end
                                                          module Input : sig ... end
                                                          module Activation : sig ... end
                                                          module Linear : sig ... end
                                                          module LinearNoBias : sig ... end
                                                          module Recurrent : sig ... end
                                                          module LSTM : sig ... end
                                                          module GRU : sig ... end
                                                          module Conv1D : sig ... end
                                                          module Conv2D : sig ... end
                                                          module Conv3D : sig ... end
                                                          module DilatedConv1D : sig ... end
                                                          module DilatedConv2D : sig ... end
                                                          module DilatedConv3D : sig ... end
                                                          module TransposeConv1D : sig ... end
                                                          module TransposeConv2D : sig ... end
                                                          module TransposeConv3D : sig ... end
                                                          module FullyConnected : sig ... end
                                                          module MaxPool1D : sig ... end
                                                          module MaxPool2D : sig ... end
                                                          module AvgPool1D : sig ... end
                                                          module AvgPool2D : sig ... end
                                                          module GlobalMaxPool1D : sig ... end
                                                          module GlobalMaxPool2D : sig ... end
                                                          module GlobalAvgPool1D : sig ... end
                                                          module GlobalAvgPool2D : sig ... end
                                                          module UpSampling1D : sig ... end
                                                          module UpSampling2D : sig ... end
                                                          module UpSampling3D : sig ... end
                                                          module Padding1D : sig ... end
                                                          module Padding2D : sig ... end
                                                          module Padding3D : sig ... end
                                                          module Lambda : sig ... end
                                                          module LambdaArray : sig ... end
                                                          module Dropout : sig ... end
                                                          module Reshape : sig ... end
                                                          module Flatten : sig ... end
                                                          module Slice : sig ... end
                                                          module Add : sig ... end
                                                          module Mul : sig ... end
                                                          module Dot : sig ... end
                                                          module Max : sig ... end
                                                          module Average : sig ... end
                                                          module Concatenate : sig ... end
                                                          module Normalisation : sig ... end
                                                          module GaussianNoise : sig ... end
                                                          module GaussianDropout : sig ... end
                                                          module AlphaDropout : sig ... end
                                                          module Embedding : sig ... end
                                                          module Masking : sig ... end
                                                          type neuron = Make_Embedded(A).Neuron.neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                          val get_in_out_shape : neuron -> int array * int array
                                                          val get_in_shape : neuron -> int array
                                                          val get_out_shape : neuron -> int array
                                                          val connect : int array array -> neuron -> unit
                                                          val init : neuron -> unit
                                                          val reset : neuron -> unit
                                                          val mktag : int -> neuron -> unit
                                                          val mkpar : neuron -> Optimise.Algodiff.t array
                                                          val mkpri : neuron -> Optimise.Algodiff.t array
                                                          val mkadj : neuron -> Optimise.Algodiff.t array
                                                          val update : neuron -> Optimise.Algodiff.t array -> unit
                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit
                                                          val save_weights : neuron -> Optimise.Algodiff.t array
                                                          val copy : neuron -> neuron
                                                          val to_string : neuron -> string
                                                          val to_name : neuron -> string
                                                          +Neuron (owl-base.Owl_neural_generic.Make.Graph.Neuron)

                                                          Module Graph.Neuron

                                                          module Optimise : sig ... end
                                                          module Init : sig ... end
                                                          module Input : sig ... end
                                                          module Activation : sig ... end
                                                          module Linear : sig ... end
                                                          module LinearNoBias : sig ... end
                                                          module Recurrent : sig ... end
                                                          module LSTM : sig ... end
                                                          module GRU : sig ... end
                                                          module Conv1D : sig ... end
                                                          module Conv2D : sig ... end
                                                          module Conv3D : sig ... end
                                                          module DilatedConv1D : sig ... end
                                                          module DilatedConv2D : sig ... end
                                                          module DilatedConv3D : sig ... end
                                                          module TransposeConv1D : sig ... end
                                                          module TransposeConv2D : sig ... end
                                                          module TransposeConv3D : sig ... end
                                                          module FullyConnected : sig ... end
                                                          module MaxPool1D : sig ... end
                                                          module MaxPool2D : sig ... end
                                                          module AvgPool1D : sig ... end
                                                          module AvgPool2D : sig ... end
                                                          module GlobalMaxPool1D : sig ... end
                                                          module GlobalMaxPool2D : sig ... end
                                                          module GlobalAvgPool1D : sig ... end
                                                          module GlobalAvgPool2D : sig ... end
                                                          module UpSampling1D : sig ... end
                                                          module UpSampling2D : sig ... end
                                                          module UpSampling3D : sig ... end
                                                          module Padding1D : sig ... end
                                                          module Padding2D : sig ... end
                                                          module Padding3D : sig ... end
                                                          module Lambda : sig ... end
                                                          module LambdaArray : sig ... end
                                                          module Dropout : sig ... end
                                                          module Reshape : sig ... end
                                                          module Flatten : sig ... end
                                                          module Slice : sig ... end
                                                          module Add : sig ... end
                                                          module Mul : sig ... end
                                                          module Dot : sig ... end
                                                          module Max : sig ... end
                                                          module Average : sig ... end
                                                          module Concatenate : sig ... end
                                                          module Normalisation : sig ... end
                                                          module GaussianNoise : sig ... end
                                                          module GaussianDropout : sig ... end
                                                          module AlphaDropout : sig ... end
                                                          module Embedding : sig ... end
                                                          module Masking : sig ... end
                                                          type neuron = Make_Embedded(A).Neuron.neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                          val get_in_out_shape : neuron -> int array * int array
                                                          val get_in_shape : neuron -> int array
                                                          val get_out_shape : neuron -> int array
                                                          val connect : int array array -> neuron -> unit
                                                          val init : neuron -> unit
                                                          val reset : neuron -> unit
                                                          val mktag : int -> neuron -> unit
                                                          val mkpar : neuron -> Optimise.Algodiff.t array
                                                          val mkpri : neuron -> Optimise.Algodiff.t array
                                                          val mkadj : neuron -> Optimise.Algodiff.t array
                                                          val update : neuron -> Optimise.Algodiff.t array -> unit
                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit
                                                          val save_weights : neuron -> Optimise.Algodiff.t array
                                                          val copy : neuron -> neuron
                                                          val to_string : neuron -> string
                                                          val to_name : neuron -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/Graph/index.html b/docs/owl-base/Owl_neural_generic/Make/Graph/index.html index a2ec98177..48105bc09 100644 --- a/docs/owl-base/Owl_neural_generic/Make/Graph/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_neural_generic.Make.Graph)

                                                          Module Make.Graph

                                                          module Neuron : sig ... end
                                                          type node = Make_Embedded(A).node = {
                                                          1. mutable name : string;
                                                          2. mutable prev : node array;
                                                          3. mutable next : node array;
                                                          4. mutable neuron : Neuron.neuron;
                                                          5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                          6. mutable network : network;
                                                          7. mutable train : bool;
                                                          }
                                                          and network = Make_Embedded(A).network = {
                                                          1. mutable nnid : string;
                                                          2. mutable size : int;
                                                          3. mutable roots : node array;
                                                          4. mutable outputs : node array;
                                                          5. mutable topo : node array;
                                                          }
                                                          val make_network : ?nnid:string -> int -> node array -> node array -> network
                                                          val make_node : +Graph (owl-base.Owl_neural_generic.Make.Graph)

                                                          Module Make.Graph

                                                          module Neuron : sig ... end
                                                          type node = Make_Embedded(A).node = {
                                                          1. mutable name : string;
                                                          2. mutable prev : node array;
                                                          3. mutable next : node array;
                                                          4. mutable neuron : Neuron.neuron;
                                                          5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                          6. mutable network : network;
                                                          7. mutable train : bool;
                                                          }
                                                          and network = Make_Embedded(A).network = {
                                                          1. mutable nnid : string;
                                                          2. mutable size : int;
                                                          3. mutable roots : node array;
                                                          4. mutable outputs : node array;
                                                          5. mutable topo : node array;
                                                          }
                                                          val make_network : ?nnid:string -> int -> node array -> node array -> network
                                                          val make_node : ?name:string -> ?train:bool -> node array -> diff --git a/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Linalg/index.html b/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Linalg/index.html index 7dd33af6d..c18d576a8 100644 --- a/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_generic.Make.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_generic.Make.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Mat/index.html b/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Mat/index.html index fcfabb6af..171c25200 100644 --- a/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Mat/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_generic.Make.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_neural_generic.Make.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Scalar/index.html b/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Scalar/index.html index 640569a26..f40d4266f 100644 --- a/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_generic.Make.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_neural_generic.Make.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make/argument-1-A/index.html b/docs/owl-base/Owl_neural_generic/Make/argument-1-A/index.html index b800d47a4..9da2a4868 100644 --- a/docs/owl-base/Owl_neural_generic/Make/argument-1-A/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_generic.Make.A)

                                                          Parameter Make.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_neural_generic.Make.A)

                                                          Parameter Make.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_generic/Make/index.html b/docs/owl-base/Owl_neural_generic/Make/index.html index 2aae21f38..e35b1b30e 100644 --- a/docs/owl-base/Owl_neural_generic/Make/index.html +++ b/docs/owl-base/Owl_neural_generic/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_neural_generic.Make)

                                                          Module Owl_neural_generic.Make

                                                          Parameters

                                                          Signature

                                                          include sig ... end
                                                          module Graph : sig ... end
                                                          module Optimise = Graph.Neuron.Optimise
                                                          module Init = Graph.Neuron.Init
                                                          module Activation = Graph.Neuron.Activation
                                                          module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                          +Make (owl-base.Owl_neural_generic.Make)

                                                          Module Owl_neural_generic.Make

                                                          Parameters

                                                          Signature

                                                          include sig ... end
                                                          module Graph : sig ... end
                                                          module Optimise = Graph.Neuron.Optimise
                                                          module Init = Graph.Neuron.Init
                                                          module Activation = Graph.Neuron.Activation
                                                          module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Activation/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Activation/index.html index 8d0ff04f7..293dbc6d1 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Activation/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Activation/index.html @@ -1,5 +1,5 @@ -Activation (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ = +Activation (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Activation.typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Activation.neuron_typ = diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Add/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Add/index.html index bea308653..59ccd1cef 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Add/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Add/index.html @@ -1,4 +1,4 @@ -Add (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = +Add (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Add.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AlphaDropout/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AlphaDropout/index.html index 7cc78efc7..bbec68fa7 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AlphaDropout/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AlphaDropout/index.html @@ -1,4 +1,4 @@ -AlphaDropout (owl-base.Owl_neural_generic.Make_Embedded.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = +AlphaDropout (owl-base.Owl_neural_generic.Make_Embedded.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).AlphaDropout.neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Average/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Average/index.html index f7485d981..9affeeacd 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Average/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Average/index.html @@ -1,4 +1,4 @@ -Average (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = +Average (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Average.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AvgPool1D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AvgPool1D/index.html index 9b13d3f53..d9e04d38f 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AvgPool1D/index.html @@ -1,4 +1,4 @@ -AvgPool1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = +AvgPool1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).AvgPool1D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AvgPool2D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AvgPool2D/index.html index e5baa7ad5..499527f15 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/AvgPool2D/index.html @@ -1,4 +1,4 @@ -AvgPool2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = +AvgPool2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).AvgPool2D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Concatenate/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Concatenate/index.html index 4bc56251b..121667859 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Concatenate/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Concatenate/index.html @@ -1,4 +1,4 @@ -Concatenate (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = +Concatenate (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Concatenate.neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv1D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv1D/index.html index 8a6457b39..6c2d40b45 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = +Conv1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Conv1D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv2D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv2D/index.html index cabd35fb3..f8847a789 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = +Conv2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Conv2D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv3D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv3D/index.html index 13ce09774..2a570a460 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = +Conv3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Conv3D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv1D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv1D/index.html index ad0b95a32..fefd7f45b 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = +DilatedConv1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).DilatedConv1D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv2D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv2D/index.html index 571cc1b1b..3e27d4e54 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = +DilatedConv2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).DilatedConv2D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv3D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv3D/index.html index 268ab37a1..8a93b351a 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = +DilatedConv3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).DilatedConv3D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Dot/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Dot/index.html index d049cbce1..71b13399f 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Dot/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Dot/index.html @@ -1,4 +1,4 @@ -Dot (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = +Dot (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Dot.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Dropout/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Dropout/index.html index 2ab7393ec..53695974e 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Dropout/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Dropout/index.html @@ -1,4 +1,4 @@ -Dropout (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = +Dropout (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Dropout.neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Embedding/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Embedding/index.html index 86903cf9c..5e3f09892 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Embedding/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Embedding/index.html @@ -1,4 +1,4 @@ -Embedding (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = +Embedding (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Embedding.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Flatten/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Flatten/index.html index 3c14878e5..5df8a77ec 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Flatten/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Flatten/index.html @@ -1,4 +1,4 @@ -Flatten (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = +Flatten (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Flatten.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/FullyConnected/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/FullyConnected/index.html index dbc06b872..9727f7166 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/FullyConnected/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/FullyConnected/index.html @@ -1,4 +1,4 @@ -FullyConnected (owl-base.Owl_neural_generic.Make_Embedded.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = +FullyConnected (owl-base.Owl_neural_generic.Make_Embedded.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).FullyConnected.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GRU/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GRU/index.html index 595ef77d1..245343682 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GRU/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GRU/index.html @@ -1,4 +1,4 @@ -GRU (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = +GRU (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).GRU.neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GaussianDropout/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GaussianDropout/index.html index 47aea2f8f..1417a521c 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GaussianDropout/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GaussianDropout/index.html @@ -1,4 +1,4 @@ -GaussianDropout (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = +GaussianDropout (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).GaussianDropout.neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GaussianNoise/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GaussianNoise/index.html index bb895d7b1..a06832d20 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GaussianNoise/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GaussianNoise/index.html @@ -1,4 +1,4 @@ -GaussianNoise (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = +GaussianNoise (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).GaussianNoise.neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalAvgPool1D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalAvgPool1D/index.html index 8357ad46b..a937480fc 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalAvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalAvgPool1D/index.html @@ -1,4 +1,4 @@ -GlobalAvgPool1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = +GlobalAvgPool1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).GlobalAvgPool1D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalAvgPool2D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalAvgPool2D/index.html index 05336e302..032afe6ca 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalAvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalAvgPool2D/index.html @@ -1,4 +1,4 @@ -GlobalAvgPool2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = +GlobalAvgPool2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).GlobalAvgPool2D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalMaxPool1D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalMaxPool1D/index.html index 4c8db0345..319388c2f 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalMaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalMaxPool1D/index.html @@ -1,4 +1,4 @@ -GlobalMaxPool1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = +GlobalMaxPool1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).GlobalMaxPool1D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalMaxPool2D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalMaxPool2D/index.html index 53cecbe6f..e722f8ec1 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalMaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/GlobalMaxPool2D/index.html @@ -1,4 +1,4 @@ -GlobalMaxPool2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = +GlobalMaxPool2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).GlobalMaxPool2D.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Init/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Init/index.html index c18bf759d..16769a4b1 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Init/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Init/index.html @@ -1,4 +1,4 @@ -Init (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ = +Init (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Init.typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                          val calc_fans : int array -> float * float
                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          val to_string : typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Input/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Input/index.html index bad395535..5c9d7fd97 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Input/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Input/index.html @@ -1,4 +1,4 @@ -Input (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = +Input (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Input.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LSTM/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LSTM/index.html index adecbd39a..a4a1deec7 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LSTM/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LSTM/index.html @@ -1,4 +1,4 @@ -LSTM (owl-base.Owl_neural_generic.Make_Embedded.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = +LSTM (owl-base.Owl_neural_generic.Make_Embedded.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).LSTM.neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Lambda/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Lambda/index.html index 5d73459ff..b723384a4 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Lambda/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = +Lambda (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Lambda.neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : ?out_shape:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LambdaArray/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LambdaArray/index.html index e8ca8dbd5..c9615bf80 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LambdaArray/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl-base.Owl_neural_generic.Make_Embedded.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = +LambdaArray (owl-base.Owl_neural_generic.Make_Embedded.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).LambdaArray.neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Linear/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Linear/index.html index e8260cf0e..b8f4a694c 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Linear/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Linear/index.html @@ -1,4 +1,4 @@ -Linear (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = +Linear (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Linear.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LinearNoBias/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LinearNoBias/index.html index 333b93491..45668f830 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LinearNoBias/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/LinearNoBias/index.html @@ -1,4 +1,4 @@ -LinearNoBias (owl-base.Owl_neural_generic.Make_Embedded.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = +LinearNoBias (owl-base.Owl_neural_generic.Make_Embedded.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).LinearNoBias.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Masking/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Masking/index.html index 4d0d6cdbc..efae28788 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Masking/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          +Masking (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Max/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Max/index.html index d14746956..2a628237b 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Max/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Max/index.html @@ -1,4 +1,4 @@ -Max (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = +Max (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Max.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/MaxPool1D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/MaxPool1D/index.html index 9e72d82c5..9a23b4fcc 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/MaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/MaxPool1D/index.html @@ -1,4 +1,4 @@ -MaxPool1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = +MaxPool1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).MaxPool1D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/MaxPool2D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/MaxPool2D/index.html index 9ffc7f695..bff1cdf11 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/MaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/MaxPool2D/index.html @@ -1,4 +1,4 @@ -MaxPool2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = +MaxPool2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).MaxPool2D.neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Mul/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Mul/index.html index 5d1c2a28d..ef0befe19 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Mul/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Mul/index.html @@ -1,4 +1,4 @@ -Mul (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = +Mul (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Mul.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Normalisation/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Normalisation/index.html index 9cd3750d4..1cb8d67d9 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Normalisation/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = +Normalisation (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Normalisation.neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?training:bool -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Linalg/index.html index 153526da5..083704036 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Mat/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Mat/index.html index 5078f1230..ee92c8b4d 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Scalar/index.html index 587b14eb8..385b483ca 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/index.html index 9a4b0ed44..b08d17b24 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          type arr = +A (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Arr/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Arr/index.html index 23eac7d95..9c217d8a5 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          +Arr (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/index.html index 7f48b3e6c..275efcbc9 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t
                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t
                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t
                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array
                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t
                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t
                                                          +Builder (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t
                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t
                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t
                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array
                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t
                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html index 8809ac323..a6c6c28ef 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          +Aiso (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html index 56157c95f..e549c0f46 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          +Piso (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html index d44c920a4..4dad095a1 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          +Siao (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 49751e113..43946339d 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sipo (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html index f39f6f254..0b40b8e0f 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          +Siso (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html index b33a93521..692fe79fc 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sito (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Linalg/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Linalg/index.html index e1eeec88d..d5ab9b307 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t
                                                          val logdet : t -> t
                                                          val chol : ?upper:bool -> t -> t
                                                          val qr : t -> t * t
                                                          val lq : t -> t * t
                                                          val svd : ?thin:bool -> t -> t * t * t
                                                          val sylvester : t -> t -> t -> t
                                                          val lyapunov : t -> t -> t
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t
                                                          val logdet : t -> t
                                                          val chol : ?upper:bool -> t -> t
                                                          val qr : t -> t * t
                                                          val lq : t -> t * t
                                                          val svd : ?thin:bool -> t -> t * t * t
                                                          val sylvester : t -> t -> t -> t
                                                          val lyapunov : t -> t -> t
                                                          val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Mat/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Mat/index.html index e1696186e..077f17572 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          +Mat (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Maths/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Maths/index.html index 7ba53d174..1c230dda9 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t
                                                          val (-) : t -> t -> t
                                                          val (*) : t -> t -> t
                                                          val (/) : t -> t -> t
                                                          val (*@) : t -> t -> t
                                                          val (**) : t -> t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val kron : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val pow : t -> t -> t
                                                          val atan2 : t -> t -> t
                                                          val min2 : t -> t -> t
                                                          val max2 : t -> t -> t
                                                          val cross_entropy : t -> t -> t
                                                          val inv : t -> t
                                                          val neg : t -> t
                                                          val abs : t -> t
                                                          val signum : t -> t
                                                          val floor : t -> t
                                                          val ceil : t -> t
                                                          val round : t -> t
                                                          val sqr : t -> t
                                                          val sqrt : t -> t
                                                          val log : t -> t
                                                          val log2 : t -> t
                                                          val log10 : t -> t
                                                          val exp : t -> t
                                                          val sin : t -> t
                                                          val cos : t -> t
                                                          val tan : t -> t
                                                          val sinh : t -> t
                                                          val cosh : t -> t
                                                          val tanh : t -> t
                                                          val asin : t -> t
                                                          val acos : t -> t
                                                          val atan : t -> t
                                                          val asinh : t -> t
                                                          val acosh : t -> t
                                                          val atanh : t -> t
                                                          val sum' : t -> t
                                                          val log_sum_exp' : t -> t
                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                          val sum_reduce : ?axis:int array -> t -> t
                                                          val mean : t -> t
                                                          val transpose : ?axis:int array -> t -> t
                                                          val swap : int -> int -> t -> t
                                                          val l1norm' : t -> t
                                                          val l2norm' : t -> t
                                                          val l2norm_sqr' : t -> t
                                                          val sigmoid : t -> t
                                                          val relu : t -> t
                                                          val dawsn : t -> t
                                                          val softplus : t -> t
                                                          val softsign : t -> t
                                                          val softmax : ?axis:int -> t -> t
                                                          val reshape : t -> int array -> t
                                                          val flatten : t -> t
                                                          val get_item : t -> int -> int -> t
                                                          val get_row : t -> int -> t
                                                          val concat : axis:int -> t -> t -> t
                                                          val split : axis:int -> int array -> t -> t array
                                                          val of_arrays : t array array -> t
                                                          val to_arrays : t -> t array array
                                                          val concatenate : axis:int -> t array -> t
                                                          val stack : axis:int -> t array -> t
                                                          val get_slice : int list list -> t -> t
                                                          val set_slice : int list list -> t -> t -> t
                                                          val get_fancy : Owl_types.index list -> t -> t
                                                          val set_fancy : Owl_types.index list -> t -> t -> t
                                                          val diag : ?k:int -> t -> t
                                                          val diagm : ?k:int -> t -> t
                                                          val trace : t -> t
                                                          val triu : ?k:int -> t -> t
                                                          val tril : ?k:int -> t -> t
                                                          +Maths (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t
                                                          val (-) : t -> t -> t
                                                          val (*) : t -> t -> t
                                                          val (/) : t -> t -> t
                                                          val (*@) : t -> t -> t
                                                          val (**) : t -> t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val kron : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val pow : t -> t -> t
                                                          val atan2 : t -> t -> t
                                                          val min2 : t -> t -> t
                                                          val max2 : t -> t -> t
                                                          val cross_entropy : t -> t -> t
                                                          val inv : t -> t
                                                          val neg : t -> t
                                                          val abs : t -> t
                                                          val signum : t -> t
                                                          val floor : t -> t
                                                          val ceil : t -> t
                                                          val round : t -> t
                                                          val sqr : t -> t
                                                          val sqrt : t -> t
                                                          val log : t -> t
                                                          val log2 : t -> t
                                                          val log10 : t -> t
                                                          val exp : t -> t
                                                          val sin : t -> t
                                                          val cos : t -> t
                                                          val tan : t -> t
                                                          val sinh : t -> t
                                                          val cosh : t -> t
                                                          val tanh : t -> t
                                                          val asin : t -> t
                                                          val acos : t -> t
                                                          val atan : t -> t
                                                          val asinh : t -> t
                                                          val acosh : t -> t
                                                          val atanh : t -> t
                                                          val sum' : t -> t
                                                          val log_sum_exp' : t -> t
                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                          val sum_reduce : ?axis:int array -> t -> t
                                                          val mean : t -> t
                                                          val transpose : ?axis:int array -> t -> t
                                                          val swap : int -> int -> t -> t
                                                          val l1norm' : t -> t
                                                          val l2norm' : t -> t
                                                          val l2norm_sqr' : t -> t
                                                          val sigmoid : t -> t
                                                          val relu : t -> t
                                                          val dawsn : t -> t
                                                          val softplus : t -> t
                                                          val softsign : t -> t
                                                          val softmax : ?axis:int -> t -> t
                                                          val reshape : t -> int array -> t
                                                          val flatten : t -> t
                                                          val get_item : t -> int -> int -> t
                                                          val get_row : t -> int -> t
                                                          val concat : axis:int -> t -> t -> t
                                                          val split : axis:int -> int array -> t -> t array
                                                          val of_arrays : t array array -> t
                                                          val to_arrays : t -> t array array
                                                          val concatenate : axis:int -> t array -> t
                                                          val stack : axis:int -> t array -> t
                                                          val get_slice : int list list -> t -> t
                                                          val set_slice : int list list -> t -> t -> t
                                                          val get_fancy : Owl_types.index list -> t -> t
                                                          val set_fancy : Owl_types.index list -> t -> t -> t
                                                          val diag : ?k:int -> t -> t
                                                          val diagm : ?k:int -> t -> t
                                                          val trace : t -> t
                                                          val triu : ?k:int -> t -> t
                                                          val tril : ?k:int -> t -> t
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/NN/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/NN/index.html index 53e6799fe..0dffc939b 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/NN/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t
                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val dilated_conv1d : +NN (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t
                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                          val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/index.html index a18240763..1523085e7 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Algodiff/index.html @@ -1,4 +1,4 @@ -Algodiff (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          module A : sig ... end
                                                          type t = +Algodiff (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          module A : sig ... end
                                                          type t = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Optimise.Algodiff.t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          val tag : unit -> int
                                                          val primal : t -> t
                                                          val primal' : t -> t
                                                          val zero : t -> t
                                                          val reset_zero : t -> t
                                                          val tangent : t -> t
                                                          val adjref : t -> t Stdlib.ref
                                                          val adjval : t -> t
                                                          val shape : t -> int array
                                                          val is_float : t -> bool
                                                          val is_arr : t -> bool
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val numel : t -> int
                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                          val clip_by_l2norm : A.elt -> t -> t
                                                          val copy_primal' : t -> t
                                                          val tile : t -> int array -> t
                                                          val repeat : t -> int array -> t
                                                          val pack_elt : A.elt -> t
                                                          val unpack_elt : t -> A.elt
                                                          val pack_flt : float -> t
                                                          val _f : float -> t
                                                          val unpack_flt : t -> float
                                                          val pack_arr : A.arr -> t
                                                          val unpack_arr : t -> A.arr
                                                          val deep_info : t -> string
                                                          val type_info : t -> string
                                                          val error_binop : string -> t -> t -> 'a
                                                          val error_uniop : string -> t -> 'a
                                                          val make_forward : t -> t -> int -> t
                                                          val make_reverse : t -> int -> t
                                                          val reverse_prop : t -> t -> unit
                                                          val diff : (t -> t) -> t -> t
                                                          val diff' : (t -> t) -> t -> t * t
                                                          val grad : (t -> t) -> t -> t
                                                          val grad' : (t -> t) -> t -> t * t
                                                          val jacobian : (t -> t) -> t -> t
                                                          val jacobian' : (t -> t) -> t -> t * t
                                                          val jacobianv : (t -> t) -> t -> t -> t
                                                          val jacobianv' : (t -> t) -> t -> t -> t * t
                                                          val jacobianTv : (t -> t) -> t -> t -> t
                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                          val hessian : (t -> t) -> t -> t
                                                          val hessian' : (t -> t) -> t -> t * t
                                                          val hessianv : (t -> t) -> t -> t -> t
                                                          val hessianv' : (t -> t) -> t -> t -> t * t
                                                          val laplacian : (t -> t) -> t -> t
                                                          val laplacian' : (t -> t) -> t -> t * t
                                                          val gradhessian : (t -> t) -> t -> t * t
                                                          val gradhessian' : (t -> t) -> t -> t * t * t
                                                          val gradhessianv : (t -> t) -> t -> t -> t * t
                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                          module Builder : sig ... end
                                                          module Maths : sig ... end
                                                          module Linalg : sig ... end
                                                          module NN : sig ... end
                                                          module Mat : sig ... end
                                                          module Arr : sig ... end
                                                          val to_trace : t list -> string
                                                          val to_dot : t list -> string
                                                          val pp_num : Stdlib.Format.formatter -> t -> unit
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Batch/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Batch/index.html index 8401767b6..ffc757a97 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Batch/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Batch/index.html @@ -1,4 +1,4 @@ -Batch (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          type typ = +Batch (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val batches : typ -> Algodiff.t -> int
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Checkpoint/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Checkpoint/index.html index 733f52737..bbce8c390 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Checkpoint/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          type state = +Checkpoint (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          type state = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Optimise.Checkpoint.state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }
                                                          type typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Optimise.Checkpoint.typ = diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Clipping/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Clipping/index.html index 1c188e5cd..14567fc05 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Clipping/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Clipping/index.html @@ -1,4 +1,4 @@ -Clipping (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          type typ = +Clipping (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Gradient/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Gradient/index.html index c6654b92b..e60aa75b9 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Gradient/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          type typ = +Gradient (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          type typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Optimise.Gradient.typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton
                                                          val run : typ -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Learning_Rate/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Learning_Rate/index.html index 4819edf80..d393402b8 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Learning_Rate/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          type typ = +Learning_Rate (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          type typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Optimise.Learning_Rate.typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                          val default : typ -> typ
                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Loss/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Loss/index.html index ed7044c95..45ab5ae5a 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Loss/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Loss/index.html @@ -1,4 +1,4 @@ -Loss (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          type typ = +Loss (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Momentum/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Momentum/index.html index f2333ec58..ff506ca86 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Momentum/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Momentum/index.html @@ -1,4 +1,4 @@ -Momentum (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          type typ = +Momentum (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Params/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Params/index.html index 2b7e50891..b164fe4d4 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Params/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          type typ = +Params (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          type typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Optimise.Params.typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }
                                                          val default : unit -> typ
                                                          val config : ?batch:Batch.typ -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Regularisation/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Regularisation/index.html index 1b52bac8d..0b0a9ee65 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Regularisation/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          type typ = +Regularisation (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          type typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Optimise.Regularisation.typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Stopping/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Stopping/index.html index d7db36d5c..4384da3cf 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Stopping/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Stopping/index.html @@ -1,4 +1,4 @@ -Stopping (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          type typ = +Stopping (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          val run : typ -> float -> bool
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Utils/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Utils/index.html index db21989ec..feda61644 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Utils/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          val sample_num : Algodiff.t -> int
                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val get_chunk : +Utils (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          val sample_num : Algodiff.t -> int
                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/index.html index 2b4f790fd..aeb4d925a 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Algodiff : sig ... end
                                                          module Utils : sig ... end
                                                          module Learning_Rate : sig ... end
                                                          module Batch : sig ... end
                                                          module Loss : sig ... end
                                                          module Gradient : sig ... end
                                                          module Momentum : sig ... end
                                                          module Regularisation : sig ... end
                                                          module Clipping : sig ... end
                                                          module Stopping : sig ... end
                                                          module Checkpoint : sig ... end
                                                          module Params : sig ... end
                                                          val minimise_weight : +Optimise (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Algodiff : sig ... end
                                                          module Utils : sig ... end
                                                          module Learning_Rate : sig ... end
                                                          module Batch : sig ... end
                                                          module Loss : sig ... end
                                                          module Gradient : sig ... end
                                                          module Momentum : sig ... end
                                                          module Regularisation : sig ... end
                                                          module Clipping : sig ... end
                                                          module Stopping : sig ... end
                                                          module Checkpoint : sig ... end
                                                          module Params : sig ... end
                                                          val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding1D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding1D/index.html index 89b2f7ff7..2efc15f43 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          +Padding1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding2D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding2D/index.html index 26f7b8fa3..16e4da63d 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding2D/index.html @@ -1,4 +1,4 @@ -Padding2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = +Padding2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Padding2D.neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding3D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding3D/index.html index 505fca10e..19791ae2e 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          +Padding3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Recurrent/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Recurrent/index.html index 6cc319b40..4a28f100c 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Recurrent/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = +Recurrent (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Recurrent.neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Reshape/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Reshape/index.html index 255a8d629..dc1131885 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Reshape/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Reshape/index.html @@ -1,4 +1,4 @@ -Reshape (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = +Reshape (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Reshape.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Slice/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Slice/index.html index 4b9f873c4..4aa0979b9 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Slice/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/Slice/index.html @@ -1,4 +1,4 @@ -Slice (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = +Slice (owl-base.Owl_neural_generic.Make_Embedded.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).Slice.neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }
                                                          val create : int list list -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv1D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv1D/index.html index 67ffaff89..4c9d2f6dd 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = +TransposeConv1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).TransposeConv1D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv2D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv2D/index.html index d0686b2af..85ca53ea1 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = +TransposeConv2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).TransposeConv2D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv3D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv3D/index.html index c6ebe08c8..22ba5bc90 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = +TransposeConv3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).TransposeConv3D.neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling1D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling1D/index.html index d2afdf113..992064581 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling1D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          +UpSampling1D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling2D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling2D/index.html index c0b9914ff..66c2e1d37 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling2D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling2D/index.html @@ -1,4 +1,4 @@ -UpSampling2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = +UpSampling2D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).UpSampling2D.neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling3D/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling3D/index.html index fa582f37e..c15490bca 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling3D/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          +UpSampling3D (owl-base.Owl_neural_generic.Make_Embedded.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/index.html index df6652440..8789d51fb 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/Neuron/index.html @@ -1,4 +1,4 @@ -Neuron (owl-base.Owl_neural_generic.Make_Embedded.Neuron)

                                                          Module Make_Embedded.Neuron

                                                          module Optimise : sig ... end
                                                          module Init : sig ... end
                                                          module Input : sig ... end
                                                          module Activation : sig ... end
                                                          module Linear : sig ... end
                                                          module LinearNoBias : sig ... end
                                                          module Recurrent : sig ... end
                                                          module LSTM : sig ... end
                                                          module GRU : sig ... end
                                                          module Conv1D : sig ... end
                                                          module Conv2D : sig ... end
                                                          module Conv3D : sig ... end
                                                          module DilatedConv1D : sig ... end
                                                          module DilatedConv2D : sig ... end
                                                          module DilatedConv3D : sig ... end
                                                          module TransposeConv1D : sig ... end
                                                          module TransposeConv2D : sig ... end
                                                          module TransposeConv3D : sig ... end
                                                          module FullyConnected : sig ... end
                                                          module MaxPool1D : sig ... end
                                                          module MaxPool2D : sig ... end
                                                          module AvgPool1D : sig ... end
                                                          module AvgPool2D : sig ... end
                                                          module GlobalMaxPool1D : sig ... end
                                                          module GlobalMaxPool2D : sig ... end
                                                          module GlobalAvgPool1D : sig ... end
                                                          module GlobalAvgPool2D : sig ... end
                                                          module UpSampling1D : sig ... end
                                                          module UpSampling2D : sig ... end
                                                          module UpSampling3D : sig ... end
                                                          module Padding1D : sig ... end
                                                          module Padding2D : sig ... end
                                                          module Padding3D : sig ... end
                                                          module Lambda : sig ... end
                                                          module LambdaArray : sig ... end
                                                          module Dropout : sig ... end
                                                          module Reshape : sig ... end
                                                          module Flatten : sig ... end
                                                          module Slice : sig ... end
                                                          module Add : sig ... end
                                                          module Mul : sig ... end
                                                          module Dot : sig ... end
                                                          module Max : sig ... end
                                                          module Average : sig ... end
                                                          module Concatenate : sig ... end
                                                          module Normalisation : sig ... end
                                                          module GaussianNoise : sig ... end
                                                          module GaussianDropout : sig ... end
                                                          module AlphaDropout : sig ... end
                                                          module Embedding : sig ... end
                                                          module Masking : sig ... end
                                                          type neuron = +Neuron (owl-base.Owl_neural_generic.Make_Embedded.Neuron)

                                                          Module Make_Embedded.Neuron

                                                          module Optimise : sig ... end
                                                          module Init : sig ... end
                                                          module Input : sig ... end
                                                          module Activation : sig ... end
                                                          module Linear : sig ... end
                                                          module LinearNoBias : sig ... end
                                                          module Recurrent : sig ... end
                                                          module LSTM : sig ... end
                                                          module GRU : sig ... end
                                                          module Conv1D : sig ... end
                                                          module Conv2D : sig ... end
                                                          module Conv3D : sig ... end
                                                          module DilatedConv1D : sig ... end
                                                          module DilatedConv2D : sig ... end
                                                          module DilatedConv3D : sig ... end
                                                          module TransposeConv1D : sig ... end
                                                          module TransposeConv2D : sig ... end
                                                          module TransposeConv3D : sig ... end
                                                          module FullyConnected : sig ... end
                                                          module MaxPool1D : sig ... end
                                                          module MaxPool2D : sig ... end
                                                          module AvgPool1D : sig ... end
                                                          module AvgPool2D : sig ... end
                                                          module GlobalMaxPool1D : sig ... end
                                                          module GlobalMaxPool2D : sig ... end
                                                          module GlobalAvgPool1D : sig ... end
                                                          module GlobalAvgPool2D : sig ... end
                                                          module UpSampling1D : sig ... end
                                                          module UpSampling2D : sig ... end
                                                          module UpSampling3D : sig ... end
                                                          module Padding1D : sig ... end
                                                          module Padding2D : sig ... end
                                                          module Padding3D : sig ... end
                                                          module Lambda : sig ... end
                                                          module LambdaArray : sig ... end
                                                          module Dropout : sig ... end
                                                          module Reshape : sig ... end
                                                          module Flatten : sig ... end
                                                          module Slice : sig ... end
                                                          module Add : sig ... end
                                                          module Mul : sig ... end
                                                          module Dot : sig ... end
                                                          module Max : sig ... end
                                                          module Average : sig ... end
                                                          module Concatenate : sig ... end
                                                          module Normalisation : sig ... end
                                                          module GaussianNoise : sig ... end
                                                          module GaussianDropout : sig ... end
                                                          module AlphaDropout : sig ... end
                                                          module Embedding : sig ... end
                                                          module Masking : sig ... end
                                                          type neuron = Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A))).neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                          val get_in_out_shape : neuron -> int array * int array
                                                          val get_in_shape : neuron -> int array
                                                          val get_out_shape : neuron -> int array
                                                          val connect : int array array -> neuron -> unit
                                                          val init : neuron -> unit
                                                          val reset : neuron -> unit
                                                          val mktag : int -> neuron -> unit
                                                          val mkpar : neuron -> Optimise.Algodiff.t array
                                                          val mkpri : neuron -> Optimise.Algodiff.t array
                                                          val mkadj : neuron -> Optimise.Algodiff.t array
                                                          val update : neuron -> Optimise.Algodiff.t array -> unit
                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit
                                                          val save_weights : neuron -> Optimise.Algodiff.t array
                                                          val copy : neuron -> neuron
                                                          val to_string : neuron -> string
                                                          val to_name : neuron -> string
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Linalg/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Linalg/index.html index 9de7dd131..1f27fc498 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_generic.Make_Embedded.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_generic.Make_Embedded.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Mat/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Mat/index.html index 3fcb95f2e..eda949784 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Mat/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_generic.Make_Embedded.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_neural_generic.Make_Embedded.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Scalar/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Scalar/index.html index a95c9bc5b..6b6c6167e 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_generic.Make_Embedded.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_neural_generic.Make_Embedded.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/index.html index 972a9b108..eb98750b2 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_generic.Make_Embedded.A)

                                                          Parameter Make_Embedded.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_neural_generic.Make_Embedded.A)

                                                          Parameter Make_Embedded.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_generic/Make_Embedded/index.html b/docs/owl-base/Owl_neural_generic/Make_Embedded/index.html index f50728b6a..9833d144e 100644 --- a/docs/owl-base/Owl_neural_generic/Make_Embedded/index.html +++ b/docs/owl-base/Owl_neural_generic/Make_Embedded/index.html @@ -1,5 +1,5 @@ -Make_Embedded (owl-base.Owl_neural_generic.Make_Embedded)

                                                          Module Owl_neural_generic.Make_Embedded

                                                          Parameters

                                                          Signature

                                                          include sig ... end
                                                          module Neuron : sig ... end
                                                          type node = +Make_Embedded (owl-base.Owl_neural_generic.Make_Embedded)

                                                          Module Owl_neural_generic.Make_Embedded

                                                          Parameters

                                                          Signature

                                                          include sig ... end
                                                          module Neuron : sig ... end
                                                          type node = Owl_neural_graph.Make(Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)))).node = {
                                                          1. mutable name : string;
                                                          2. mutable prev : node array;
                                                          3. mutable next : node array;
                                                          4. mutable neuron : Neuron.neuron;
                                                          5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                          6. mutable network : network;
                                                          7. mutable train : bool;
                                                          }
                                                          and network = Owl_neural_graph.Make(Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)))).network = diff --git a/docs/owl-base/Owl_neural_generic/index.html b/docs/owl-base/Owl_neural_generic/index.html index 120b921f1..c4cef3cc8 100644 --- a/docs/owl-base/Owl_neural_generic/index.html +++ b/docs/owl-base/Owl_neural_generic/index.html @@ -1,2 +1,2 @@ -Owl_neural_generic (owl-base.Owl_neural_generic)

                                                          Module Owl_neural_generic

                                                          Functor to create neural networks of different precision.

                                                          module Flatten (Graph : Owl_neural_graph_sig.Sig) : sig ... end
                                                          module Make (A : Owl_types_ndarray_algodiff.Sig) : sig ... end
                                                          +Owl_neural_generic (owl-base.Owl_neural_generic)

                                                          Module Owl_neural_generic

                                                          Functor to create neural networks of different precision.

                                                          module Flatten (Graph : Owl_neural_graph_sig.Sig) : sig ... end
                                                          module Make (A : Owl_types_ndarray_algodiff.Sig) : sig ... end
                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Activation/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Activation/index.html index 42956b824..7054d6fa7 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Activation/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Activation/index.html @@ -1,2 +1,2 @@ -Activation (owl-base.Owl_neural_graph.Make.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                            (*

                                                            Types of activation functions.

                                                            *)
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t

                                                          Run one specific activation function.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val activation_to_string : typ -> string

                                                          Return the name of a specific activation function.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Activation (owl-base.Owl_neural_graph.Make.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                            (*

                                                            Types of activation functions.

                                                            *)
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t

                                                          Run one specific activation function.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val activation_to_string : typ -> string

                                                          Return the name of a specific activation function.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Add/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Add/index.html index ac1dcf365..6f62e3a90 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Add/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Add/index.html @@ -1,2 +1,2 @@ -Add (owl-base.Owl_neural_graph.Make.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Add (owl-base.Owl_neural_graph.Make.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AlphaDropout/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AlphaDropout/index.html index bfdf6fa95..affbe56dd 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AlphaDropout/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AlphaDropout/index.html @@ -1,2 +1,2 @@ -AlphaDropout (owl-base.Owl_neural_graph.Make.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AlphaDropout (owl-base.Owl_neural_graph.Make.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Average/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Average/index.html index a2fd2a611..f82d19bbc 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Average/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Average/index.html @@ -1,2 +1,2 @@ -Average (owl-base.Owl_neural_graph.Make.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Average (owl-base.Owl_neural_graph.Make.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AvgPool1D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AvgPool1D/index.html index ceae32aa2..429579454 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AvgPool1D/index.html @@ -1,2 +1,2 @@ -AvgPool1D (owl-base.Owl_neural_graph.Make.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AvgPool1D (owl-base.Owl_neural_graph.Make.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AvgPool2D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AvgPool2D/index.html index 6d62cfb2c..66f95b560 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/AvgPool2D/index.html @@ -1,2 +1,2 @@ -AvgPool2D (owl-base.Owl_neural_graph.Make.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AvgPool2D (owl-base.Owl_neural_graph.Make.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Concatenate/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Concatenate/index.html index d20129098..f692ac453 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Concatenate/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Concatenate/index.html @@ -1,2 +1,2 @@ -Concatenate (owl-base.Owl_neural_graph.Make.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Concatenate (owl-base.Owl_neural_graph.Make.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv1D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv1D/index.html index 6c32a0f4a..bfea46749 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv1D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl-base.Owl_neural_graph.Make.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv1D (owl-base.Owl_neural_graph.Make.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv2D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv2D/index.html index b3979a5dd..a366b910b 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv2D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl-base.Owl_neural_graph.Make.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv2D (owl-base.Owl_neural_graph.Make.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv3D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv3D/index.html index 96c9c6e69..5778fa093 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv3D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl-base.Owl_neural_graph.Make.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv3D (owl-base.Owl_neural_graph.Make.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv1D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv1D/index.html index 68decf9f6..006f99295 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv1D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl-base.Owl_neural_graph.Make.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv1D (owl-base.Owl_neural_graph.Make.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv2D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv2D/index.html index 4c5589ca2..bc2b51bb7 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv2D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl-base.Owl_neural_graph.Make.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv2D (owl-base.Owl_neural_graph.Make.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv3D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv3D/index.html index ac616dad2..1c035dd8b 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv3D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl-base.Owl_neural_graph.Make.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv3D (owl-base.Owl_neural_graph.Make.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Dot/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Dot/index.html index 3305a68b7..c765c137e 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Dot/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Dot/index.html @@ -1,2 +1,2 @@ -Dot (owl-base.Owl_neural_graph.Make.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Dot (owl-base.Owl_neural_graph.Make.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Dropout/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Dropout/index.html index f1e0b64e3..c5514fa00 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Dropout/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Dropout/index.html @@ -1,2 +1,2 @@ -Dropout (owl-base.Owl_neural_graph.Make.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Dropout (owl-base.Owl_neural_graph.Make.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Embedding/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Embedding/index.html index 19916309b..1342d5895 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Embedding/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Embedding/index.html @@ -1,2 +1,2 @@ -Embedding (owl-base.Owl_neural_graph.Make.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Embedding (owl-base.Owl_neural_graph.Make.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Flatten/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Flatten/index.html index 8e3847624..885aa0eb8 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Flatten/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Flatten/index.html @@ -1,2 +1,2 @@ -Flatten (owl-base.Owl_neural_graph.Make.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Flatten (owl-base.Owl_neural_graph.Make.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/FullyConnected/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/FullyConnected/index.html index 7e657a6da..00937b276 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/FullyConnected/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/FullyConnected/index.html @@ -1,2 +1,2 @@ -FullyConnected (owl-base.Owl_neural_graph.Make.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +FullyConnected (owl-base.Owl_neural_graph.Make.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GRU/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GRU/index.html index 6dba9eceb..e7a8d4205 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GRU/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GRU/index.html @@ -1,2 +1,2 @@ -GRU (owl-base.Owl_neural_graph.Make.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GRU (owl-base.Owl_neural_graph.Make.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GaussianDropout/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GaussianDropout/index.html index 462aa305e..a61526dcc 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GaussianDropout/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GaussianDropout/index.html @@ -1,2 +1,2 @@ -GaussianDropout (owl-base.Owl_neural_graph.Make.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GaussianDropout (owl-base.Owl_neural_graph.Make.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GaussianNoise/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GaussianNoise/index.html index 45821b770..31611445b 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GaussianNoise/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GaussianNoise/index.html @@ -1,2 +1,2 @@ -GaussianNoise (owl-base.Owl_neural_graph.Make.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GaussianNoise (owl-base.Owl_neural_graph.Make.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalAvgPool1D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalAvgPool1D/index.html index 06c3cccbc..6b227ec4e 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalAvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalAvgPool1D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool1D (owl-base.Owl_neural_graph.Make.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalAvgPool1D (owl-base.Owl_neural_graph.Make.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalAvgPool2D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalAvgPool2D/index.html index 5ed0841ac..23ac4ce8d 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalAvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalAvgPool2D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool2D (owl-base.Owl_neural_graph.Make.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalAvgPool2D (owl-base.Owl_neural_graph.Make.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalMaxPool1D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalMaxPool1D/index.html index be6958418..0ec1cb037 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalMaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalMaxPool1D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool1D (owl-base.Owl_neural_graph.Make.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalMaxPool1D (owl-base.Owl_neural_graph.Make.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalMaxPool2D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalMaxPool2D/index.html index caec08bb4..a63317d95 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalMaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/GlobalMaxPool2D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool2D (owl-base.Owl_neural_graph.Make.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalMaxPool2D (owl-base.Owl_neural_graph.Make.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Init/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Init/index.html index 46ed0faea..aa3d6df2e 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Init/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Init/index.html @@ -1,2 +1,2 @@ -Init (owl-base.Owl_neural_graph.Make.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                            (*

                                                            Initialisation types

                                                            *)
                                                          val calc_fans : int array -> float * float

                                                          Calculate fan-in and fan-out of weights.

                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Init (owl-base.Owl_neural_graph.Make.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                            (*

                                                            Initialisation types

                                                            *)
                                                          val calc_fans : int array -> float * float

                                                          Calculate fan-in and fan-out of weights.

                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Input/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Input/index.html index a3699a70f..3ddbe0264 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Input/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Input/index.html @@ -1,2 +1,2 @@ -Input (owl-base.Owl_neural_graph.Make.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Input (owl-base.Owl_neural_graph.Make.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LSTM/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LSTM/index.html index fa64f5dd2..07b1c08cc 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LSTM/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LSTM/index.html @@ -1,2 +1,2 @@ -LSTM (owl-base.Owl_neural_graph.Make.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +LSTM (owl-base.Owl_neural_graph.Make.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Lambda/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Lambda/index.html index acbed889f..5d91cc6c7 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Lambda/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl-base.Owl_neural_graph.Make.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Lambda (owl-base.Owl_neural_graph.Make.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?out_shape:int array -> (Optimise.Algodiff.t -> Optimise.Algodiff.t) -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LambdaArray/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LambdaArray/index.html index 86917dac1..b9a4a8b87 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LambdaArray/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl-base.Owl_neural_graph.Make.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +LambdaArray (owl-base.Owl_neural_graph.Make.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> (Optimise.Algodiff.t array -> Optimise.Algodiff.t) -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Linear/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Linear/index.html index f17178e15..0447e2a4b 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Linear/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Linear/index.html @@ -1,2 +1,2 @@ -Linear (owl-base.Owl_neural_graph.Make.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Linear (owl-base.Owl_neural_graph.Make.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LinearNoBias/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LinearNoBias/index.html index 91bc1a143..ace3c9e2b 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LinearNoBias/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/LinearNoBias/index.html @@ -1,2 +1,2 @@ -LinearNoBias (owl-base.Owl_neural_graph.Make.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +LinearNoBias (owl-base.Owl_neural_graph.Make.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Masking/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Masking/index.html index 150fc8b30..7ec4955ef 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Masking/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl-base.Owl_neural_graph.Make.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          +Masking (owl-base.Owl_neural_graph.Make.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Max/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Max/index.html index 7ed4c9a59..9ed0b985f 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Max/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Max/index.html @@ -1,2 +1,2 @@ -Max (owl-base.Owl_neural_graph.Make.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Max (owl-base.Owl_neural_graph.Make.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/MaxPool1D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/MaxPool1D/index.html index a685a81e3..8d32c3d65 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/MaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/MaxPool1D/index.html @@ -1,2 +1,2 @@ -MaxPool1D (owl-base.Owl_neural_graph.Make.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +MaxPool1D (owl-base.Owl_neural_graph.Make.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/MaxPool2D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/MaxPool2D/index.html index 06e71217b..658c98e50 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/MaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/MaxPool2D/index.html @@ -1,2 +1,2 @@ -MaxPool2D (owl-base.Owl_neural_graph.Make.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +MaxPool2D (owl-base.Owl_neural_graph.Make.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Mul/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Mul/index.html index 49b50dd05..050b18ebc 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Mul/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Mul/index.html @@ -1,2 +1,2 @@ -Mul (owl-base.Owl_neural_graph.Make.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Mul (owl-base.Owl_neural_graph.Make.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Normalisation/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Normalisation/index.html index 8bfc3db06..1dfe256ef 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Normalisation/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl-base.Owl_neural_graph.Make.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Normalisation (owl-base.Owl_neural_graph.Make.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?training:bool -> ?decay:float -> ?mu:Optimise.Algodiff.A.arr -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Linalg/index.html index 6c6faeaf0..65ddae4bc 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Mat/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Mat/index.html index ba88944cc..a67065244 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Scalar/index.html index dec3d9a19..506589d2c 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/index.html index aa70283da..215200fac 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Arr/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Arr/index.html index 9274f1088..a3e4cebec 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          +Arr (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/index.html index 49dc1f299..e0944b00f 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          +Builder (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html index 48d9d83ea..7a0cdb2f0 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          +Aiso (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html index 90e26c8f6..59a27014e 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          +Piso (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html index 1d2a8e0f8..df7b3ba0c 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          +Siao (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 16f4e6b89..80fb389de 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sipo (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html index 093be9a78..40873aa86 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          +Siso (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html index 72efac155..27abafff4 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sito (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Linalg/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Linalg/index.html index f94a632de..c61b9c5be 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Mat/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Mat/index.html index 2fb52dc5b..68161997b 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          +Mat (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Maths/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Maths/index.html index 4fe76dee1..f3ed4b4f7 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          +Maths (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/NN/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/NN/index.html index 937e6d399..249497f3b 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/NN/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : +NN (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/index.html index 5ef87c73c..b3b343e1c 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Algodiff/index.html @@ -1,5 +1,5 @@ -Algodiff (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig +Algodiff (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Batch/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Batch/index.html index 260cb8777..b0a03bfbf 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Batch/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Batch (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Checkpoint/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Checkpoint/index.html index 075018afa..1ee9eaa91 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Checkpoint/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Checkpoint (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Clipping/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Clipping/index.html index 1a0fdefba..f9e0a38b2 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Clipping/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Clipping (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Gradient/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Gradient/index.html index b30661630..ff67f76ab 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Gradient/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : +Gradient (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Learning_Rate/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Learning_Rate/index.html index 323897bf8..db15239cd 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Learning_Rate/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Learning_Rate (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Loss/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Loss/index.html index b3a4ca0ab..cb32a0e23 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Loss/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Loss (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Momentum/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Momentum/index.html index 6bea7466f..039b2a0ce 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Momentum/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Momentum (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Params/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Params/index.html index d5c89afdf..2bf8cb533 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Params/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : +Params (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Regularisation/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Regularisation/index.html index 4a33e4541..fd77b0f84 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Regularisation/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Regularisation (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Stopping/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Stopping/index.html index c5aa14686..c465113ee 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Stopping/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Stopping (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Utils/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Utils/index.html index bc30b0d07..3ac02a7da 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Utils/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : +Utils (owl-base.Owl_neural_graph.Make.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/index.html index 01e1b68e7..adc1c2fcd 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl-base.Owl_neural_graph.Make.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : +Optimise (owl-base.Owl_neural_graph.Make.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> @@ -28,4 +28,4 @@ (string -> unit) -> Algodiff.t -> Algodiff.t -> - Checkpoint.state

                                                          TODO

                                                          + Checkpoint.state

                                                          This function is minimize the weights in a compiled neural network of graph structure.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding1D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding1D/index.html index 853082820..929aa3629 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding1D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl-base.Owl_neural_graph.Make.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          +Padding1D (owl-base.Owl_neural_graph.Make.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding2D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding2D/index.html index e8e8076e4..2bc69038f 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding2D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding2D/index.html @@ -1,2 +1,2 @@ -Padding2D (owl-base.Owl_neural_graph.Make.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Padding2D (owl-base.Owl_neural_graph.Make.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding3D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding3D/index.html index 3004bbd54..1570b4a60 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding3D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl-base.Owl_neural_graph.Make.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          +Padding3D (owl-base.Owl_neural_graph.Make.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Recurrent/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Recurrent/index.html index 2dd413e83..0e6dfddf7 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Recurrent/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl-base.Owl_neural_graph.Make.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Recurrent (owl-base.Owl_neural_graph.Make.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Reshape/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Reshape/index.html index b53a3a580..8687e3016 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Reshape/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Reshape/index.html @@ -1,2 +1,2 @@ -Reshape (owl-base.Owl_neural_graph.Make.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Reshape (owl-base.Owl_neural_graph.Make.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Slice/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Slice/index.html index f8b843e8a..77233dbbc 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Slice/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/Slice/index.html @@ -1,2 +1,2 @@ -Slice (owl-base.Owl_neural_graph.Make.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }

                                                          Neuron type definition.

                                                          val create : int list list -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Slice (owl-base.Owl_neural_graph.Make.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }

                                                          Neuron type definition.

                                                          val create : int list list -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv1D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv1D/index.html index 2725ab2e3..0df812449 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv1D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl-base.Owl_neural_graph.Make.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv1D (owl-base.Owl_neural_graph.Make.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv2D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv2D/index.html index 2efd277a8..1a8ea46bb 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv2D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl-base.Owl_neural_graph.Make.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv2D (owl-base.Owl_neural_graph.Make.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv3D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv3D/index.html index 41ad54992..54bf97438 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv3D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl-base.Owl_neural_graph.Make.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv3D (owl-base.Owl_neural_graph.Make.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling1D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling1D/index.html index 24ade9f9b..0797bcd37 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling1D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl-base.Owl_neural_graph.Make.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          +UpSampling1D (owl-base.Owl_neural_graph.Make.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling2D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling2D/index.html index c6a7c4384..0aab21376 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling2D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling2D/index.html @@ -1,2 +1,2 @@ -UpSampling2D (owl-base.Owl_neural_graph.Make.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +UpSampling2D (owl-base.Owl_neural_graph.Make.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling3D/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling3D/index.html index e6f201b2e..1ed40fe93 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling3D/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl-base.Owl_neural_graph.Make.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          +UpSampling3D (owl-base.Owl_neural_graph.Make.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/index.html b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/index.html index 9f0a1c9dc..2212ba859 100644 --- a/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/argument-1-Neuron/index.html @@ -1,2 +1,2 @@ -Neuron (owl-base.Owl_neural_graph.Make.Neuron)

                                                          Parameter Make.Neuron

                                                          Init neuron
                                                          module Init : sig ... end
                                                          Input neuron
                                                          module Input : sig ... end
                                                          Activation neuron
                                                          module Activation : sig ... end
                                                          Linear neuron
                                                          module Linear : sig ... end
                                                          LinearNoBias neuron
                                                          module LinearNoBias : sig ... end
                                                          Recurrent neuron
                                                          module Recurrent : sig ... end
                                                          LSTM neuron
                                                          module LSTM : sig ... end
                                                          GRU neuron
                                                          module GRU : sig ... end
                                                          Conv1D neuron
                                                          module Conv1D : sig ... end
                                                          Conv2D neuron
                                                          module Conv2D : sig ... end
                                                          Conv3D neuron
                                                          module Conv3D : sig ... end
                                                          DilatedConv1D neuron
                                                          module DilatedConv1D : sig ... end
                                                          DilatedConv2D neuron
                                                          module DilatedConv2D : sig ... end
                                                          DilatedConv3D neuron
                                                          module DilatedConv3D : sig ... end
                                                          TransposeConv1D neuron
                                                          module TransposeConv1D : sig ... end
                                                          TransposeConv2D neuron
                                                          module TransposeConv2D : sig ... end
                                                          TransposeConv3D neuron
                                                          module TransposeConv3D : sig ... end
                                                          FullyConnected neuron
                                                          module FullyConnected : sig ... end
                                                          MaxPool1D neuron
                                                          module MaxPool1D : sig ... end
                                                          MaxPool2D neuron
                                                          module MaxPool2D : sig ... end
                                                          AvgPool1D neuron
                                                          module AvgPool1D : sig ... end
                                                          AvgPool2D neuron
                                                          module AvgPool2D : sig ... end
                                                          GlobalMaxPool1D neuron
                                                          module GlobalMaxPool1D : sig ... end
                                                          GlobalMaxPool2D neuron
                                                          module GlobalMaxPool2D : sig ... end
                                                          GlobalAvgPool1D neuron
                                                          module GlobalAvgPool1D : sig ... end
                                                          GlobalAvgPool2D neuron
                                                          module GlobalAvgPool2D : sig ... end
                                                          UpSampling1D neuron
                                                          module UpSampling1D : sig ... end
                                                          UpSampling2D neuron
                                                          module UpSampling2D : sig ... end
                                                          UpSampling3D neuron
                                                          module UpSampling3D : sig ... end
                                                          Padding1D neuron
                                                          module Padding1D : sig ... end
                                                          Padding2D neuron
                                                          module Padding2D : sig ... end
                                                          Padding3D neuron
                                                          module Padding3D : sig ... end
                                                          Lambda neuron
                                                          module Lambda : sig ... end
                                                          LambdaArray neuron
                                                          module LambdaArray : sig ... end
                                                          Dropout neuron
                                                          module Dropout : sig ... end
                                                          Reshape neuron
                                                          module Reshape : sig ... end
                                                          Flatten neuron
                                                          module Flatten : sig ... end
                                                          Slice neuron
                                                          module Slice : sig ... end
                                                          Add neuron
                                                          module Add : sig ... end
                                                          Mul neuron
                                                          module Mul : sig ... end
                                                          Dot neuron
                                                          module Dot : sig ... end
                                                          Max neuron
                                                          module Max : sig ... end
                                                          Average neuron
                                                          module Average : sig ... end
                                                          Concatenate neuron
                                                          module Concatenate : sig ... end
                                                          Normalisation neuron
                                                          module Normalisation : sig ... end
                                                          GaussianNoise neuron
                                                          module GaussianNoise : sig ... end
                                                          GaussianDropout neuron
                                                          module GaussianDropout : sig ... end
                                                          AlphaDropout neuron
                                                          module AlphaDropout : sig ... end
                                                          Embedding neuron
                                                          module Embedding : sig ... end
                                                          Masking neuron
                                                          module Masking : sig ... end
                                                          Core functions
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                            (*

                                                            Types of neuron.

                                                            *)
                                                          val get_in_out_shape : neuron -> int array * int array

                                                          Get both input and output shapes of a neuron.

                                                          val get_in_shape : neuron -> int array

                                                          Get the input shape of a neuron.

                                                          val get_out_shape : neuron -> int array

                                                          Get the output shape of a neuron.

                                                          val connect : int array array -> neuron -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the trainable parameters in an array, used by Optimise module.

                                                          val mkpri : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the primal values in an array, used by Optimise module.

                                                          val mkadj : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron -> Optimise.Algodiff.t array -> unit

                                                          Update trainable parameters in a neuron, used by Optimise module.

                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit

                                                          Load both trainable and non-trainable parameters into the neuron.

                                                          val save_weights : neuron -> Optimise.Algodiff.t array

                                                          Assemble both trainable and non-trainable parameters of the neuron.

                                                          val copy : neuron -> neuron

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : neuron -> string

                                                          Return the name of the neuron.

                                                          +Neuron (owl-base.Owl_neural_graph.Make.Neuron)

                                                          Parameter Make.Neuron

                                                          Init neuron
                                                          module Init : sig ... end
                                                          Input neuron
                                                          module Input : sig ... end
                                                          Activation neuron
                                                          module Activation : sig ... end
                                                          Linear neuron
                                                          module Linear : sig ... end
                                                          LinearNoBias neuron
                                                          module LinearNoBias : sig ... end
                                                          Recurrent neuron
                                                          module Recurrent : sig ... end
                                                          LSTM neuron
                                                          module LSTM : sig ... end
                                                          GRU neuron
                                                          module GRU : sig ... end
                                                          Conv1D neuron
                                                          module Conv1D : sig ... end
                                                          Conv2D neuron
                                                          module Conv2D : sig ... end
                                                          Conv3D neuron
                                                          module Conv3D : sig ... end
                                                          DilatedConv1D neuron
                                                          module DilatedConv1D : sig ... end
                                                          DilatedConv2D neuron
                                                          module DilatedConv2D : sig ... end
                                                          DilatedConv3D neuron
                                                          module DilatedConv3D : sig ... end
                                                          TransposeConv1D neuron
                                                          module TransposeConv1D : sig ... end
                                                          TransposeConv2D neuron
                                                          module TransposeConv2D : sig ... end
                                                          TransposeConv3D neuron
                                                          module TransposeConv3D : sig ... end
                                                          FullyConnected neuron
                                                          module FullyConnected : sig ... end
                                                          MaxPool1D neuron
                                                          module MaxPool1D : sig ... end
                                                          MaxPool2D neuron
                                                          module MaxPool2D : sig ... end
                                                          AvgPool1D neuron
                                                          module AvgPool1D : sig ... end
                                                          AvgPool2D neuron
                                                          module AvgPool2D : sig ... end
                                                          GlobalMaxPool1D neuron
                                                          module GlobalMaxPool1D : sig ... end
                                                          GlobalMaxPool2D neuron
                                                          module GlobalMaxPool2D : sig ... end
                                                          GlobalAvgPool1D neuron
                                                          module GlobalAvgPool1D : sig ... end
                                                          GlobalAvgPool2D neuron
                                                          module GlobalAvgPool2D : sig ... end
                                                          UpSampling1D neuron
                                                          module UpSampling1D : sig ... end
                                                          UpSampling2D neuron
                                                          module UpSampling2D : sig ... end
                                                          UpSampling3D neuron
                                                          module UpSampling3D : sig ... end
                                                          Padding1D neuron
                                                          module Padding1D : sig ... end
                                                          Padding2D neuron
                                                          module Padding2D : sig ... end
                                                          Padding3D neuron
                                                          module Padding3D : sig ... end
                                                          Lambda neuron
                                                          module Lambda : sig ... end
                                                          LambdaArray neuron
                                                          module LambdaArray : sig ... end
                                                          Dropout neuron
                                                          module Dropout : sig ... end
                                                          Reshape neuron
                                                          module Reshape : sig ... end
                                                          Flatten neuron
                                                          module Flatten : sig ... end
                                                          Slice neuron
                                                          module Slice : sig ... end
                                                          Add neuron
                                                          module Add : sig ... end
                                                          Mul neuron
                                                          module Mul : sig ... end
                                                          Dot neuron
                                                          module Dot : sig ... end
                                                          Max neuron
                                                          module Max : sig ... end
                                                          Average neuron
                                                          module Average : sig ... end
                                                          Concatenate neuron
                                                          module Concatenate : sig ... end
                                                          Normalisation neuron
                                                          module Normalisation : sig ... end
                                                          GaussianNoise neuron
                                                          module GaussianNoise : sig ... end
                                                          GaussianDropout neuron
                                                          module GaussianDropout : sig ... end
                                                          AlphaDropout neuron
                                                          module AlphaDropout : sig ... end
                                                          Embedding neuron
                                                          module Embedding : sig ... end
                                                          Masking neuron
                                                          module Masking : sig ... end
                                                          Core functions
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                            (*

                                                            Types of neuron.

                                                            *)
                                                          val get_in_out_shape : neuron -> int array * int array

                                                          Get both input and output shapes of a neuron.

                                                          val get_in_shape : neuron -> int array

                                                          Get the input shape of a neuron.

                                                          val get_out_shape : neuron -> int array

                                                          Get the output shape of a neuron.

                                                          val connect : int array array -> neuron -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the trainable parameters in an array, used by Optimise module.

                                                          val mkpri : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the primal values in an array, used by Optimise module.

                                                          val mkadj : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron -> Optimise.Algodiff.t array -> unit

                                                          Update trainable parameters in a neuron, used by Optimise module.

                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit

                                                          Load both trainable and non-trainable parameters into the neuron.

                                                          val save_weights : neuron -> Optimise.Algodiff.t array

                                                          Assemble both trainable and non-trainable parameters of the neuron.

                                                          val copy : neuron -> neuron

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : neuron -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph/Make/index.html b/docs/owl-base/Owl_neural_graph/Make/index.html index d723ee48e..42b9b151a 100644 --- a/docs/owl-base/Owl_neural_graph/Make/index.html +++ b/docs/owl-base/Owl_neural_graph/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_neural_graph.Make)

                                                          Module Owl_neural_graph.Make

                                                          Parameters

                                                          Signature

                                                          module Neuron = Neuron
                                                          type node = {
                                                          1. mutable name : string;
                                                          2. mutable prev : node array;
                                                          3. mutable next : node array;
                                                          4. mutable neuron : Neuron.neuron;
                                                          5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                          6. mutable network : network;
                                                          7. mutable train : bool;
                                                          }
                                                          and network = {
                                                          1. mutable nnid : string;
                                                          2. mutable size : int;
                                                          3. mutable roots : node array;
                                                          4. mutable outputs : node array;
                                                          5. mutable topo : node array;
                                                          }
                                                          val make_network : ?nnid:string -> int -> node array -> node array -> network
                                                          val make_node : +Make (owl-base.Owl_neural_graph.Make)

                                                          Module Owl_neural_graph.Make

                                                          Parameters

                                                          Signature

                                                          module Neuron = Neuron
                                                          type node = {
                                                          1. mutable name : string;
                                                          2. mutable prev : node array;
                                                          3. mutable next : node array;
                                                          4. mutable neuron : Neuron.neuron;
                                                          5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                          6. mutable network : network;
                                                          7. mutable train : bool;
                                                          }
                                                          and network = {
                                                          1. mutable nnid : string;
                                                          2. mutable size : int;
                                                          3. mutable roots : node array;
                                                          4. mutable outputs : node array;
                                                          5. mutable topo : node array;
                                                          }
                                                          val make_network : ?nnid:string -> int -> node array -> node array -> network
                                                          val make_node : ?name:string -> ?train:bool -> node array -> diff --git a/docs/owl-base/Owl_neural_graph/index.html b/docs/owl-base/Owl_neural_graph/index.html index 10d43b2af..8ed45b793 100644 --- a/docs/owl-base/Owl_neural_graph/index.html +++ b/docs/owl-base/Owl_neural_graph/index.html @@ -1,2 +1,2 @@ -Owl_neural_graph (owl-base.Owl_neural_graph)

                                                          Module Owl_neural_graph

                                                          Neural network: Graphical neural network

                                                          module Make (Neuron : Owl_neural_neuron_sig.Sig) : sig ... end
                                                          +Owl_neural_graph (owl-base.Owl_neural_graph)

                                                          Module Owl_neural_graph

                                                          Neural network: Graphical neural network

                                                          module Make (Neuron : Owl_neural_neuron_sig.Sig) : sig ... end
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/index.html b/docs/owl-base/Owl_neural_graph_sig/index.html index 287dd3b4c..af941c46d 100644 --- a/docs/owl-base/Owl_neural_graph_sig/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/index.html @@ -1,2 +1,2 @@ -Owl_neural_graph_sig (owl-base.Owl_neural_graph_sig)

                                                          Module Owl_neural_graph_sig

                                                          module type Sig = sig ... end
                                                          +Owl_neural_graph_sig (owl-base.Owl_neural_graph_sig)

                                                          Module Owl_neural_graph_sig

                                                          module type Sig = sig ... end
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Activation/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Activation/index.html index e144ba231..74dbb6aa5 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Activation/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Activation/index.html @@ -1,2 +1,2 @@ -Activation (owl-base.Owl_neural_graph_sig.Sig.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                            (*

                                                            Types of activation functions.

                                                            *)
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t

                                                          Run one specific activation function.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val activation_to_string : typ -> string

                                                          Return the name of a specific activation function.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Activation (owl-base.Owl_neural_graph_sig.Sig.Neuron.Activation)

                                                          Module Neuron.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                            (*

                                                            Types of activation functions.

                                                            *)
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t

                                                          Run one specific activation function.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val activation_to_string : typ -> string

                                                          Return the name of a specific activation function.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Add/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Add/index.html index 4dbbfdbd2..0a236673c 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Add/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Add/index.html @@ -1,2 +1,2 @@ -Add (owl-base.Owl_neural_graph_sig.Sig.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Add (owl-base.Owl_neural_graph_sig.Sig.Neuron.Add)

                                                          Module Neuron.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AlphaDropout/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AlphaDropout/index.html index 56f02c636..720ac6751 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AlphaDropout/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AlphaDropout/index.html @@ -1,2 +1,2 @@ -AlphaDropout (owl-base.Owl_neural_graph_sig.Sig.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AlphaDropout (owl-base.Owl_neural_graph_sig.Sig.Neuron.AlphaDropout)

                                                          Module Neuron.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Average/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Average/index.html index 40020e65b..9d08239bb 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Average/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Average/index.html @@ -1,2 +1,2 @@ -Average (owl-base.Owl_neural_graph_sig.Sig.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Average (owl-base.Owl_neural_graph_sig.Sig.Neuron.Average)

                                                          Module Neuron.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AvgPool1D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AvgPool1D/index.html index c878ace61..8f0d1052a 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AvgPool1D/index.html @@ -1,2 +1,2 @@ -AvgPool1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AvgPool1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.AvgPool1D)

                                                          Module Neuron.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AvgPool2D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AvgPool2D/index.html index 6767fe3f7..73c33df6c 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/AvgPool2D/index.html @@ -1,2 +1,2 @@ -AvgPool2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AvgPool2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.AvgPool2D)

                                                          Module Neuron.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Concatenate/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Concatenate/index.html index 618b22056..df27b4712 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Concatenate/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Concatenate/index.html @@ -1,2 +1,2 @@ -Concatenate (owl-base.Owl_neural_graph_sig.Sig.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Concatenate (owl-base.Owl_neural_graph_sig.Sig.Neuron.Concatenate)

                                                          Module Neuron.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv1D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv1D/index.html index 9f5684f35..b3c62d45a 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv1D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Conv1D)

                                                          Module Neuron.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv2D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv2D/index.html index aa1a682f0..865bc4cc1 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv2D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Conv2D)

                                                          Module Neuron.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv3D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv3D/index.html index 0c670b579..0890749d0 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv3D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Conv3D)

                                                          Module Neuron.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv1D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv1D/index.html index 3b77f8f3d..ab2dc73cb 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv1D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.DilatedConv1D)

                                                          Module Neuron.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv2D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv2D/index.html index 6294a0a75..4319d3203 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv2D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.DilatedConv2D)

                                                          Module Neuron.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv3D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv3D/index.html index a3bf92bb8..891bc4937 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv3D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.DilatedConv3D)

                                                          Module Neuron.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Dot/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Dot/index.html index 66e7c9de0..20210d272 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Dot/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Dot/index.html @@ -1,2 +1,2 @@ -Dot (owl-base.Owl_neural_graph_sig.Sig.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Dot (owl-base.Owl_neural_graph_sig.Sig.Neuron.Dot)

                                                          Module Neuron.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Dropout/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Dropout/index.html index 5eefc6641..937519347 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Dropout/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Dropout/index.html @@ -1,2 +1,2 @@ -Dropout (owl-base.Owl_neural_graph_sig.Sig.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Dropout (owl-base.Owl_neural_graph_sig.Sig.Neuron.Dropout)

                                                          Module Neuron.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Embedding/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Embedding/index.html index 8a585b5ff..8de3127d8 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Embedding/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Embedding/index.html @@ -1,2 +1,2 @@ -Embedding (owl-base.Owl_neural_graph_sig.Sig.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Embedding (owl-base.Owl_neural_graph_sig.Sig.Neuron.Embedding)

                                                          Module Neuron.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Flatten/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Flatten/index.html index 0fcc64971..3087614d8 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Flatten/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Flatten/index.html @@ -1,2 +1,2 @@ -Flatten (owl-base.Owl_neural_graph_sig.Sig.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Flatten (owl-base.Owl_neural_graph_sig.Sig.Neuron.Flatten)

                                                          Module Neuron.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/FullyConnected/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/FullyConnected/index.html index abfbb618b..c0c21b063 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/FullyConnected/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/FullyConnected/index.html @@ -1,2 +1,2 @@ -FullyConnected (owl-base.Owl_neural_graph_sig.Sig.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +FullyConnected (owl-base.Owl_neural_graph_sig.Sig.Neuron.FullyConnected)

                                                          Module Neuron.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GRU/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GRU/index.html index c08e686c7..350c87803 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GRU/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GRU/index.html @@ -1,2 +1,2 @@ -GRU (owl-base.Owl_neural_graph_sig.Sig.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GRU (owl-base.Owl_neural_graph_sig.Sig.Neuron.GRU)

                                                          Module Neuron.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GaussianDropout/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GaussianDropout/index.html index 4ee2bc2dc..769e56313 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GaussianDropout/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GaussianDropout/index.html @@ -1,2 +1,2 @@ -GaussianDropout (owl-base.Owl_neural_graph_sig.Sig.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GaussianDropout (owl-base.Owl_neural_graph_sig.Sig.Neuron.GaussianDropout)

                                                          Module Neuron.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GaussianNoise/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GaussianNoise/index.html index 245248d54..3b6c8805e 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GaussianNoise/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GaussianNoise/index.html @@ -1,2 +1,2 @@ -GaussianNoise (owl-base.Owl_neural_graph_sig.Sig.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GaussianNoise (owl-base.Owl_neural_graph_sig.Sig.Neuron.GaussianNoise)

                                                          Module Neuron.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalAvgPool1D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalAvgPool1D/index.html index 9a54b20d7..1aaedd56f 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalAvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalAvgPool1D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalAvgPool1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.GlobalAvgPool1D)

                                                          Module Neuron.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalAvgPool2D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalAvgPool2D/index.html index 11eeb1f64..dd0f27d0f 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalAvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalAvgPool2D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalAvgPool2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.GlobalAvgPool2D)

                                                          Module Neuron.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalMaxPool1D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalMaxPool1D/index.html index 0e3d8c1a2..be1f34cf3 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalMaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalMaxPool1D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalMaxPool1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.GlobalMaxPool1D)

                                                          Module Neuron.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalMaxPool2D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalMaxPool2D/index.html index 154e14459..dc0710f76 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalMaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/GlobalMaxPool2D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalMaxPool2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.GlobalMaxPool2D)

                                                          Module Neuron.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Init/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Init/index.html index 8134f4c31..80d43ebc7 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Init/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Init/index.html @@ -1,2 +1,2 @@ -Init (owl-base.Owl_neural_graph_sig.Sig.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                            (*

                                                            Initialisation types

                                                            *)
                                                          val calc_fans : int array -> float * float

                                                          Calculate fan-in and fan-out of weights.

                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Init (owl-base.Owl_neural_graph_sig.Sig.Neuron.Init)

                                                          Module Neuron.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                            (*

                                                            Initialisation types

                                                            *)
                                                          val calc_fans : int array -> float * float

                                                          Calculate fan-in and fan-out of weights.

                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Input/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Input/index.html index 34c10f57e..9647bedca 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Input/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Input/index.html @@ -1,2 +1,2 @@ -Input (owl-base.Owl_neural_graph_sig.Sig.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Input (owl-base.Owl_neural_graph_sig.Sig.Neuron.Input)

                                                          Module Neuron.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LSTM/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LSTM/index.html index 3d039bbd3..4829aeaf7 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LSTM/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LSTM/index.html @@ -1,2 +1,2 @@ -LSTM (owl-base.Owl_neural_graph_sig.Sig.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +LSTM (owl-base.Owl_neural_graph_sig.Sig.Neuron.LSTM)

                                                          Module Neuron.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Lambda/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Lambda/index.html index 98453097a..959bb4802 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Lambda/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl-base.Owl_neural_graph_sig.Sig.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Lambda (owl-base.Owl_neural_graph_sig.Sig.Neuron.Lambda)

                                                          Module Neuron.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?out_shape:int array -> (Optimise.Algodiff.t -> Optimise.Algodiff.t) -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LambdaArray/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LambdaArray/index.html index 043937d01..ed60e1f5e 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LambdaArray/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl-base.Owl_neural_graph_sig.Sig.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +LambdaArray (owl-base.Owl_neural_graph_sig.Sig.Neuron.LambdaArray)

                                                          Module Neuron.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> (Optimise.Algodiff.t array -> Optimise.Algodiff.t) -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Linear/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Linear/index.html index d86f13570..1c7e0f3f5 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Linear/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Linear/index.html @@ -1,2 +1,2 @@ -Linear (owl-base.Owl_neural_graph_sig.Sig.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Linear (owl-base.Owl_neural_graph_sig.Sig.Neuron.Linear)

                                                          Module Neuron.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LinearNoBias/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LinearNoBias/index.html index 4b27db799..089daabb9 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LinearNoBias/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/LinearNoBias/index.html @@ -1,2 +1,2 @@ -LinearNoBias (owl-base.Owl_neural_graph_sig.Sig.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +LinearNoBias (owl-base.Owl_neural_graph_sig.Sig.Neuron.LinearNoBias)

                                                          Module Neuron.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Masking/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Masking/index.html index 3b0d60d7f..dbc06683c 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Masking/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl-base.Owl_neural_graph_sig.Sig.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          +Masking (owl-base.Owl_neural_graph_sig.Sig.Neuron.Masking)

                                                          Module Neuron.Masking

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Max/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Max/index.html index bac9f014c..44c3b41e7 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Max/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Max/index.html @@ -1,2 +1,2 @@ -Max (owl-base.Owl_neural_graph_sig.Sig.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Max (owl-base.Owl_neural_graph_sig.Sig.Neuron.Max)

                                                          Module Neuron.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/MaxPool1D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/MaxPool1D/index.html index 84a45b892..8127318bc 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/MaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/MaxPool1D/index.html @@ -1,2 +1,2 @@ -MaxPool1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +MaxPool1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.MaxPool1D)

                                                          Module Neuron.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/MaxPool2D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/MaxPool2D/index.html index 4601299f8..906a4fc41 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/MaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/MaxPool2D/index.html @@ -1,2 +1,2 @@ -MaxPool2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +MaxPool2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.MaxPool2D)

                                                          Module Neuron.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Mul/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Mul/index.html index 8c60c424c..b79d0c080 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Mul/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Mul/index.html @@ -1,2 +1,2 @@ -Mul (owl-base.Owl_neural_graph_sig.Sig.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Mul (owl-base.Owl_neural_graph_sig.Sig.Neuron.Mul)

                                                          Module Neuron.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Normalisation/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Normalisation/index.html index dd452ac90..450790cb2 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Normalisation/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl-base.Owl_neural_graph_sig.Sig.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Normalisation (owl-base.Owl_neural_graph_sig.Sig.Neuron.Normalisation)

                                                          Module Neuron.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?training:bool -> ?decay:float -> ?mu:Optimise.Algodiff.A.arr -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Linalg/index.html index c32a0c59b..85de79a11 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Mat/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Mat/index.html index 4d0e47eec..d9909b668 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Scalar/index.html index 113984958..dfc937edc 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/index.html index 4837e8051..e1c5ec00e 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Arr/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Arr/index.html index 0d8e0b7b0..2fa5c4af2 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          +Arr (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/index.html index a7ba36637..237b95f96 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          +Builder (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html index af78dab4e..b5a29fd7e 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          +Aiso (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html index 3258dd4d9..33407aa30 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          +Piso (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html index fcf29e306..c19a8f990 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          +Siao (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 9ceb2e789..7bda4f37e 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sipo (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html index 162937d1d..28aea68b0 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          +Siso (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html index 66d196bd9..ff30fa7b4 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sito (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Linalg/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Linalg/index.html index fdf27facf..49eb74c5b 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Mat/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Mat/index.html index 7ad20b375..25edddf37 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          +Mat (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Maths/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Maths/index.html index c3621cae7..36d0882a2 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          +Maths (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/NN/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/NN/index.html index 1bcef296b..d21381965 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/NN/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : +NN (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/index.html index 5673c0c10..9b23ad7d5 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Algodiff/index.html @@ -1,5 +1,5 @@ -Algodiff (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig +Algodiff (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Batch/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Batch/index.html index dc63ec664..60525f53f 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Batch/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Batch (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Checkpoint/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Checkpoint/index.html index b06434305..4a22dca9d 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Checkpoint/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Checkpoint (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Clipping/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Clipping/index.html index a3a80f32b..a8fd5bba5 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Clipping/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Clipping (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Gradient/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Gradient/index.html index cb7629b92..7755ac57d 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Gradient/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : +Gradient (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Learning_Rate/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Learning_Rate/index.html index a663b8fd2..294c37917 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Learning_Rate/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Learning_Rate (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Loss/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Loss/index.html index e017aaa9a..d14706a0b 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Loss/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Loss (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Momentum/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Momentum/index.html index 9eddb805d..87353dd90 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Momentum/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Momentum (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Params/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Params/index.html index 7f9eb170d..c07463a60 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Params/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : +Params (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Regularisation/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Regularisation/index.html index 59de5023b..1359b8a6e 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Regularisation/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Regularisation (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Stopping/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Stopping/index.html index 4084c8dea..28094b8e6 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Stopping/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Stopping (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Utils/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Utils/index.html index ae9a48bd6..bb1273229 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Utils/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : +Utils (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/index.html index ac27dbbb1..c499ad9d2 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : +Optimise (owl-base.Owl_neural_graph_sig.Sig.Neuron.Optimise)

                                                          Module Neuron.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> @@ -28,4 +28,4 @@ (string -> unit) -> Algodiff.t -> Algodiff.t -> - Checkpoint.state

                                                          TODO

                                                          + Checkpoint.state

                                                          This function is minimize the weights in a compiled neural network of graph structure.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding1D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding1D/index.html index f6bc1ad09..f7e76b6be 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding1D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          +Padding1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Padding1D)

                                                          Module Neuron.Padding1D

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding2D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding2D/index.html index 5d184a9f2..191173ded 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding2D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding2D/index.html @@ -1,2 +1,2 @@ -Padding2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Padding2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Padding2D)

                                                          Module Neuron.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding3D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding3D/index.html index a7faf1048..984cf3470 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding3D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          +Padding3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.Padding3D)

                                                          Module Neuron.Padding3D

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Recurrent/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Recurrent/index.html index a9fedeca3..96652319f 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Recurrent/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl-base.Owl_neural_graph_sig.Sig.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Recurrent (owl-base.Owl_neural_graph_sig.Sig.Neuron.Recurrent)

                                                          Module Neuron.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Reshape/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Reshape/index.html index 55b12f3cb..d1d430ed4 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Reshape/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Reshape/index.html @@ -1,2 +1,2 @@ -Reshape (owl-base.Owl_neural_graph_sig.Sig.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Reshape (owl-base.Owl_neural_graph_sig.Sig.Neuron.Reshape)

                                                          Module Neuron.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Slice/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Slice/index.html index 67abbf23f..9afa916ba 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Slice/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/Slice/index.html @@ -1,2 +1,2 @@ -Slice (owl-base.Owl_neural_graph_sig.Sig.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }

                                                          Neuron type definition.

                                                          val create : int list list -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Slice (owl-base.Owl_neural_graph_sig.Sig.Neuron.Slice)

                                                          Module Neuron.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }

                                                          Neuron type definition.

                                                          val create : int list list -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv1D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv1D/index.html index 3c8dcc940..c6e6d2ed9 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv1D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.TransposeConv1D)

                                                          Module Neuron.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv2D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv2D/index.html index ad27a4cad..ccf18daca 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv2D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.TransposeConv2D)

                                                          Module Neuron.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv3D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv3D/index.html index 14eb5318b..701652725 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv3D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.TransposeConv3D)

                                                          Module Neuron.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling1D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling1D/index.html index e37cc2d40..e9f89b8ec 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling1D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          +UpSampling1D (owl-base.Owl_neural_graph_sig.Sig.Neuron.UpSampling1D)

                                                          Module Neuron.UpSampling1D

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling2D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling2D/index.html index 37ec720c0..9545ba82b 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling2D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling2D/index.html @@ -1,2 +1,2 @@ -UpSampling2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +UpSampling2D (owl-base.Owl_neural_graph_sig.Sig.Neuron.UpSampling2D)

                                                          Module Neuron.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling3D/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling3D/index.html index 3cc31c28f..164c7bbd5 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling3D/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          +UpSampling3D (owl-base.Owl_neural_graph_sig.Sig.Neuron.UpSampling3D)

                                                          Module Neuron.UpSampling3D

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/index.html index a2fcc4f63..c19aa81c7 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/Neuron/index.html @@ -1,2 +1,2 @@ -Neuron (owl-base.Owl_neural_graph_sig.Sig.Neuron)

                                                          Module Sig.Neuron

                                                          Init neuron
                                                          module Init : sig ... end
                                                          Input neuron
                                                          module Input : sig ... end
                                                          Activation neuron
                                                          module Activation : sig ... end
                                                          Linear neuron
                                                          module Linear : sig ... end
                                                          LinearNoBias neuron
                                                          module LinearNoBias : sig ... end
                                                          Recurrent neuron
                                                          module Recurrent : sig ... end
                                                          LSTM neuron
                                                          module LSTM : sig ... end
                                                          GRU neuron
                                                          module GRU : sig ... end
                                                          Conv1D neuron
                                                          module Conv1D : sig ... end
                                                          Conv2D neuron
                                                          module Conv2D : sig ... end
                                                          Conv3D neuron
                                                          module Conv3D : sig ... end
                                                          DilatedConv1D neuron
                                                          module DilatedConv1D : sig ... end
                                                          DilatedConv2D neuron
                                                          module DilatedConv2D : sig ... end
                                                          DilatedConv3D neuron
                                                          module DilatedConv3D : sig ... end
                                                          TransposeConv1D neuron
                                                          module TransposeConv1D : sig ... end
                                                          TransposeConv2D neuron
                                                          module TransposeConv2D : sig ... end
                                                          TransposeConv3D neuron
                                                          module TransposeConv3D : sig ... end
                                                          FullyConnected neuron
                                                          module FullyConnected : sig ... end
                                                          MaxPool1D neuron
                                                          module MaxPool1D : sig ... end
                                                          MaxPool2D neuron
                                                          module MaxPool2D : sig ... end
                                                          AvgPool1D neuron
                                                          module AvgPool1D : sig ... end
                                                          AvgPool2D neuron
                                                          module AvgPool2D : sig ... end
                                                          GlobalMaxPool1D neuron
                                                          module GlobalMaxPool1D : sig ... end
                                                          GlobalMaxPool2D neuron
                                                          module GlobalMaxPool2D : sig ... end
                                                          GlobalAvgPool1D neuron
                                                          module GlobalAvgPool1D : sig ... end
                                                          GlobalAvgPool2D neuron
                                                          module GlobalAvgPool2D : sig ... end
                                                          UpSampling1D neuron
                                                          module UpSampling1D : sig ... end
                                                          UpSampling2D neuron
                                                          module UpSampling2D : sig ... end
                                                          UpSampling3D neuron
                                                          module UpSampling3D : sig ... end
                                                          Padding1D neuron
                                                          module Padding1D : sig ... end
                                                          Padding2D neuron
                                                          module Padding2D : sig ... end
                                                          Padding3D neuron
                                                          module Padding3D : sig ... end
                                                          Lambda neuron
                                                          module Lambda : sig ... end
                                                          LambdaArray neuron
                                                          module LambdaArray : sig ... end
                                                          Dropout neuron
                                                          module Dropout : sig ... end
                                                          Reshape neuron
                                                          module Reshape : sig ... end
                                                          Flatten neuron
                                                          module Flatten : sig ... end
                                                          Slice neuron
                                                          module Slice : sig ... end
                                                          Add neuron
                                                          module Add : sig ... end
                                                          Mul neuron
                                                          module Mul : sig ... end
                                                          Dot neuron
                                                          module Dot : sig ... end
                                                          Max neuron
                                                          module Max : sig ... end
                                                          Average neuron
                                                          module Average : sig ... end
                                                          Concatenate neuron
                                                          module Concatenate : sig ... end
                                                          Normalisation neuron
                                                          module Normalisation : sig ... end
                                                          GaussianNoise neuron
                                                          module GaussianNoise : sig ... end
                                                          GaussianDropout neuron
                                                          module GaussianDropout : sig ... end
                                                          AlphaDropout neuron
                                                          module AlphaDropout : sig ... end
                                                          Embedding neuron
                                                          module Embedding : sig ... end
                                                          Masking neuron
                                                          module Masking : sig ... end
                                                          Core functions
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                            (*

                                                            Types of neuron.

                                                            *)
                                                          val get_in_out_shape : neuron -> int array * int array

                                                          Get both input and output shapes of a neuron.

                                                          val get_in_shape : neuron -> int array

                                                          Get the input shape of a neuron.

                                                          val get_out_shape : neuron -> int array

                                                          Get the output shape of a neuron.

                                                          val connect : int array array -> neuron -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the trainable parameters in an array, used by Optimise module.

                                                          val mkpri : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the primal values in an array, used by Optimise module.

                                                          val mkadj : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron -> Optimise.Algodiff.t array -> unit

                                                          Update trainable parameters in a neuron, used by Optimise module.

                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit

                                                          Load both trainable and non-trainable parameters into the neuron.

                                                          val save_weights : neuron -> Optimise.Algodiff.t array

                                                          Assemble both trainable and non-trainable parameters of the neuron.

                                                          val copy : neuron -> neuron

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : neuron -> string

                                                          Return the name of the neuron.

                                                          +Neuron (owl-base.Owl_neural_graph_sig.Sig.Neuron)

                                                          Module Sig.Neuron

                                                          Init neuron
                                                          module Init : sig ... end
                                                          Input neuron
                                                          module Input : sig ... end
                                                          Activation neuron
                                                          module Activation : sig ... end
                                                          Linear neuron
                                                          module Linear : sig ... end
                                                          LinearNoBias neuron
                                                          module LinearNoBias : sig ... end
                                                          Recurrent neuron
                                                          module Recurrent : sig ... end
                                                          LSTM neuron
                                                          module LSTM : sig ... end
                                                          GRU neuron
                                                          module GRU : sig ... end
                                                          Conv1D neuron
                                                          module Conv1D : sig ... end
                                                          Conv2D neuron
                                                          module Conv2D : sig ... end
                                                          Conv3D neuron
                                                          module Conv3D : sig ... end
                                                          DilatedConv1D neuron
                                                          module DilatedConv1D : sig ... end
                                                          DilatedConv2D neuron
                                                          module DilatedConv2D : sig ... end
                                                          DilatedConv3D neuron
                                                          module DilatedConv3D : sig ... end
                                                          TransposeConv1D neuron
                                                          module TransposeConv1D : sig ... end
                                                          TransposeConv2D neuron
                                                          module TransposeConv2D : sig ... end
                                                          TransposeConv3D neuron
                                                          module TransposeConv3D : sig ... end
                                                          FullyConnected neuron
                                                          module FullyConnected : sig ... end
                                                          MaxPool1D neuron
                                                          module MaxPool1D : sig ... end
                                                          MaxPool2D neuron
                                                          module MaxPool2D : sig ... end
                                                          AvgPool1D neuron
                                                          module AvgPool1D : sig ... end
                                                          AvgPool2D neuron
                                                          module AvgPool2D : sig ... end
                                                          GlobalMaxPool1D neuron
                                                          module GlobalMaxPool1D : sig ... end
                                                          GlobalMaxPool2D neuron
                                                          module GlobalMaxPool2D : sig ... end
                                                          GlobalAvgPool1D neuron
                                                          module GlobalAvgPool1D : sig ... end
                                                          GlobalAvgPool2D neuron
                                                          module GlobalAvgPool2D : sig ... end
                                                          UpSampling1D neuron
                                                          module UpSampling1D : sig ... end
                                                          UpSampling2D neuron
                                                          module UpSampling2D : sig ... end
                                                          UpSampling3D neuron
                                                          module UpSampling3D : sig ... end
                                                          Padding1D neuron
                                                          module Padding1D : sig ... end
                                                          Padding2D neuron
                                                          module Padding2D : sig ... end
                                                          Padding3D neuron
                                                          module Padding3D : sig ... end
                                                          Lambda neuron
                                                          module Lambda : sig ... end
                                                          LambdaArray neuron
                                                          module LambdaArray : sig ... end
                                                          Dropout neuron
                                                          module Dropout : sig ... end
                                                          Reshape neuron
                                                          module Reshape : sig ... end
                                                          Flatten neuron
                                                          module Flatten : sig ... end
                                                          Slice neuron
                                                          module Slice : sig ... end
                                                          Add neuron
                                                          module Add : sig ... end
                                                          Mul neuron
                                                          module Mul : sig ... end
                                                          Dot neuron
                                                          module Dot : sig ... end
                                                          Max neuron
                                                          module Max : sig ... end
                                                          Average neuron
                                                          module Average : sig ... end
                                                          Concatenate neuron
                                                          module Concatenate : sig ... end
                                                          Normalisation neuron
                                                          module Normalisation : sig ... end
                                                          GaussianNoise neuron
                                                          module GaussianNoise : sig ... end
                                                          GaussianDropout neuron
                                                          module GaussianDropout : sig ... end
                                                          AlphaDropout neuron
                                                          module AlphaDropout : sig ... end
                                                          Embedding neuron
                                                          module Embedding : sig ... end
                                                          Masking neuron
                                                          module Masking : sig ... end
                                                          Core functions
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                            (*

                                                            Types of neuron.

                                                            *)
                                                          val get_in_out_shape : neuron -> int array * int array

                                                          Get both input and output shapes of a neuron.

                                                          val get_in_shape : neuron -> int array

                                                          Get the input shape of a neuron.

                                                          val get_out_shape : neuron -> int array

                                                          Get the output shape of a neuron.

                                                          val connect : int array array -> neuron -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the trainable parameters in an array, used by Optimise module.

                                                          val mkpri : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the primal values in an array, used by Optimise module.

                                                          val mkadj : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron -> Optimise.Algodiff.t array -> unit

                                                          Update trainable parameters in a neuron, used by Optimise module.

                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit

                                                          Load both trainable and non-trainable parameters into the neuron.

                                                          val save_weights : neuron -> Optimise.Algodiff.t array

                                                          Assemble both trainable and non-trainable parameters of the neuron.

                                                          val copy : neuron -> neuron

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : neuron -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/index.html b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/index.html index 0aade4272..913456281 100644 --- a/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_neural_graph_sig/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_neural_graph_sig.Sig)

                                                          Module type Owl_neural_graph_sig.Sig

                                                          Type definition
                                                          type node = {
                                                          1. mutable name : string;
                                                          2. mutable prev : node array;
                                                          3. mutable next : node array;
                                                          4. mutable neuron : Neuron.neuron;
                                                          5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                          6. mutable network : network;
                                                          7. mutable train : bool;
                                                          }
                                                          and network = {
                                                          1. mutable nnid : string;
                                                          2. mutable size : int;
                                                          3. mutable roots : node array;
                                                          4. mutable outputs : node array;
                                                          5. mutable topo : node array;
                                                          }

                                                          Type definition of a node and a neural network.

                                                          Manipulate networks
                                                          val make_network : ?nnid:string -> int -> node array -> node array -> network

                                                          Create an empty neural network.

                                                          val make_node : +Sig (owl-base.Owl_neural_graph_sig.Sig)

                                                          Module type Owl_neural_graph_sig.Sig

                                                          Type definition
                                                          type node = {
                                                          1. mutable name : string;
                                                          2. mutable prev : node array;
                                                          3. mutable next : node array;
                                                          4. mutable neuron : Neuron.neuron;
                                                          5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                          6. mutable network : network;
                                                          7. mutable train : bool;
                                                          }
                                                          and network = {
                                                          1. mutable nnid : string;
                                                          2. mutable size : int;
                                                          3. mutable roots : node array;
                                                          4. mutable outputs : node array;
                                                          5. mutable topo : node array;
                                                          }

                                                          Type definition of a node and a neural network.

                                                          Manipulate networks
                                                          val make_network : ?nnid:string -> int -> node array -> node array -> network

                                                          Create an empty neural network.

                                                          val make_node : ?name:string -> ?train:bool -> node array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/Activation/index.html b/docs/owl-base/Owl_neural_neuron/Make/Activation/index.html index e42fb87cb..a300db0cb 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Activation/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Activation/index.html @@ -1,2 +1,2 @@ -Activation (owl-base.Owl_neural_neuron.Make.Activation)

                                                          Module Make.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t
                                                          val copy : neuron_typ -> neuron_typ
                                                          val activation_to_string : typ -> string
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Activation (owl-base.Owl_neural_neuron.Make.Activation)

                                                          Module Make.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t
                                                          val copy : neuron_typ -> neuron_typ
                                                          val activation_to_string : typ -> string
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Add/index.html b/docs/owl-base/Owl_neural_neuron/Make/Add/index.html index 55f895866..1e512e35c 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Add/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Add/index.html @@ -1,2 +1,2 @@ -Add (owl-base.Owl_neural_neuron.Make.Add)

                                                          Module Make.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Add (owl-base.Owl_neural_neuron.Make.Add)

                                                          Module Make.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/AlphaDropout/index.html b/docs/owl-base/Owl_neural_neuron/Make/AlphaDropout/index.html index fce77babb..01c1e0800 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/AlphaDropout/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/AlphaDropout/index.html @@ -1,2 +1,2 @@ -AlphaDropout (owl-base.Owl_neural_neuron.Make.AlphaDropout)

                                                          Module Make.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +AlphaDropout (owl-base.Owl_neural_neuron.Make.AlphaDropout)

                                                          Module Make.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Average/index.html b/docs/owl-base/Owl_neural_neuron/Make/Average/index.html index 215faa46f..a8d4ee45f 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Average/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Average/index.html @@ -1,2 +1,2 @@ -Average (owl-base.Owl_neural_neuron.Make.Average)

                                                          Module Make.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Average (owl-base.Owl_neural_neuron.Make.Average)

                                                          Module Make.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/AvgPool1D/index.html b/docs/owl-base/Owl_neural_neuron/Make/AvgPool1D/index.html index 33e70e768..e57731cd9 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/AvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/AvgPool1D/index.html @@ -1,2 +1,2 @@ -AvgPool1D (owl-base.Owl_neural_neuron.Make.AvgPool1D)

                                                          Module Make.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +AvgPool1D (owl-base.Owl_neural_neuron.Make.AvgPool1D)

                                                          Module Make.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/AvgPool2D/index.html b/docs/owl-base/Owl_neural_neuron/Make/AvgPool2D/index.html index a426f6a56..bdb3b9cbc 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/AvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/AvgPool2D/index.html @@ -1,2 +1,2 @@ -AvgPool2D (owl-base.Owl_neural_neuron.Make.AvgPool2D)

                                                          Module Make.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +AvgPool2D (owl-base.Owl_neural_neuron.Make.AvgPool2D)

                                                          Module Make.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Concatenate/index.html b/docs/owl-base/Owl_neural_neuron/Make/Concatenate/index.html index ad3e0e14e..1a88aa5ed 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Concatenate/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Concatenate/index.html @@ -1,2 +1,2 @@ -Concatenate (owl-base.Owl_neural_neuron.Make.Concatenate)

                                                          Module Make.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Concatenate (owl-base.Owl_neural_neuron.Make.Concatenate)

                                                          Module Make.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Conv1D/index.html b/docs/owl-base/Owl_neural_neuron/Make/Conv1D/index.html index 7d12d58ff..2eccac5c5 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Conv1D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl-base.Owl_neural_neuron.Make.Conv1D)

                                                          Module Make.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +Conv1D (owl-base.Owl_neural_neuron.Make.Conv1D)

                                                          Module Make.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/Conv2D/index.html b/docs/owl-base/Owl_neural_neuron/Make/Conv2D/index.html index 695147cdc..dbf23ee84 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Conv2D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl-base.Owl_neural_neuron.Make.Conv2D)

                                                          Module Make.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +Conv2D (owl-base.Owl_neural_neuron.Make.Conv2D)

                                                          Module Make.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/Conv3D/index.html b/docs/owl-base/Owl_neural_neuron/Make/Conv3D/index.html index 4d0d613a6..6196e1c8b 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Conv3D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl-base.Owl_neural_neuron.Make.Conv3D)

                                                          Module Make.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +Conv3D (owl-base.Owl_neural_neuron.Make.Conv3D)

                                                          Module Make.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/DilatedConv1D/index.html b/docs/owl-base/Owl_neural_neuron/Make/DilatedConv1D/index.html index 66464b9ad..cf635f74e 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/DilatedConv1D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl-base.Owl_neural_neuron.Make.DilatedConv1D)

                                                          Module Make.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : +DilatedConv1D (owl-base.Owl_neural_neuron.Make.DilatedConv1D)

                                                          Module Make.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/DilatedConv2D/index.html b/docs/owl-base/Owl_neural_neuron/Make/DilatedConv2D/index.html index ef7be645b..5a7ccb610 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/DilatedConv2D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl-base.Owl_neural_neuron.Make.DilatedConv2D)

                                                          Module Make.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : +DilatedConv2D (owl-base.Owl_neural_neuron.Make.DilatedConv2D)

                                                          Module Make.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/DilatedConv3D/index.html b/docs/owl-base/Owl_neural_neuron/Make/DilatedConv3D/index.html index 87b92c4c5..099871535 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/DilatedConv3D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl-base.Owl_neural_neuron.Make.DilatedConv3D)

                                                          Module Make.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : +DilatedConv3D (owl-base.Owl_neural_neuron.Make.DilatedConv3D)

                                                          Module Make.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/Dot/index.html b/docs/owl-base/Owl_neural_neuron/Make/Dot/index.html index 8416caef4..a6545579d 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Dot/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Dot/index.html @@ -1,2 +1,2 @@ -Dot (owl-base.Owl_neural_neuron.Make.Dot)

                                                          Module Make.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Dot (owl-base.Owl_neural_neuron.Make.Dot)

                                                          Module Make.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Dropout/index.html b/docs/owl-base/Owl_neural_neuron/Make/Dropout/index.html index 4e1840da5..22ae73508 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Dropout/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Dropout/index.html @@ -1,2 +1,2 @@ -Dropout (owl-base.Owl_neural_neuron.Make.Dropout)

                                                          Module Make.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Dropout (owl-base.Owl_neural_neuron.Make.Dropout)

                                                          Module Make.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Embedding/index.html b/docs/owl-base/Owl_neural_neuron/Make/Embedding/index.html index cedcf9679..b7742e210 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Embedding/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Embedding/index.html @@ -1,2 +1,2 @@ -Embedding (owl-base.Owl_neural_neuron.Make.Embedding)

                                                          Module Make.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Embedding (owl-base.Owl_neural_neuron.Make.Embedding)

                                                          Module Make.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Flatten/index.html b/docs/owl-base/Owl_neural_neuron/Make/Flatten/index.html index 6181ce192..57c641d7d 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Flatten/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Flatten/index.html @@ -1,2 +1,2 @@ -Flatten (owl-base.Owl_neural_neuron.Make.Flatten)

                                                          Module Make.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Flatten (owl-base.Owl_neural_neuron.Make.Flatten)

                                                          Module Make.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/FullyConnected/index.html b/docs/owl-base/Owl_neural_neuron/Make/FullyConnected/index.html index b6732b611..d05223187 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/FullyConnected/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/FullyConnected/index.html @@ -1,2 +1,2 @@ -FullyConnected (owl-base.Owl_neural_neuron.Make.FullyConnected)

                                                          Module Make.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +FullyConnected (owl-base.Owl_neural_neuron.Make.FullyConnected)

                                                          Module Make.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/GRU/index.html b/docs/owl-base/Owl_neural_neuron/Make/GRU/index.html index f6fbaa2e4..ce03f109e 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/GRU/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/GRU/index.html @@ -1,2 +1,2 @@ -GRU (owl-base.Owl_neural_neuron.Make.GRU)

                                                          Module Make.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GRU (owl-base.Owl_neural_neuron.Make.GRU)

                                                          Module Make.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/GaussianDropout/index.html b/docs/owl-base/Owl_neural_neuron/Make/GaussianDropout/index.html index 7bbfedf79..ac47f20f3 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/GaussianDropout/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/GaussianDropout/index.html @@ -1,2 +1,2 @@ -GaussianDropout (owl-base.Owl_neural_neuron.Make.GaussianDropout)

                                                          Module Make.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GaussianDropout (owl-base.Owl_neural_neuron.Make.GaussianDropout)

                                                          Module Make.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/GaussianNoise/index.html b/docs/owl-base/Owl_neural_neuron/Make/GaussianNoise/index.html index c51f34d61..050b448d6 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/GaussianNoise/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/GaussianNoise/index.html @@ -1,2 +1,2 @@ -GaussianNoise (owl-base.Owl_neural_neuron.Make.GaussianNoise)

                                                          Module Make.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GaussianNoise (owl-base.Owl_neural_neuron.Make.GaussianNoise)

                                                          Module Make.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : float -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/GlobalAvgPool1D/index.html b/docs/owl-base/Owl_neural_neuron/Make/GlobalAvgPool1D/index.html index de7e940c5..473b81004 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/GlobalAvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/GlobalAvgPool1D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool1D (owl-base.Owl_neural_neuron.Make.GlobalAvgPool1D)

                                                          Module Make.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GlobalAvgPool1D (owl-base.Owl_neural_neuron.Make.GlobalAvgPool1D)

                                                          Module Make.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/GlobalAvgPool2D/index.html b/docs/owl-base/Owl_neural_neuron/Make/GlobalAvgPool2D/index.html index b7d26a66b..c49f9817f 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/GlobalAvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/GlobalAvgPool2D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool2D (owl-base.Owl_neural_neuron.Make.GlobalAvgPool2D)

                                                          Module Make.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GlobalAvgPool2D (owl-base.Owl_neural_neuron.Make.GlobalAvgPool2D)

                                                          Module Make.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/GlobalMaxPool1D/index.html b/docs/owl-base/Owl_neural_neuron/Make/GlobalMaxPool1D/index.html index 3aa3a0c9c..a2641b741 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/GlobalMaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/GlobalMaxPool1D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool1D (owl-base.Owl_neural_neuron.Make.GlobalMaxPool1D)

                                                          Module Make.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GlobalMaxPool1D (owl-base.Owl_neural_neuron.Make.GlobalMaxPool1D)

                                                          Module Make.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/GlobalMaxPool2D/index.html b/docs/owl-base/Owl_neural_neuron/Make/GlobalMaxPool2D/index.html index f5f90d4a4..70b7a26ae 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/GlobalMaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/GlobalMaxPool2D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool2D (owl-base.Owl_neural_neuron.Make.GlobalMaxPool2D)

                                                          Module Make.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +GlobalMaxPool2D (owl-base.Owl_neural_neuron.Make.GlobalMaxPool2D)

                                                          Module Make.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Init/index.html b/docs/owl-base/Owl_neural_neuron/Make/Init/index.html index 70e735cbe..12d24a503 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Init/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Init/index.html @@ -1,2 +1,2 @@ -Init (owl-base.Owl_neural_neuron.Make.Init)

                                                          Module Make.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                          val calc_fans : int array -> float * float
                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          val to_string : typ -> string
                                                          val to_name : unit -> string
                                                          +Init (owl-base.Owl_neural_neuron.Make.Init)

                                                          Module Make.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                          val calc_fans : int array -> float * float
                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          val to_string : typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Input/index.html b/docs/owl-base/Owl_neural_neuron/Make/Input/index.html index 02fbb845d..d71d293cd 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Input/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Input/index.html @@ -1,2 +1,2 @@ -Input (owl-base.Owl_neural_neuron.Make.Input)

                                                          Module Make.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Input (owl-base.Owl_neural_neuron.Make.Input)

                                                          Module Make.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/LSTM/index.html b/docs/owl-base/Owl_neural_neuron/Make/LSTM/index.html index eb0243d6c..15e4ebd00 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/LSTM/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/LSTM/index.html @@ -1,2 +1,2 @@ -LSTM (owl-base.Owl_neural_neuron.Make.LSTM)

                                                          Module Make.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +LSTM (owl-base.Owl_neural_neuron.Make.LSTM)

                                                          Module Make.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Lambda/index.html b/docs/owl-base/Owl_neural_neuron/Make/Lambda/index.html index b23c9d6c6..2f397d848 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Lambda/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl-base.Owl_neural_neuron.Make.Lambda)

                                                          Module Make.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : +Lambda (owl-base.Owl_neural_neuron.Make.Lambda)

                                                          Module Make.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : ?out_shape:int array -> (Optimise.Algodiff.t -> Optimise.Algodiff.t) -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/LambdaArray/index.html b/docs/owl-base/Owl_neural_neuron/Make/LambdaArray/index.html index d3f2239b1..c6be5b582 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/LambdaArray/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl-base.Owl_neural_neuron.Make.LambdaArray)

                                                          Module Make.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : +LambdaArray (owl-base.Owl_neural_neuron.Make.LambdaArray)

                                                          Module Make.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> (Optimise.Algodiff.t array -> Optimise.Algodiff.t) -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Linear/index.html b/docs/owl-base/Owl_neural_neuron/Make/Linear/index.html index f489f6e9d..ed8ded507 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Linear/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Linear/index.html @@ -1,2 +1,2 @@ -Linear (owl-base.Owl_neural_neuron.Make.Linear)

                                                          Module Make.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Linear (owl-base.Owl_neural_neuron.Make.Linear)

                                                          Module Make.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/LinearNoBias/index.html b/docs/owl-base/Owl_neural_neuron/Make/LinearNoBias/index.html index 45e1b7f2a..a5e284899 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/LinearNoBias/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/LinearNoBias/index.html @@ -1,2 +1,2 @@ -LinearNoBias (owl-base.Owl_neural_neuron.Make.LinearNoBias)

                                                          Module Make.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +LinearNoBias (owl-base.Owl_neural_neuron.Make.LinearNoBias)

                                                          Module Make.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val init : neuron_typ -> unit
                                                          val reset : neuron_typ -> unit
                                                          val mktag : int -> neuron_typ -> unit
                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Masking/index.html b/docs/owl-base/Owl_neural_neuron/Make/Masking/index.html index e9d857785..b9352d917 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Masking/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl-base.Owl_neural_neuron.Make.Masking)

                                                          Module Make.Masking

                                                          +Masking (owl-base.Owl_neural_neuron.Make.Masking)

                                                          Module Make.Masking

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Max/index.html b/docs/owl-base/Owl_neural_neuron/Make/Max/index.html index 3b3f4b1a3..1a4098d7f 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Max/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Max/index.html @@ -1,2 +1,2 @@ -Max (owl-base.Owl_neural_neuron.Make.Max)

                                                          Module Make.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Max (owl-base.Owl_neural_neuron.Make.Max)

                                                          Module Make.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/MaxPool1D/index.html b/docs/owl-base/Owl_neural_neuron/Make/MaxPool1D/index.html index 84cbf9a46..5e8cc98e9 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/MaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/MaxPool1D/index.html @@ -1,2 +1,2 @@ -MaxPool1D (owl-base.Owl_neural_neuron.Make.MaxPool1D)

                                                          Module Make.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +MaxPool1D (owl-base.Owl_neural_neuron.Make.MaxPool1D)

                                                          Module Make.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/MaxPool2D/index.html b/docs/owl-base/Owl_neural_neuron/Make/MaxPool2D/index.html index e09e30295..a74b6e733 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/MaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/MaxPool2D/index.html @@ -1,2 +1,2 @@ -MaxPool2D (owl-base.Owl_neural_neuron.Make.MaxPool2D)

                                                          Module Make.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +MaxPool2D (owl-base.Owl_neural_neuron.Make.MaxPool2D)

                                                          Module Make.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }
                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Mul/index.html b/docs/owl-base/Owl_neural_neuron/Make/Mul/index.html index 42a8025e8..78286fbb6 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Mul/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Mul/index.html @@ -1,2 +1,2 @@ -Mul (owl-base.Owl_neural_neuron.Make.Mul)

                                                          Module Make.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Mul (owl-base.Owl_neural_neuron.Make.Mul)

                                                          Module Make.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : unit -> neuron_typ
                                                          val connect : int array array -> neuron_typ -> unit
                                                          val copy : 'a -> neuron_typ
                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Normalisation/index.html b/docs/owl-base/Owl_neural_neuron/Make/Normalisation/index.html index c7c85fc28..f524ebc97 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Normalisation/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl-base.Owl_neural_neuron.Make.Normalisation)

                                                          Module Make.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : +Normalisation (owl-base.Owl_neural_neuron.Make.Normalisation)

                                                          Module Make.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }
                                                          val create : ?training:bool -> ?decay:float -> ?mu:Optimise.Algodiff.A.arr -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/Padding1D/index.html b/docs/owl-base/Owl_neural_neuron/Make/Padding1D/index.html index 8ac4a9876..8d313c30d 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Padding1D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl-base.Owl_neural_neuron.Make.Padding1D)

                                                          Module Make.Padding1D

                                                          +Padding1D (owl-base.Owl_neural_neuron.Make.Padding1D)

                                                          Module Make.Padding1D

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Padding2D/index.html b/docs/owl-base/Owl_neural_neuron/Make/Padding2D/index.html index 339363199..aec8d9660 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Padding2D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Padding2D/index.html @@ -1,2 +1,2 @@ -Padding2D (owl-base.Owl_neural_neuron.Make.Padding2D)

                                                          Module Make.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Padding2D (owl-base.Owl_neural_neuron.Make.Padding2D)

                                                          Module Make.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Padding3D/index.html b/docs/owl-base/Owl_neural_neuron/Make/Padding3D/index.html index 5ed6019fd..2fe8232a4 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Padding3D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl-base.Owl_neural_neuron.Make.Padding3D)

                                                          Module Make.Padding3D

                                                          +Padding3D (owl-base.Owl_neural_neuron.Make.Padding3D)

                                                          Module Make.Padding3D

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Recurrent/index.html b/docs/owl-base/Owl_neural_neuron/Make/Recurrent/index.html index bed6eb966..156486998 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Recurrent/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl-base.Owl_neural_neuron.Make.Recurrent)

                                                          Module Make.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }
                                                          val create : +Recurrent (owl-base.Owl_neural_neuron.Make.Recurrent)

                                                          Module Make.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }
                                                          val create : ?time_steps:int -> ?inputs:int -> int -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/Reshape/index.html b/docs/owl-base/Owl_neural_neuron/Make/Reshape/index.html index d500b885c..d6c6bd29f 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Reshape/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Reshape/index.html @@ -1,2 +1,2 @@ -Reshape (owl-base.Owl_neural_neuron.Make.Reshape)

                                                          Module Make.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Reshape (owl-base.Owl_neural_neuron.Make.Reshape)

                                                          Module Make.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/Slice/index.html b/docs/owl-base/Owl_neural_neuron/Make/Slice/index.html index 8cde14b0d..6215147aa 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/Slice/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/Slice/index.html @@ -1,2 +1,2 @@ -Slice (owl-base.Owl_neural_neuron.Make.Slice)

                                                          Module Make.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }
                                                          val create : int list list -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +Slice (owl-base.Owl_neural_neuron.Make.Slice)

                                                          Module Make.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }
                                                          val create : int list list -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/TransposeConv1D/index.html b/docs/owl-base/Owl_neural_neuron/Make/TransposeConv1D/index.html index 83913d98e..d3fdd5404 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/TransposeConv1D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl-base.Owl_neural_neuron.Make.TransposeConv1D)

                                                          Module Make.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +TransposeConv1D (owl-base.Owl_neural_neuron.Make.TransposeConv1D)

                                                          Module Make.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/TransposeConv2D/index.html b/docs/owl-base/Owl_neural_neuron/Make/TransposeConv2D/index.html index ff455dcab..8ea153011 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/TransposeConv2D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl-base.Owl_neural_neuron.Make.TransposeConv2D)

                                                          Module Make.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +TransposeConv2D (owl-base.Owl_neural_neuron.Make.TransposeConv2D)

                                                          Module Make.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/TransposeConv3D/index.html b/docs/owl-base/Owl_neural_neuron/Make/TransposeConv3D/index.html index 3737c7df4..b22159552 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/TransposeConv3D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl-base.Owl_neural_neuron.Make.TransposeConv3D)

                                                          Module Make.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : +TransposeConv3D (owl-base.Owl_neural_neuron.Make.TransposeConv3D)

                                                          Module Make.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }
                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/UpSampling1D/index.html b/docs/owl-base/Owl_neural_neuron/Make/UpSampling1D/index.html index 2e38c779a..d2982e9a7 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/UpSampling1D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl-base.Owl_neural_neuron.Make.UpSampling1D)

                                                          Module Make.UpSampling1D

                                                          +UpSampling1D (owl-base.Owl_neural_neuron.Make.UpSampling1D)

                                                          Module Make.UpSampling1D

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/UpSampling2D/index.html b/docs/owl-base/Owl_neural_neuron/Make/UpSampling2D/index.html index 0032af04c..f9317d1f6 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/UpSampling2D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/UpSampling2D/index.html @@ -1,2 +1,2 @@ -UpSampling2D (owl-base.Owl_neural_neuron.Make.UpSampling2D)

                                                          Module Make.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          +UpSampling2D (owl-base.Owl_neural_neuron.Make.UpSampling2D)

                                                          Module Make.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }
                                                          val create : int array -> neuron_typ
                                                          val connect : int array -> neuron_typ -> unit
                                                          val copy : neuron_typ -> neuron_typ
                                                          val to_string : neuron_typ -> string
                                                          val to_name : unit -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/UpSampling3D/index.html b/docs/owl-base/Owl_neural_neuron/Make/UpSampling3D/index.html index 2efb580b0..b77c59b22 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/UpSampling3D/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl-base.Owl_neural_neuron.Make.UpSampling3D)

                                                          Module Make.UpSampling3D

                                                          +UpSampling3D (owl-base.Owl_neural_neuron.Make.UpSampling3D)

                                                          Module Make.UpSampling3D

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Linalg/index.html index ac772947d..88a6a0329 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Mat/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Mat/index.html index 2d1d474bf..d625bc177 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Scalar/index.html index 187176edd..1cdc42158 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/index.html index e19c948c5..4dec3eaa3 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Arr/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Arr/index.html index 673582fe3..2469a1cf1 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          +Arr (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/index.html index 8a6bca6c0..a5f794abd 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          +Builder (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Aiso/index.html index 44598b293..56c198d93 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          +Aiso (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Piso/index.html index 47906b32c..973ac7202 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          +Piso (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siao/index.html index f4dddab0d..0f6764646 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          +Siao (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sipo/index.html index b188f9df6..dfa8e0bb7 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sipo (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siso/index.html index f0d5b7beb..9d2954947 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          +Siso (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sito/index.html index b61b0c3ba..2500ebee4 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sito (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Linalg/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Linalg/index.html index ca3dc91d1..df692d4d7 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Mat/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Mat/index.html index 30a92ba66..f0ffa4a8a 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          +Mat (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Maths/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Maths/index.html index 7ddb87da6..c7f6889df 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          +Maths (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/NN/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/NN/index.html index bce03eea3..78afcb7ff 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/NN/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : +NN (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/index.html index 006aa3dc2..2c9d2f895 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Algodiff/index.html @@ -1,5 +1,5 @@ -Algodiff (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig +Algodiff (owl-base.Owl_neural_neuron.Make.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Batch/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Batch/index.html index 176526243..7e43ad0a5 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Batch/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl-base.Owl_neural_neuron.Make.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Batch (owl-base.Owl_neural_neuron.Make.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Checkpoint/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Checkpoint/index.html index 2be21fe52..93c7286dc 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Checkpoint/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl-base.Owl_neural_neuron.Make.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Checkpoint (owl-base.Owl_neural_neuron.Make.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Clipping/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Clipping/index.html index a42e54400..be2959687 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Clipping/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl-base.Owl_neural_neuron.Make.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Clipping (owl-base.Owl_neural_neuron.Make.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Gradient/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Gradient/index.html index 87d86749b..bc51b60a1 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Gradient/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_neural_neuron.Make.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : +Gradient (owl-base.Owl_neural_neuron.Make.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Learning_Rate/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Learning_Rate/index.html index e510159ba..937c6c5ea 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Learning_Rate/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl-base.Owl_neural_neuron.Make.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Learning_Rate (owl-base.Owl_neural_neuron.Make.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Loss/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Loss/index.html index 600f8b544..2b446cfbf 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Loss/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl-base.Owl_neural_neuron.Make.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Loss (owl-base.Owl_neural_neuron.Make.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Momentum/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Momentum/index.html index ed4de8732..705fc08dd 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Momentum/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl-base.Owl_neural_neuron.Make.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Momentum (owl-base.Owl_neural_neuron.Make.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Params/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Params/index.html index c41ae46da..aa3d3c803 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Params/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_neural_neuron.Make.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : +Params (owl-base.Owl_neural_neuron.Make.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Regularisation/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Regularisation/index.html index 44d4bae81..6d8a1b0cc 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Regularisation/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl-base.Owl_neural_neuron.Make.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Regularisation (owl-base.Owl_neural_neuron.Make.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Stopping/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Stopping/index.html index 08a77fa47..7434206fa 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Stopping/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl-base.Owl_neural_neuron.Make.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Stopping (owl-base.Owl_neural_neuron.Make.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Utils/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Utils/index.html index 36ffe586c..4e8f61103 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Utils/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_neural_neuron.Make.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : +Utils (owl-base.Owl_neural_neuron.Make.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/index.html b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/index.html index b535f45f5..15e9c75ab 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/argument-1-Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl-base.Owl_neural_neuron.Make.Optimise)

                                                          Parameter Make.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : +Optimise (owl-base.Owl_neural_neuron.Make.Optimise)

                                                          Parameter Make.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> @@ -28,4 +28,4 @@ (string -> unit) -> Algodiff.t -> Algodiff.t -> - Checkpoint.state

                                                          TODO

                                                          + Checkpoint.state

                                                          This function is minimize the weights in a compiled neural network of graph structure.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron/Make/index.html b/docs/owl-base/Owl_neural_neuron/Make/index.html index f2b05a7a8..d97eb3397 100644 --- a/docs/owl-base/Owl_neural_neuron/Make/index.html +++ b/docs/owl-base/Owl_neural_neuron/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_neural_neuron.Make)

                                                          Module Owl_neural_neuron.Make

                                                          Parameters

                                                          Signature

                                                          module Optimise = Optimise
                                                          module Init : sig ... end
                                                          module Input : sig ... end
                                                          module Activation : sig ... end
                                                          module Linear : sig ... end
                                                          module LinearNoBias : sig ... end
                                                          module Recurrent : sig ... end
                                                          module LSTM : sig ... end
                                                          module GRU : sig ... end
                                                          module Conv1D : sig ... end
                                                          module DilatedConv1D : sig ... end
                                                          module TransposeConv1D : sig ... end
                                                          module Conv2D : sig ... end
                                                          module DilatedConv2D : sig ... end
                                                          module TransposeConv2D : sig ... end
                                                          module Conv3D : sig ... end
                                                          module DilatedConv3D : sig ... end
                                                          module TransposeConv3D : sig ... end
                                                          module FullyConnected : sig ... end
                                                          module MaxPool1D : sig ... end
                                                          module MaxPool2D : sig ... end
                                                          module AvgPool1D : sig ... end
                                                          module AvgPool2D : sig ... end
                                                          module GlobalMaxPool1D : sig ... end
                                                          module GlobalMaxPool2D : sig ... end
                                                          module GlobalAvgPool1D : sig ... end
                                                          module GlobalAvgPool2D : sig ... end
                                                          module UpSampling1D : sig ... end
                                                          module UpSampling2D : sig ... end
                                                          module UpSampling3D : sig ... end
                                                          module Padding1D : sig ... end
                                                          module Padding2D : sig ... end
                                                          module Padding3D : sig ... end
                                                          module Lambda : sig ... end
                                                          module LambdaArray : sig ... end
                                                          module Dropout : sig ... end
                                                          module Reshape : sig ... end
                                                          module Flatten : sig ... end
                                                          module Slice : sig ... end
                                                          module Add : sig ... end
                                                          module Mul : sig ... end
                                                          module Dot : sig ... end
                                                          module Max : sig ... end
                                                          module Average : sig ... end
                                                          module Concatenate : sig ... end
                                                          module Normalisation : sig ... end
                                                          module GaussianNoise : sig ... end
                                                          module GaussianDropout : sig ... end
                                                          module AlphaDropout : sig ... end
                                                          module Embedding : sig ... end
                                                          module Masking : sig ... end
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                          val get_in_out_shape : neuron -> int array * int array
                                                          val get_in_shape : neuron -> int array
                                                          val get_out_shape : neuron -> int array
                                                          val connect : int array array -> neuron -> unit
                                                          val init : neuron -> unit
                                                          val reset : neuron -> unit
                                                          val mktag : int -> neuron -> unit
                                                          val mkpar : neuron -> Optimise.Algodiff.t array
                                                          val mkpri : neuron -> Optimise.Algodiff.t array
                                                          val mkadj : neuron -> Optimise.Algodiff.t array
                                                          val update : neuron -> Optimise.Algodiff.t array -> unit
                                                          val save_weights : neuron -> Optimise.Algodiff.t array
                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron -> neuron
                                                          val to_string : neuron -> string
                                                          val to_name : neuron -> string
                                                          +Make (owl-base.Owl_neural_neuron.Make)

                                                          Module Owl_neural_neuron.Make

                                                          Parameters

                                                          Signature

                                                          module Optimise = Optimise
                                                          module Init : sig ... end
                                                          module Input : sig ... end
                                                          module Activation : sig ... end
                                                          module Linear : sig ... end
                                                          module LinearNoBias : sig ... end
                                                          module Recurrent : sig ... end
                                                          module LSTM : sig ... end
                                                          module GRU : sig ... end
                                                          module Conv1D : sig ... end
                                                          module DilatedConv1D : sig ... end
                                                          module TransposeConv1D : sig ... end
                                                          module Conv2D : sig ... end
                                                          module DilatedConv2D : sig ... end
                                                          module TransposeConv2D : sig ... end
                                                          module Conv3D : sig ... end
                                                          module DilatedConv3D : sig ... end
                                                          module TransposeConv3D : sig ... end
                                                          module FullyConnected : sig ... end
                                                          module MaxPool1D : sig ... end
                                                          module MaxPool2D : sig ... end
                                                          module AvgPool1D : sig ... end
                                                          module AvgPool2D : sig ... end
                                                          module GlobalMaxPool1D : sig ... end
                                                          module GlobalMaxPool2D : sig ... end
                                                          module GlobalAvgPool1D : sig ... end
                                                          module GlobalAvgPool2D : sig ... end
                                                          module UpSampling1D : sig ... end
                                                          module UpSampling2D : sig ... end
                                                          module UpSampling3D : sig ... end
                                                          module Padding1D : sig ... end
                                                          module Padding2D : sig ... end
                                                          module Padding3D : sig ... end
                                                          module Lambda : sig ... end
                                                          module LambdaArray : sig ... end
                                                          module Dropout : sig ... end
                                                          module Reshape : sig ... end
                                                          module Flatten : sig ... end
                                                          module Slice : sig ... end
                                                          module Add : sig ... end
                                                          module Mul : sig ... end
                                                          module Dot : sig ... end
                                                          module Max : sig ... end
                                                          module Average : sig ... end
                                                          module Concatenate : sig ... end
                                                          module Normalisation : sig ... end
                                                          module GaussianNoise : sig ... end
                                                          module GaussianDropout : sig ... end
                                                          module AlphaDropout : sig ... end
                                                          module Embedding : sig ... end
                                                          module Masking : sig ... end
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                          val get_in_out_shape : neuron -> int array * int array
                                                          val get_in_shape : neuron -> int array
                                                          val get_out_shape : neuron -> int array
                                                          val connect : int array array -> neuron -> unit
                                                          val init : neuron -> unit
                                                          val reset : neuron -> unit
                                                          val mktag : int -> neuron -> unit
                                                          val mkpar : neuron -> Optimise.Algodiff.t array
                                                          val mkpri : neuron -> Optimise.Algodiff.t array
                                                          val mkadj : neuron -> Optimise.Algodiff.t array
                                                          val update : neuron -> Optimise.Algodiff.t array -> unit
                                                          val save_weights : neuron -> Optimise.Algodiff.t array
                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit
                                                          val copy : neuron -> neuron
                                                          val to_string : neuron -> string
                                                          val to_name : neuron -> string
                                                          diff --git a/docs/owl-base/Owl_neural_neuron/index.html b/docs/owl-base/Owl_neural_neuron/index.html index 5a27b8c07..66486dc05 100644 --- a/docs/owl-base/Owl_neural_neuron/index.html +++ b/docs/owl-base/Owl_neural_neuron/index.html @@ -1,2 +1,2 @@ -Owl_neural_neuron (owl-base.Owl_neural_neuron)

                                                          Module Owl_neural_neuron

                                                          Neural network: Neuron definitions

                                                          module Make (Optimise : Owl_optimise_generic_sig.Sig) : sig ... end
                                                          +Owl_neural_neuron (owl-base.Owl_neural_neuron)

                                                          Module Owl_neural_neuron

                                                          Neural network: Neuron definitions

                                                          module Make (Optimise : Owl_optimise_generic_sig.Sig) : sig ... end
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/index.html b/docs/owl-base/Owl_neural_neuron_sig/index.html index 2bfdfe6f9..098db8d9d 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/index.html @@ -1,2 +1,2 @@ -Owl_neural_neuron_sig (owl-base.Owl_neural_neuron_sig)

                                                          Module Owl_neural_neuron_sig

                                                          module type Sig = sig ... end
                                                          +Owl_neural_neuron_sig (owl-base.Owl_neural_neuron_sig)

                                                          Module Owl_neural_neuron_sig

                                                          module type Sig = sig ... end
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Activation/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Activation/index.html index 217da5e7b..438e7c967 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Activation/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Activation/index.html @@ -1,2 +1,2 @@ -Activation (owl-base.Owl_neural_neuron_sig.Sig.Activation)

                                                          Module Sig.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                            (*

                                                            Types of activation functions.

                                                            *)
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t

                                                          Run one specific activation function.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val activation_to_string : typ -> string

                                                          Return the name of a specific activation function.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Activation (owl-base.Owl_neural_neuron_sig.Sig.Activation)

                                                          Module Sig.Activation

                                                          type typ =
                                                          1. | Elu
                                                          2. | Relu
                                                          3. | Sigmoid
                                                          4. | HardSigmoid
                                                          5. | Softmax of int
                                                          6. | Softplus
                                                          7. | Softsign
                                                          8. | Tanh
                                                          9. | Relu6
                                                          10. | LeakyRelu of float
                                                          11. | TRelu of float
                                                          12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                          13. | None
                                                            (*

                                                            Types of activation functions.

                                                            *)
                                                          type neuron_typ = {
                                                          1. mutable activation : typ;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val run_activation : Optimise.Algodiff.t -> typ -> Optimise.Algodiff.t

                                                          Run one specific activation function.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val activation_to_string : typ -> string

                                                          Return the name of a specific activation function.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Add/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Add/index.html index d620c9481..b7d0c1cc5 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Add/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Add/index.html @@ -1,2 +1,2 @@ -Add (owl-base.Owl_neural_neuron_sig.Sig.Add)

                                                          Module Sig.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Add (owl-base.Owl_neural_neuron_sig.Sig.Add)

                                                          Module Sig.Add

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AlphaDropout/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AlphaDropout/index.html index c1b816d31..a51e48689 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AlphaDropout/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AlphaDropout/index.html @@ -1,2 +1,2 @@ -AlphaDropout (owl-base.Owl_neural_neuron_sig.Sig.AlphaDropout)

                                                          Module Sig.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AlphaDropout (owl-base.Owl_neural_neuron_sig.Sig.AlphaDropout)

                                                          Module Sig.AlphaDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Average/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Average/index.html index 3320dc1af..71d4b8571 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Average/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Average/index.html @@ -1,2 +1,2 @@ -Average (owl-base.Owl_neural_neuron_sig.Sig.Average)

                                                          Module Sig.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Average (owl-base.Owl_neural_neuron_sig.Sig.Average)

                                                          Module Sig.Average

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AvgPool1D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AvgPool1D/index.html index bc5422c30..ee9c5f6df 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AvgPool1D/index.html @@ -1,2 +1,2 @@ -AvgPool1D (owl-base.Owl_neural_neuron_sig.Sig.AvgPool1D)

                                                          Module Sig.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AvgPool1D (owl-base.Owl_neural_neuron_sig.Sig.AvgPool1D)

                                                          Module Sig.AvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AvgPool2D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AvgPool2D/index.html index d3dcd0b59..6f87e47fe 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/AvgPool2D/index.html @@ -1,2 +1,2 @@ -AvgPool2D (owl-base.Owl_neural_neuron_sig.Sig.AvgPool2D)

                                                          Module Sig.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +AvgPool2D (owl-base.Owl_neural_neuron_sig.Sig.AvgPool2D)

                                                          Module Sig.AvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Concatenate/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Concatenate/index.html index cc89b6b41..d6246a52a 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Concatenate/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Concatenate/index.html @@ -1,2 +1,2 @@ -Concatenate (owl-base.Owl_neural_neuron_sig.Sig.Concatenate)

                                                          Module Sig.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Concatenate (owl-base.Owl_neural_neuron_sig.Sig.Concatenate)

                                                          Module Sig.Concatenate

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv1D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv1D/index.html index 1296904f3..5add10ae8 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv1D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl-base.Owl_neural_neuron_sig.Sig.Conv1D)

                                                          Module Sig.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv1D (owl-base.Owl_neural_neuron_sig.Sig.Conv1D)

                                                          Module Sig.Conv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv2D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv2D/index.html index f253a95e4..899fdbab6 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv2D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl-base.Owl_neural_neuron_sig.Sig.Conv2D)

                                                          Module Sig.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv2D (owl-base.Owl_neural_neuron_sig.Sig.Conv2D)

                                                          Module Sig.Conv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv3D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv3D/index.html index 03e6f6e61..122e49a92 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv3D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl-base.Owl_neural_neuron_sig.Sig.Conv3D)

                                                          Module Sig.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Conv3D (owl-base.Owl_neural_neuron_sig.Sig.Conv3D)

                                                          Module Sig.Conv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv1D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv1D/index.html index ac24c6270..c91f26e6a 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv1D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl-base.Owl_neural_neuron_sig.Sig.DilatedConv1D)

                                                          Module Sig.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv1D (owl-base.Owl_neural_neuron_sig.Sig.DilatedConv1D)

                                                          Module Sig.DilatedConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv2D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv2D/index.html index 2f761aca4..6c3108888 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv2D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl-base.Owl_neural_neuron_sig.Sig.DilatedConv2D)

                                                          Module Sig.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv2D (owl-base.Owl_neural_neuron_sig.Sig.DilatedConv2D)

                                                          Module Sig.DilatedConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv3D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv3D/index.html index 61e5440b4..97e8a1a3f 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv3D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl-base.Owl_neural_neuron_sig.Sig.DilatedConv3D)

                                                          Module Sig.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +DilatedConv3D (owl-base.Owl_neural_neuron_sig.Sig.DilatedConv3D)

                                                          Module Sig.DilatedConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable rate : int array;
                                                          6. mutable padding : Owl_types.padding;
                                                          7. mutable init_typ : Init.typ;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Dot/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Dot/index.html index 0834002dc..30a94f91c 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Dot/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Dot/index.html @@ -1,2 +1,2 @@ -Dot (owl-base.Owl_neural_neuron_sig.Sig.Dot)

                                                          Module Sig.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Dot (owl-base.Owl_neural_neuron_sig.Sig.Dot)

                                                          Module Sig.Dot

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Dropout/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Dropout/index.html index c46630417..4d04a171d 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Dropout/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Dropout/index.html @@ -1,2 +1,2 @@ -Dropout (owl-base.Owl_neural_neuron_sig.Sig.Dropout)

                                                          Module Sig.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Dropout (owl-base.Owl_neural_neuron_sig.Sig.Dropout)

                                                          Module Sig.Dropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Embedding/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Embedding/index.html index 8827b4a0c..c046f783a 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Embedding/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Embedding/index.html @@ -1,2 +1,2 @@ -Embedding (owl-base.Owl_neural_neuron_sig.Sig.Embedding)

                                                          Module Sig.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Embedding (owl-base.Owl_neural_neuron_sig.Sig.Embedding)

                                                          Module Sig.Embedding

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_dim : int;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Flatten/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Flatten/index.html index eac6dbfe3..736a83734 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Flatten/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Flatten/index.html @@ -1,2 +1,2 @@ -Flatten (owl-base.Owl_neural_neuron_sig.Sig.Flatten)

                                                          Module Sig.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Flatten (owl-base.Owl_neural_neuron_sig.Sig.Flatten)

                                                          Module Sig.Flatten

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/FullyConnected/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/FullyConnected/index.html index 2b86ea939..8e6438b74 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/FullyConnected/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/FullyConnected/index.html @@ -1,2 +1,2 @@ -FullyConnected (owl-base.Owl_neural_neuron_sig.Sig.FullyConnected)

                                                          Module Sig.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +FullyConnected (owl-base.Owl_neural_neuron_sig.Sig.FullyConnected)

                                                          Module Sig.FullyConnected

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GRU/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GRU/index.html index d0bc89ac7..ca342a491 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GRU/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GRU/index.html @@ -1,2 +1,2 @@ -GRU (owl-base.Owl_neural_neuron_sig.Sig.GRU)

                                                          Module Sig.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GRU (owl-base.Owl_neural_neuron_sig.Sig.GRU)

                                                          Module Sig.GRU

                                                          type neuron_typ = {
                                                          1. mutable wxz : Optimise.Algodiff.t;
                                                          2. mutable whz : Optimise.Algodiff.t;
                                                          3. mutable wxr : Optimise.Algodiff.t;
                                                          4. mutable whr : Optimise.Algodiff.t;
                                                          5. mutable wxh : Optimise.Algodiff.t;
                                                          6. mutable whh : Optimise.Algodiff.t;
                                                          7. mutable bz : Optimise.Algodiff.t;
                                                          8. mutable br : Optimise.Algodiff.t;
                                                          9. mutable bh : Optimise.Algodiff.t;
                                                          10. mutable h : Optimise.Algodiff.t;
                                                          11. mutable init_typ : Init.typ;
                                                          12. mutable in_shape : int array;
                                                          13. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GaussianDropout/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GaussianDropout/index.html index 02c8acb21..45a8dc257 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GaussianDropout/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GaussianDropout/index.html @@ -1,2 +1,2 @@ -GaussianDropout (owl-base.Owl_neural_neuron_sig.Sig.GaussianDropout)

                                                          Module Sig.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GaussianDropout (owl-base.Owl_neural_neuron_sig.Sig.GaussianDropout)

                                                          Module Sig.GaussianDropout

                                                          type neuron_typ = {
                                                          1. mutable rate : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GaussianNoise/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GaussianNoise/index.html index f3bb69b61..bfa535f0d 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GaussianNoise/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GaussianNoise/index.html @@ -1,2 +1,2 @@ -GaussianNoise (owl-base.Owl_neural_neuron_sig.Sig.GaussianNoise)

                                                          Module Sig.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GaussianNoise (owl-base.Owl_neural_neuron_sig.Sig.GaussianNoise)

                                                          Module Sig.GaussianNoise

                                                          type neuron_typ = {
                                                          1. mutable sigma : float;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : float -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalAvgPool1D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalAvgPool1D/index.html index f16c2b78b..f548eeffc 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalAvgPool1D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalAvgPool1D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool1D (owl-base.Owl_neural_neuron_sig.Sig.GlobalAvgPool1D)

                                                          Module Sig.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalAvgPool1D (owl-base.Owl_neural_neuron_sig.Sig.GlobalAvgPool1D)

                                                          Module Sig.GlobalAvgPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalAvgPool2D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalAvgPool2D/index.html index f38fcf24a..c34e965a6 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalAvgPool2D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalAvgPool2D/index.html @@ -1,2 +1,2 @@ -GlobalAvgPool2D (owl-base.Owl_neural_neuron_sig.Sig.GlobalAvgPool2D)

                                                          Module Sig.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalAvgPool2D (owl-base.Owl_neural_neuron_sig.Sig.GlobalAvgPool2D)

                                                          Module Sig.GlobalAvgPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalMaxPool1D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalMaxPool1D/index.html index f67d442e4..9311e871e 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalMaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalMaxPool1D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool1D (owl-base.Owl_neural_neuron_sig.Sig.GlobalMaxPool1D)

                                                          Module Sig.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalMaxPool1D (owl-base.Owl_neural_neuron_sig.Sig.GlobalMaxPool1D)

                                                          Module Sig.GlobalMaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalMaxPool2D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalMaxPool2D/index.html index 97fe027c1..a234bd954 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalMaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/GlobalMaxPool2D/index.html @@ -1,2 +1,2 @@ -GlobalMaxPool2D (owl-base.Owl_neural_neuron_sig.Sig.GlobalMaxPool2D)

                                                          Module Sig.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +GlobalMaxPool2D (owl-base.Owl_neural_neuron_sig.Sig.GlobalMaxPool2D)

                                                          Module Sig.GlobalMaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Init/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Init/index.html index 443d77e75..776f12a80 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Init/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Init/index.html @@ -1,2 +1,2 @@ -Init (owl-base.Owl_neural_neuron_sig.Sig.Init)

                                                          Module Sig.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                            (*

                                                            Initialisation types

                                                            *)
                                                          val calc_fans : int array -> float * float

                                                          Calculate fan-in and fan-out of weights.

                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Init (owl-base.Owl_neural_neuron_sig.Sig.Init)

                                                          Module Sig.Init

                                                          type typ =
                                                          1. | Uniform of float * float
                                                          2. | Gaussian of float * float
                                                          3. | Standard
                                                          4. | Tanh
                                                          5. | GlorotNormal
                                                          6. | GlorotUniform
                                                          7. | LecunNormal
                                                          8. | HeNormal
                                                          9. | Custom of int array -> Optimise.Algodiff.t
                                                            (*

                                                            Initialisation types

                                                            *)
                                                          val calc_fans : int array -> float * float

                                                          Calculate fan-in and fan-out of weights.

                                                          val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Input/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Input/index.html index 13d380d9f..a91aa83a6 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Input/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Input/index.html @@ -1,2 +1,2 @@ -Input (owl-base.Owl_neural_neuron_sig.Sig.Input)

                                                          Module Sig.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Input (owl-base.Owl_neural_neuron_sig.Sig.Input)

                                                          Module Sig.Input

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LSTM/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LSTM/index.html index 27063ecd4..2f8bd8f6e 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LSTM/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LSTM/index.html @@ -1,2 +1,2 @@ -LSTM (owl-base.Owl_neural_neuron_sig.Sig.LSTM)

                                                          Module Sig.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +LSTM (owl-base.Owl_neural_neuron_sig.Sig.LSTM)

                                                          Module Sig.LSTM

                                                          type neuron_typ = {
                                                          1. mutable wxi : Optimise.Algodiff.t;
                                                          2. mutable whi : Optimise.Algodiff.t;
                                                          3. mutable wxc : Optimise.Algodiff.t;
                                                          4. mutable whc : Optimise.Algodiff.t;
                                                          5. mutable wxf : Optimise.Algodiff.t;
                                                          6. mutable whf : Optimise.Algodiff.t;
                                                          7. mutable wxo : Optimise.Algodiff.t;
                                                          8. mutable who : Optimise.Algodiff.t;
                                                          9. mutable bi : Optimise.Algodiff.t;
                                                          10. mutable bc : Optimise.Algodiff.t;
                                                          11. mutable bf : Optimise.Algodiff.t;
                                                          12. mutable bo : Optimise.Algodiff.t;
                                                          13. mutable c : Optimise.Algodiff.t;
                                                          14. mutable h : Optimise.Algodiff.t;
                                                          15. mutable init_typ : Init.typ;
                                                          16. mutable in_shape : int array;
                                                          17. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Lambda/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Lambda/index.html index e258d0244..ceefd9600 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Lambda/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl-base.Owl_neural_neuron_sig.Sig.Lambda)

                                                          Module Sig.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Lambda (owl-base.Owl_neural_neuron_sig.Sig.Lambda)

                                                          Module Sig.Lambda

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?out_shape:int array -> (Optimise.Algodiff.t -> Optimise.Algodiff.t) -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LambdaArray/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LambdaArray/index.html index f8f9380e1..0411581fe 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LambdaArray/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl-base.Owl_neural_neuron_sig.Sig.LambdaArray)

                                                          Module Sig.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +LambdaArray (owl-base.Owl_neural_neuron_sig.Sig.LambdaArray)

                                                          Module Sig.LambdaArray

                                                          type neuron_typ = {
                                                          1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> (Optimise.Algodiff.t array -> Optimise.Algodiff.t) -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Linear/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Linear/index.html index 6b614c6ae..f6243aab4 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Linear/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Linear/index.html @@ -1,2 +1,2 @@ -Linear (owl-base.Owl_neural_neuron_sig.Sig.Linear)

                                                          Module Sig.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Linear (owl-base.Owl_neural_neuron_sig.Sig.Linear)

                                                          Module Sig.Linear

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable init_typ : Init.typ;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LinearNoBias/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LinearNoBias/index.html index d6faa454c..a8fbc7125 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LinearNoBias/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/LinearNoBias/index.html @@ -1,2 +1,2 @@ -LinearNoBias (owl-base.Owl_neural_neuron_sig.Sig.LinearNoBias)

                                                          Module Sig.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +LinearNoBias (owl-base.Owl_neural_neuron_sig.Sig.LinearNoBias)

                                                          Module Sig.LinearNoBias

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable init_typ : Init.typ;
                                                          3. mutable in_shape : int array;
                                                          4. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int -> int -> Init.typ -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron_typ -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron_typ -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron_typ -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the parameters in an array, used by Optimise module.

                                                          val mkpri : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the primial values in an array, used by Optimise module.

                                                          val mkadj : neuron_typ -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron_typ -> Optimise.Algodiff.t array -> unit

                                                          Update parameters in a neuron, used by Optimise module.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Masking/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Masking/index.html index 585de0ea0..a159e0bfd 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Masking/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl-base.Owl_neural_neuron_sig.Sig.Masking)

                                                          Module Sig.Masking

                                                          +Masking (owl-base.Owl_neural_neuron_sig.Sig.Masking)

                                                          Module Sig.Masking

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Max/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Max/index.html index 5829fcad0..0cee44364 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Max/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Max/index.html @@ -1,2 +1,2 @@ -Max (owl-base.Owl_neural_neuron_sig.Sig.Max)

                                                          Module Sig.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Max (owl-base.Owl_neural_neuron_sig.Sig.Max)

                                                          Module Sig.Max

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/MaxPool1D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/MaxPool1D/index.html index c994ff252..5470fbf47 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/MaxPool1D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/MaxPool1D/index.html @@ -1,2 +1,2 @@ -MaxPool1D (owl-base.Owl_neural_neuron_sig.Sig.MaxPool1D)

                                                          Module Sig.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +MaxPool1D (owl-base.Owl_neural_neuron_sig.Sig.MaxPool1D)

                                                          Module Sig.MaxPool1D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/MaxPool2D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/MaxPool2D/index.html index 3775b58b8..44f56d0f6 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/MaxPool2D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/MaxPool2D/index.html @@ -1,2 +1,2 @@ -MaxPool2D (owl-base.Owl_neural_neuron_sig.Sig.MaxPool2D)

                                                          Module Sig.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +MaxPool2D (owl-base.Owl_neural_neuron_sig.Sig.MaxPool2D)

                                                          Module Sig.MaxPool2D

                                                          type neuron_typ = {
                                                          1. mutable padding : Owl_types.padding;
                                                          2. mutable kernel : int array;
                                                          3. mutable stride : int array;
                                                          4. mutable in_shape : int array;
                                                          5. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : Owl_types.padding -> int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Mul/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Mul/index.html index 22faf4090..43bf6542e 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Mul/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Mul/index.html @@ -1,2 +1,2 @@ -Mul (owl-base.Owl_neural_neuron_sig.Sig.Mul)

                                                          Module Sig.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Mul (owl-base.Owl_neural_neuron_sig.Sig.Mul)

                                                          Module Sig.Mul

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : unit -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : 'a -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Normalisation/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Normalisation/index.html index 675d9f6fa..3026ecb48 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Normalisation/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl-base.Owl_neural_neuron_sig.Sig.Normalisation)

                                                          Module Sig.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Normalisation (owl-base.Owl_neural_neuron_sig.Sig.Normalisation)

                                                          Module Sig.Normalisation

                                                          type neuron_typ = {
                                                          1. mutable axis : int;
                                                          2. mutable beta : Optimise.Algodiff.t;
                                                          3. mutable gamma : Optimise.Algodiff.t;
                                                          4. mutable mu : Optimise.Algodiff.t;
                                                          5. mutable var : Optimise.Algodiff.t;
                                                          6. mutable decay : Optimise.Algodiff.t;
                                                          7. mutable training : bool;
                                                          8. mutable in_shape : int array;
                                                          9. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?training:bool -> ?decay:float -> ?mu:Optimise.Algodiff.A.arr -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Linalg/index.html index e4772023b..c79cac2ab 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Mat/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Mat/index.html index d1eea0f97..f7807899c 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Scalar/index.html index b8d216451..3685312cf 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/index.html index 671c44a69..f11b3874b 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Arr/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Arr/index.html index 2e84f6736..a7999cf65 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          +Arr (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/index.html index 3c29614d0..1a19f3702 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          +Builder (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Aiso/index.html index b4c0614b6..444c1c4b3 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          +Aiso (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Piso/index.html index cd260ab0a..503687311 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          +Piso (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siao/index.html index a82c9bbd8..1992f7046 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          +Siao (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 7bbecd379..01ef43f1e 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sipo (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siso/index.html index ef692dfd2..5d8db5815 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          +Siso (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sito/index.html index 03b088451..3996f64b1 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sito (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Linalg/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Linalg/index.html index d5140de8c..089591e60 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Mat/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Mat/index.html index 0968a0d25..bbc83e0c1 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          +Mat (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Maths/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Maths/index.html index 178957a2f..6b66e822e 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          +Maths (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/NN/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/NN/index.html index 6051369e8..e01236f79 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/NN/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : +NN (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/index.html index b53a4ee89..953876238 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Algodiff/index.html @@ -1,5 +1,5 @@ -Algodiff (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig +Algodiff (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Algodiff)

                                                          Module Optimise.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Batch/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Batch/index.html index 8edc5626a..4bef60c8b 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Batch/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Batch (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Batch)

                                                          Module Optimise.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Checkpoint/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Checkpoint/index.html index a6dc78048..2bb9a255a 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Checkpoint/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Checkpoint (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Checkpoint)

                                                          Module Optimise.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Clipping/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Clipping/index.html index 3a7f3c97c..bcf4993b8 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Clipping/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Clipping (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Clipping)

                                                          Module Optimise.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Gradient/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Gradient/index.html index 45e984c4f..de808f613 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Gradient/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : +Gradient (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Gradient)

                                                          Module Optimise.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Learning_Rate/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Learning_Rate/index.html index f30059d27..cad50240c 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Learning_Rate/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Learning_Rate (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Learning_Rate)

                                                          Module Optimise.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Loss/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Loss/index.html index d582fbcd8..70a4cd6c7 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Loss/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Loss (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Loss)

                                                          Module Optimise.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Momentum/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Momentum/index.html index 1963091fc..4a6751187 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Momentum/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Momentum (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Momentum)

                                                          Module Optimise.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Params/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Params/index.html index 98353e0c2..a9b918e27 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Params/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : +Params (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Params)

                                                          Module Optimise.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Regularisation/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Regularisation/index.html index aae3b480c..7e7ee024c 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Regularisation/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Regularisation (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Regularisation)

                                                          Module Optimise.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Stopping/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Stopping/index.html index caeec14d8..de48ff79f 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Stopping/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Stopping (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Stopping)

                                                          Module Optimise.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Utils/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Utils/index.html index b079f7e36..893d66f58 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Utils/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : +Utils (owl-base.Owl_neural_neuron_sig.Sig.Optimise.Utils)

                                                          Module Optimise.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/index.html index 59f787a96..398ed9e35 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl-base.Owl_neural_neuron_sig.Sig.Optimise)

                                                          Module Sig.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : +Optimise (owl-base.Owl_neural_neuron_sig.Sig.Optimise)

                                                          Module Sig.Optimise

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> @@ -28,4 +28,4 @@ (string -> unit) -> Algodiff.t -> Algodiff.t -> - Checkpoint.state

                                                          TODO

                                                          + Checkpoint.state

                                                          This function is minimize the weights in a compiled neural network of graph structure.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding1D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding1D/index.html index 0eb23f111..4d0f94898 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding1D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl-base.Owl_neural_neuron_sig.Sig.Padding1D)

                                                          Module Sig.Padding1D

                                                          +Padding1D (owl-base.Owl_neural_neuron_sig.Sig.Padding1D)

                                                          Module Sig.Padding1D

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding2D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding2D/index.html index 40a2c1654..1c09c8e1f 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding2D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding2D/index.html @@ -1,2 +1,2 @@ -Padding2D (owl-base.Owl_neural_neuron_sig.Sig.Padding2D)

                                                          Module Sig.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Padding2D (owl-base.Owl_neural_neuron_sig.Sig.Padding2D)

                                                          Module Sig.Padding2D

                                                          type neuron_typ = {
                                                          1. mutable padding : int array array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding3D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding3D/index.html index d968ae4cc..b1f28e406 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding3D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl-base.Owl_neural_neuron_sig.Sig.Padding3D)

                                                          Module Sig.Padding3D

                                                          +Padding3D (owl-base.Owl_neural_neuron_sig.Sig.Padding3D)

                                                          Module Sig.Padding3D

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Recurrent/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Recurrent/index.html index 949855312..eba11b578 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Recurrent/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl-base.Owl_neural_neuron_sig.Sig.Recurrent)

                                                          Module Sig.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +Recurrent (owl-base.Owl_neural_neuron_sig.Sig.Recurrent)

                                                          Module Sig.Recurrent

                                                          type neuron_typ = {
                                                          1. mutable whh : Optimise.Algodiff.t;
                                                          2. mutable wxh : Optimise.Algodiff.t;
                                                          3. mutable why : Optimise.Algodiff.t;
                                                          4. mutable bh : Optimise.Algodiff.t;
                                                          5. mutable by : Optimise.Algodiff.t;
                                                          6. mutable h : Optimise.Algodiff.t;
                                                          7. mutable hiddens : int;
                                                          8. mutable act : Activation.typ;
                                                          9. mutable init_typ : Init.typ;
                                                          10. mutable in_shape : int array;
                                                          11. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?time_steps:int -> ?inputs:int -> int -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Reshape/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Reshape/index.html index 1a000d6e5..3391a2b74 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Reshape/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Reshape/index.html @@ -1,2 +1,2 @@ -Reshape (owl-base.Owl_neural_neuron_sig.Sig.Reshape)

                                                          Module Sig.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Reshape (owl-base.Owl_neural_neuron_sig.Sig.Reshape)

                                                          Module Sig.Reshape

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Slice/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Slice/index.html index 2b75c062a..d820c6670 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Slice/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/Slice/index.html @@ -1,2 +1,2 @@ -Slice (owl-base.Owl_neural_neuron_sig.Sig.Slice)

                                                          Module Sig.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }

                                                          Neuron type definition.

                                                          val create : int list list -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +Slice (owl-base.Owl_neural_neuron_sig.Sig.Slice)

                                                          Module Sig.Slice

                                                          type neuron_typ = {
                                                          1. mutable in_shape : int array;
                                                          2. mutable out_shape : int array;
                                                          3. mutable slice : int list list;
                                                          }

                                                          Neuron type definition.

                                                          val create : int list list -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv1D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv1D/index.html index 27d041033..9452a67a4 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv1D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl-base.Owl_neural_neuron_sig.Sig.TransposeConv1D)

                                                          Module Sig.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv1D (owl-base.Owl_neural_neuron_sig.Sig.TransposeConv1D)

                                                          Module Sig.TransposeConv1D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv2D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv2D/index.html index 9be3c25be..d9fb9bd7f 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv2D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl-base.Owl_neural_neuron_sig.Sig.TransposeConv2D)

                                                          Module Sig.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv2D (owl-base.Owl_neural_neuron_sig.Sig.TransposeConv2D)

                                                          Module Sig.TransposeConv2D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv3D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv3D/index.html index 3bfd416ab..513fc240e 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv3D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl-base.Owl_neural_neuron_sig.Sig.TransposeConv3D)

                                                          Module Sig.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : +TransposeConv3D (owl-base.Owl_neural_neuron_sig.Sig.TransposeConv3D)

                                                          Module Sig.TransposeConv3D

                                                          type neuron_typ = {
                                                          1. mutable w : Optimise.Algodiff.t;
                                                          2. mutable b : Optimise.Algodiff.t;
                                                          3. mutable kernel : int array;
                                                          4. mutable stride : int array;
                                                          5. mutable padding : Owl_types.padding;
                                                          6. mutable init_typ : Init.typ;
                                                          7. mutable in_shape : int array;
                                                          8. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : ?inputs:int array -> Owl_types.padding -> int array -> diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling1D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling1D/index.html index 64e55f501..69a804fe1 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling1D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl-base.Owl_neural_neuron_sig.Sig.UpSampling1D)

                                                          Module Sig.UpSampling1D

                                                          +UpSampling1D (owl-base.Owl_neural_neuron_sig.Sig.UpSampling1D)

                                                          Module Sig.UpSampling1D

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling2D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling2D/index.html index acb200392..b92a70929 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling2D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling2D/index.html @@ -1,2 +1,2 @@ -UpSampling2D (owl-base.Owl_neural_neuron_sig.Sig.UpSampling2D)

                                                          Module Sig.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          +UpSampling2D (owl-base.Owl_neural_neuron_sig.Sig.UpSampling2D)

                                                          Module Sig.UpSampling2D

                                                          type neuron_typ = {
                                                          1. mutable size : int array;
                                                          2. mutable in_shape : int array;
                                                          3. mutable out_shape : int array;
                                                          }

                                                          Neuron type definition.

                                                          val create : int array -> neuron_typ

                                                          Create the neuron.

                                                          val connect : int array -> neuron_typ -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val copy : neuron_typ -> neuron_typ

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron_typ -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : unit -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling3D/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling3D/index.html index c06894a19..c5f3898e5 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling3D/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl-base.Owl_neural_neuron_sig.Sig.UpSampling3D)

                                                          Module Sig.UpSampling3D

                                                          +UpSampling3D (owl-base.Owl_neural_neuron_sig.Sig.UpSampling3D)

                                                          Module Sig.UpSampling3D

                                                          diff --git a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/index.html b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/index.html index b0a5c2f62..f5705c069 100644 --- a/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_neural_neuron_sig/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_neural_neuron_sig.Sig)

                                                          Module type Owl_neural_neuron_sig.Sig

                                                          Init neuron
                                                          module Init : sig ... end
                                                          Input neuron
                                                          module Input : sig ... end
                                                          Activation neuron
                                                          module Activation : sig ... end
                                                          Linear neuron
                                                          module Linear : sig ... end
                                                          LinearNoBias neuron
                                                          module LinearNoBias : sig ... end
                                                          Recurrent neuron
                                                          module Recurrent : sig ... end
                                                          LSTM neuron
                                                          module LSTM : sig ... end
                                                          GRU neuron
                                                          module GRU : sig ... end
                                                          Conv1D neuron
                                                          module Conv1D : sig ... end
                                                          Conv2D neuron
                                                          module Conv2D : sig ... end
                                                          Conv3D neuron
                                                          module Conv3D : sig ... end
                                                          DilatedConv1D neuron
                                                          module DilatedConv1D : sig ... end
                                                          DilatedConv2D neuron
                                                          module DilatedConv2D : sig ... end
                                                          DilatedConv3D neuron
                                                          module DilatedConv3D : sig ... end
                                                          TransposeConv1D neuron
                                                          module TransposeConv1D : sig ... end
                                                          TransposeConv2D neuron
                                                          module TransposeConv2D : sig ... end
                                                          TransposeConv3D neuron
                                                          module TransposeConv3D : sig ... end
                                                          FullyConnected neuron
                                                          module FullyConnected : sig ... end
                                                          MaxPool1D neuron
                                                          module MaxPool1D : sig ... end
                                                          MaxPool2D neuron
                                                          module MaxPool2D : sig ... end
                                                          AvgPool1D neuron
                                                          module AvgPool1D : sig ... end
                                                          AvgPool2D neuron
                                                          module AvgPool2D : sig ... end
                                                          GlobalMaxPool1D neuron
                                                          module GlobalMaxPool1D : sig ... end
                                                          GlobalMaxPool2D neuron
                                                          module GlobalMaxPool2D : sig ... end
                                                          GlobalAvgPool1D neuron
                                                          module GlobalAvgPool1D : sig ... end
                                                          GlobalAvgPool2D neuron
                                                          module GlobalAvgPool2D : sig ... end
                                                          UpSampling1D neuron
                                                          module UpSampling1D : sig ... end
                                                          UpSampling2D neuron
                                                          module UpSampling2D : sig ... end
                                                          UpSampling3D neuron
                                                          module UpSampling3D : sig ... end
                                                          Padding1D neuron
                                                          module Padding1D : sig ... end
                                                          Padding2D neuron
                                                          module Padding2D : sig ... end
                                                          Padding3D neuron
                                                          module Padding3D : sig ... end
                                                          Lambda neuron
                                                          module Lambda : sig ... end
                                                          LambdaArray neuron
                                                          module LambdaArray : sig ... end
                                                          Dropout neuron
                                                          module Dropout : sig ... end
                                                          Reshape neuron
                                                          module Reshape : sig ... end
                                                          Flatten neuron
                                                          module Flatten : sig ... end
                                                          Slice neuron
                                                          module Slice : sig ... end
                                                          Add neuron
                                                          module Add : sig ... end
                                                          Mul neuron
                                                          module Mul : sig ... end
                                                          Dot neuron
                                                          module Dot : sig ... end
                                                          Max neuron
                                                          module Max : sig ... end
                                                          Average neuron
                                                          module Average : sig ... end
                                                          Concatenate neuron
                                                          module Concatenate : sig ... end
                                                          Normalisation neuron
                                                          module Normalisation : sig ... end
                                                          GaussianNoise neuron
                                                          module GaussianNoise : sig ... end
                                                          GaussianDropout neuron
                                                          module GaussianDropout : sig ... end
                                                          AlphaDropout neuron
                                                          module AlphaDropout : sig ... end
                                                          Embedding neuron
                                                          module Embedding : sig ... end
                                                          Masking neuron
                                                          module Masking : sig ... end
                                                          Core functions
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                            (*

                                                            Types of neuron.

                                                            *)
                                                          val get_in_out_shape : neuron -> int array * int array

                                                          Get both input and output shapes of a neuron.

                                                          val get_in_shape : neuron -> int array

                                                          Get the input shape of a neuron.

                                                          val get_out_shape : neuron -> int array

                                                          Get the output shape of a neuron.

                                                          val connect : int array array -> neuron -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the trainable parameters in an array, used by Optimise module.

                                                          val mkpri : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the primal values in an array, used by Optimise module.

                                                          val mkadj : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron -> Optimise.Algodiff.t array -> unit

                                                          Update trainable parameters in a neuron, used by Optimise module.

                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit

                                                          Load both trainable and non-trainable parameters into the neuron.

                                                          val save_weights : neuron -> Optimise.Algodiff.t array

                                                          Assemble both trainable and non-trainable parameters of the neuron.

                                                          val copy : neuron -> neuron

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : neuron -> string

                                                          Return the name of the neuron.

                                                          +Sig (owl-base.Owl_neural_neuron_sig.Sig)

                                                          Module type Owl_neural_neuron_sig.Sig

                                                          Init neuron
                                                          module Init : sig ... end
                                                          Input neuron
                                                          module Input : sig ... end
                                                          Activation neuron
                                                          module Activation : sig ... end
                                                          Linear neuron
                                                          module Linear : sig ... end
                                                          LinearNoBias neuron
                                                          module LinearNoBias : sig ... end
                                                          Recurrent neuron
                                                          module Recurrent : sig ... end
                                                          LSTM neuron
                                                          module LSTM : sig ... end
                                                          GRU neuron
                                                          module GRU : sig ... end
                                                          Conv1D neuron
                                                          module Conv1D : sig ... end
                                                          Conv2D neuron
                                                          module Conv2D : sig ... end
                                                          Conv3D neuron
                                                          module Conv3D : sig ... end
                                                          DilatedConv1D neuron
                                                          module DilatedConv1D : sig ... end
                                                          DilatedConv2D neuron
                                                          module DilatedConv2D : sig ... end
                                                          DilatedConv3D neuron
                                                          module DilatedConv3D : sig ... end
                                                          TransposeConv1D neuron
                                                          module TransposeConv1D : sig ... end
                                                          TransposeConv2D neuron
                                                          module TransposeConv2D : sig ... end
                                                          TransposeConv3D neuron
                                                          module TransposeConv3D : sig ... end
                                                          FullyConnected neuron
                                                          module FullyConnected : sig ... end
                                                          MaxPool1D neuron
                                                          module MaxPool1D : sig ... end
                                                          MaxPool2D neuron
                                                          module MaxPool2D : sig ... end
                                                          AvgPool1D neuron
                                                          module AvgPool1D : sig ... end
                                                          AvgPool2D neuron
                                                          module AvgPool2D : sig ... end
                                                          GlobalMaxPool1D neuron
                                                          module GlobalMaxPool1D : sig ... end
                                                          GlobalMaxPool2D neuron
                                                          module GlobalMaxPool2D : sig ... end
                                                          GlobalAvgPool1D neuron
                                                          module GlobalAvgPool1D : sig ... end
                                                          GlobalAvgPool2D neuron
                                                          module GlobalAvgPool2D : sig ... end
                                                          UpSampling1D neuron
                                                          module UpSampling1D : sig ... end
                                                          UpSampling2D neuron
                                                          module UpSampling2D : sig ... end
                                                          UpSampling3D neuron
                                                          module UpSampling3D : sig ... end
                                                          Padding1D neuron
                                                          module Padding1D : sig ... end
                                                          Padding2D neuron
                                                          module Padding2D : sig ... end
                                                          Padding3D neuron
                                                          module Padding3D : sig ... end
                                                          Lambda neuron
                                                          module Lambda : sig ... end
                                                          LambdaArray neuron
                                                          module LambdaArray : sig ... end
                                                          Dropout neuron
                                                          module Dropout : sig ... end
                                                          Reshape neuron
                                                          module Reshape : sig ... end
                                                          Flatten neuron
                                                          module Flatten : sig ... end
                                                          Slice neuron
                                                          module Slice : sig ... end
                                                          Add neuron
                                                          module Add : sig ... end
                                                          Mul neuron
                                                          module Mul : sig ... end
                                                          Dot neuron
                                                          module Dot : sig ... end
                                                          Max neuron
                                                          module Max : sig ... end
                                                          Average neuron
                                                          module Average : sig ... end
                                                          Concatenate neuron
                                                          module Concatenate : sig ... end
                                                          Normalisation neuron
                                                          module Normalisation : sig ... end
                                                          GaussianNoise neuron
                                                          module GaussianNoise : sig ... end
                                                          GaussianDropout neuron
                                                          module GaussianDropout : sig ... end
                                                          AlphaDropout neuron
                                                          module AlphaDropout : sig ... end
                                                          Embedding neuron
                                                          module Embedding : sig ... end
                                                          Masking neuron
                                                          module Masking : sig ... end
                                                          Core functions
                                                          type neuron =
                                                          1. | Input of Input.neuron_typ
                                                          2. | Linear of Linear.neuron_typ
                                                          3. | LinearNoBias of LinearNoBias.neuron_typ
                                                          4. | Embedding of Embedding.neuron_typ
                                                          5. | LSTM of LSTM.neuron_typ
                                                          6. | GRU of GRU.neuron_typ
                                                          7. | Recurrent of Recurrent.neuron_typ
                                                          8. | Conv1D of Conv1D.neuron_typ
                                                          9. | Conv2D of Conv2D.neuron_typ
                                                          10. | Conv3D of Conv3D.neuron_typ
                                                          11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                          12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                          13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                          14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                          15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                          16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                          17. | FullyConnected of FullyConnected.neuron_typ
                                                          18. | MaxPool1D of MaxPool1D.neuron_typ
                                                          19. | MaxPool2D of MaxPool2D.neuron_typ
                                                          20. | AvgPool1D of AvgPool1D.neuron_typ
                                                          21. | AvgPool2D of AvgPool2D.neuron_typ
                                                          22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                          23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                          24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                          25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                          26. | UpSampling2D of UpSampling2D.neuron_typ
                                                          27. | Padding2D of Padding2D.neuron_typ
                                                          28. | Dropout of Dropout.neuron_typ
                                                          29. | Reshape of Reshape.neuron_typ
                                                          30. | Flatten of Flatten.neuron_typ
                                                          31. | Slice of Slice.neuron_typ
                                                          32. | Lambda of Lambda.neuron_typ
                                                          33. | LambdaArray of LambdaArray.neuron_typ
                                                          34. | Activation of Activation.neuron_typ
                                                          35. | GaussianNoise of GaussianNoise.neuron_typ
                                                          36. | GaussianDropout of GaussianDropout.neuron_typ
                                                          37. | AlphaDropout of AlphaDropout.neuron_typ
                                                          38. | Normalisation of Normalisation.neuron_typ
                                                          39. | Add of Add.neuron_typ
                                                          40. | Mul of Mul.neuron_typ
                                                          41. | Dot of Dot.neuron_typ
                                                          42. | Max of Max.neuron_typ
                                                          43. | Average of Average.neuron_typ
                                                          44. | Concatenate of Concatenate.neuron_typ
                                                            (*

                                                            Types of neuron.

                                                            *)
                                                          val get_in_out_shape : neuron -> int array * int array

                                                          Get both input and output shapes of a neuron.

                                                          val get_in_shape : neuron -> int array

                                                          Get the input shape of a neuron.

                                                          val get_out_shape : neuron -> int array

                                                          Get the output shape of a neuron.

                                                          val connect : int array array -> neuron -> unit

                                                          Connect this neuron to others in a neural network.

                                                          val init : neuron -> unit

                                                          Initialise the neuron and its parameters.

                                                          val reset : neuron -> unit

                                                          Reset the parameters in a neuron.

                                                          val mktag : int -> neuron -> unit

                                                          Tag the neuron, used by Algodiff module.

                                                          val mkpar : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the trainable parameters in an array, used by Optimise module.

                                                          val mkpri : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the primal values in an array, used by Optimise module.

                                                          val mkadj : neuron -> Optimise.Algodiff.t array

                                                          Assemble all the adjacent values in an array, used by Optimise module.

                                                          val update : neuron -> Optimise.Algodiff.t array -> unit

                                                          Update trainable parameters in a neuron, used by Optimise module.

                                                          val load_weights : neuron -> Optimise.Algodiff.t array -> unit

                                                          Load both trainable and non-trainable parameters into the neuron.

                                                          val save_weights : neuron -> Optimise.Algodiff.t array

                                                          Assemble both trainable and non-trainable parameters of the neuron.

                                                          val copy : neuron -> neuron

                                                          Make a deep copy of the neuron and its parameters.

                                                          Execute the computation in this neuron.

                                                          val to_string : neuron -> string

                                                          Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron.

                                                          val to_name : neuron -> string

                                                          Return the name of the neuron.

                                                          diff --git a/docs/owl-base/Owl_numdiff_generic/Make/argument-1-A/index.html b/docs/owl-base/Owl_numdiff_generic/Make/argument-1-A/index.html index 41a0c8f6c..1dca8f86f 100644 --- a/docs/owl-base/Owl_numdiff_generic/Make/argument-1-A/index.html +++ b/docs/owl-base/Owl_numdiff_generic/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_numdiff_generic.Make.A)

                                                          Parameter Make.A

                                                          include Owl_types_ndarray_numdiff.Sig with type elt = float
                                                          include Owl_types_ndarray_basic.Sig with type elt = float
                                                          type arr
                                                          type elt = float
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_numdiff_generic.Make.A)

                                                          Parameter Make.A

                                                          include Owl_types_ndarray_numdiff.Sig with type elt = float
                                                          include Owl_types_ndarray_basic.Sig with type elt = float
                                                          type arr
                                                          type elt = float
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_numdiff_generic/Make/index.html b/docs/owl-base/Owl_numdiff_generic/Make/index.html index e132a576c..3ceb2801e 100644 --- a/docs/owl-base/Owl_numdiff_generic/Make/index.html +++ b/docs/owl-base/Owl_numdiff_generic/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_numdiff_generic.Make)

                                                          Module Owl_numdiff_generic.Make

                                                          Parameters

                                                          module A : Owl_types.Ndarray_Numdiff with type elt = float

                                                          Signature

                                                          type arr = A.arr
                                                          type elt = A.elt
                                                          val _eps : float
                                                          val _ep1 : float
                                                          val _ep2 : float
                                                          val diff : (float -> float) -> float -> float
                                                          val diff' : (float -> float) -> float -> float * float
                                                          val diff2 : (float -> float) -> float -> float
                                                          val diff2' : (float -> float) -> float -> float * float
                                                          val grad' : (A.arr -> A.elt) -> A.arr -> A.arr * A.arr
                                                          val grad : (A.arr -> A.elt) -> A.arr -> A.arr
                                                          val jacobianT' : (A.arr -> A.arr) -> A.arr -> A.arr * A.arr
                                                          val jacobianT : (A.arr -> A.arr) -> A.arr -> A.arr
                                                          val jacobian' : (A.arr -> A.arr) -> A.arr -> A.arr * A.arr
                                                          val jacobian : (A.arr -> A.arr) -> A.arr -> A.arr
                                                          +Make (owl-base.Owl_numdiff_generic.Make)

                                                          Module Owl_numdiff_generic.Make

                                                          Parameters

                                                          module A : Owl_types.Ndarray_Numdiff with type elt = float

                                                          Signature

                                                          type arr = A.arr
                                                          type elt = A.elt
                                                          val _eps : float
                                                          val _ep1 : float
                                                          val _ep2 : float
                                                          val diff : (float -> float) -> float -> float
                                                          val diff' : (float -> float) -> float -> float * float
                                                          val diff2 : (float -> float) -> float -> float
                                                          val diff2' : (float -> float) -> float -> float * float
                                                          val grad' : (A.arr -> A.elt) -> A.arr -> A.arr * A.arr
                                                          val grad : (A.arr -> A.elt) -> A.arr -> A.arr
                                                          val jacobianT' : (A.arr -> A.arr) -> A.arr -> A.arr * A.arr
                                                          val jacobianT : (A.arr -> A.arr) -> A.arr -> A.arr
                                                          val jacobian' : (A.arr -> A.arr) -> A.arr -> A.arr * A.arr
                                                          val jacobian : (A.arr -> A.arr) -> A.arr -> A.arr
                                                          diff --git a/docs/owl-base/Owl_numdiff_generic/index.html b/docs/owl-base/Owl_numdiff_generic/index.html index 8eb3c8679..4159ebdf0 100644 --- a/docs/owl-base/Owl_numdiff_generic/index.html +++ b/docs/owl-base/Owl_numdiff_generic/index.html @@ -1,2 +1,2 @@ -Owl_numdiff_generic (owl-base.Owl_numdiff_generic)

                                                          Module Owl_numdiff_generic

                                                          module Make (A : Owl_types.Ndarray_Numdiff with type elt = float) : sig ... end
                                                          +Owl_numdiff_generic (owl-base.Owl_numdiff_generic)

                                                          Module Owl_numdiff_generic

                                                          module Make (A : Owl_types.Ndarray_Numdiff with type elt = float) : sig ... end
                                                          diff --git a/docs/owl-base/Owl_numdiff_generic_sig/Impl/argument-1-A/index.html b/docs/owl-base/Owl_numdiff_generic_sig/Impl/argument-1-A/index.html index 95ca35f6d..4c793927b 100644 --- a/docs/owl-base/Owl_numdiff_generic_sig/Impl/argument-1-A/index.html +++ b/docs/owl-base/Owl_numdiff_generic_sig/Impl/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_numdiff_generic_sig.Impl.A)

                                                          Parameter Impl.A

                                                          include Owl_types_ndarray_numdiff.Sig with type elt = float
                                                          include Owl_types_ndarray_basic.Sig with type elt = float
                                                          type arr
                                                          type elt = float
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_numdiff_generic_sig.Impl.A)

                                                          Parameter Impl.A

                                                          include Owl_types_ndarray_numdiff.Sig with type elt = float
                                                          include Owl_types_ndarray_basic.Sig with type elt = float
                                                          type arr
                                                          type elt = float
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_numdiff_generic_sig/Impl/index.html b/docs/owl-base/Owl_numdiff_generic_sig/Impl/index.html index e0245dc0d..5e6f43ec5 100644 --- a/docs/owl-base/Owl_numdiff_generic_sig/Impl/index.html +++ b/docs/owl-base/Owl_numdiff_generic_sig/Impl/index.html @@ -1,2 +1,2 @@ -Impl (owl-base.Owl_numdiff_generic_sig.Impl)

                                                          Module Owl_numdiff_generic_sig.Impl

                                                          Parameters

                                                          module A : Owl_types.Ndarray_Numdiff with type elt = float

                                                          Signature

                                                          Type definition
                                                          type arr

                                                          General ndarray type

                                                          type elt

                                                          Scalar type

                                                          Basic functions
                                                          val diff : (elt -> elt) -> elt -> elt

                                                          derivative of f : scalar -> scalar.

                                                          val diff' : (elt -> elt) -> elt -> elt * elt

                                                          derivative of f : scalar -> scalar, return both f x and f' x.

                                                          val diff2 : (elt -> elt) -> elt -> elt

                                                          second order derivative of f : float -> float.

                                                          val diff2' : (elt -> elt) -> elt -> elt * elt

                                                          second order derivative of f : float -> float, return f x and f' x.

                                                          val grad : (arr -> elt) -> arr -> arr

                                                          gradient of f : vector -> scalar.

                                                          val grad' : (arr -> elt) -> arr -> arr * arr

                                                          gradient of f : vector -> scalar, return f x and g x.

                                                          val jacobian : (arr -> arr) -> arr -> arr

                                                          jacobian of f : vector -> vector.

                                                          val jacobian' : (arr -> arr) -> arr -> arr * arr

                                                          jacobian of f : vector -> vector, return f x and j x.

                                                          val jacobianT : (arr -> arr) -> arr -> arr

                                                          transposed jacobian of f : vector -> vector.

                                                          val jacobianT' : (arr -> arr) -> arr -> arr * arr

                                                          transposed jacobian of f : vector -> vector, return f x and j x.

                                                          +Impl (owl-base.Owl_numdiff_generic_sig.Impl)

                                                          Module Owl_numdiff_generic_sig.Impl

                                                          Parameters

                                                          module A : Owl_types.Ndarray_Numdiff with type elt = float

                                                          Signature

                                                          Type definition
                                                          type arr

                                                          General ndarray type

                                                          type elt

                                                          Scalar type

                                                          Basic functions
                                                          val diff : (elt -> elt) -> elt -> elt

                                                          derivative of f : scalar -> scalar.

                                                          val diff' : (elt -> elt) -> elt -> elt * elt

                                                          derivative of f : scalar -> scalar, return both f x and f' x.

                                                          val diff2 : (elt -> elt) -> elt -> elt

                                                          second order derivative of f : float -> float.

                                                          val diff2' : (elt -> elt) -> elt -> elt * elt

                                                          second order derivative of f : float -> float, return f x and f' x.

                                                          val grad : (arr -> elt) -> arr -> arr

                                                          gradient of f : vector -> scalar.

                                                          val grad' : (arr -> elt) -> arr -> arr * arr

                                                          gradient of f : vector -> scalar, return f x and g x.

                                                          val jacobian : (arr -> arr) -> arr -> arr

                                                          jacobian of f : vector -> vector.

                                                          val jacobian' : (arr -> arr) -> arr -> arr * arr

                                                          jacobian of f : vector -> vector, return f x and j x.

                                                          val jacobianT : (arr -> arr) -> arr -> arr

                                                          transposed jacobian of f : vector -> vector.

                                                          val jacobianT' : (arr -> arr) -> arr -> arr * arr

                                                          transposed jacobian of f : vector -> vector, return f x and j x.

                                                          diff --git a/docs/owl-base/Owl_numdiff_generic_sig/index.html b/docs/owl-base/Owl_numdiff_generic_sig/index.html index 45a71d742..d2356a598 100644 --- a/docs/owl-base/Owl_numdiff_generic_sig/index.html +++ b/docs/owl-base/Owl_numdiff_generic_sig/index.html @@ -1,2 +1,2 @@ -Owl_numdiff_generic_sig (owl-base.Owl_numdiff_generic_sig)

                                                          Module Owl_numdiff_generic_sig

                                                          Numdiff: numerical differentiation module

                                                          The functor used to generate Numdiff module of various precisions.

                                                          module type Sig = sig ... end
                                                          module Impl (A : Owl_types.Ndarray_Numdiff with type elt = float) : Sig
                                                          +Owl_numdiff_generic_sig (owl-base.Owl_numdiff_generic_sig)

                                                          Module Owl_numdiff_generic_sig

                                                          Numdiff: numerical differentiation module

                                                          The functor used to generate Numdiff module of various precisions.

                                                          module type Sig = sig ... end
                                                          module Impl (A : Owl_types.Ndarray_Numdiff with type elt = float) : Sig
                                                          diff --git a/docs/owl-base/Owl_numdiff_generic_sig/module-type-Sig/index.html b/docs/owl-base/Owl_numdiff_generic_sig/module-type-Sig/index.html index b1cbcc114..ff31463c3 100644 --- a/docs/owl-base/Owl_numdiff_generic_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_numdiff_generic_sig/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_numdiff_generic_sig.Sig)

                                                          Module type Owl_numdiff_generic_sig.Sig

                                                          Type definition
                                                          type arr

                                                          General ndarray type

                                                          type elt

                                                          Scalar type

                                                          Basic functions
                                                          val diff : (elt -> elt) -> elt -> elt

                                                          derivative of f : scalar -> scalar.

                                                          val diff' : (elt -> elt) -> elt -> elt * elt

                                                          derivative of f : scalar -> scalar, return both f x and f' x.

                                                          val diff2 : (elt -> elt) -> elt -> elt

                                                          second order derivative of f : float -> float.

                                                          val diff2' : (elt -> elt) -> elt -> elt * elt

                                                          second order derivative of f : float -> float, return f x and f' x.

                                                          val grad : (arr -> elt) -> arr -> arr

                                                          gradient of f : vector -> scalar.

                                                          val grad' : (arr -> elt) -> arr -> arr * arr

                                                          gradient of f : vector -> scalar, return f x and g x.

                                                          val jacobian : (arr -> arr) -> arr -> arr

                                                          jacobian of f : vector -> vector.

                                                          val jacobian' : (arr -> arr) -> arr -> arr * arr

                                                          jacobian of f : vector -> vector, return f x and j x.

                                                          val jacobianT : (arr -> arr) -> arr -> arr

                                                          transposed jacobian of f : vector -> vector.

                                                          val jacobianT' : (arr -> arr) -> arr -> arr * arr

                                                          transposed jacobian of f : vector -> vector, return f x and j x.

                                                          +Sig (owl-base.Owl_numdiff_generic_sig.Sig)

                                                          Module type Owl_numdiff_generic_sig.Sig

                                                          Type definition
                                                          type arr

                                                          General ndarray type

                                                          type elt

                                                          Scalar type

                                                          Basic functions
                                                          val diff : (elt -> elt) -> elt -> elt

                                                          derivative of f : scalar -> scalar.

                                                          val diff' : (elt -> elt) -> elt -> elt * elt

                                                          derivative of f : scalar -> scalar, return both f x and f' x.

                                                          val diff2 : (elt -> elt) -> elt -> elt

                                                          second order derivative of f : float -> float.

                                                          val diff2' : (elt -> elt) -> elt -> elt * elt

                                                          second order derivative of f : float -> float, return f x and f' x.

                                                          val grad : (arr -> elt) -> arr -> arr

                                                          gradient of f : vector -> scalar.

                                                          val grad' : (arr -> elt) -> arr -> arr * arr

                                                          gradient of f : vector -> scalar, return f x and g x.

                                                          val jacobian : (arr -> arr) -> arr -> arr

                                                          jacobian of f : vector -> vector.

                                                          val jacobian' : (arr -> arr) -> arr -> arr * arr

                                                          jacobian of f : vector -> vector, return f x and j x.

                                                          val jacobianT : (arr -> arr) -> arr -> arr

                                                          transposed jacobian of f : vector -> vector.

                                                          val jacobianT' : (arr -> arr) -> arr -> arr * arr

                                                          transposed jacobian of f : vector -> vector, return f x and j x.

                                                          diff --git a/docs/owl-base/Owl_operator/Make_Basic/argument-1-M/index.html b/docs/owl-base/Owl_operator/Make_Basic/argument-1-M/index.html index 50db8b95d..e03bb8ada 100644 --- a/docs/owl-base/Owl_operator/Make_Basic/argument-1-M/index.html +++ b/docs/owl-base/Owl_operator/Make_Basic/argument-1-M/index.html @@ -1,2 +1,2 @@ -M (owl-base.Owl_operator.Make_Basic.M)

                                                          Parameter Make_Basic.M

                                                          type ('a, 'b) t
                                                          val add : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val sub : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val mul : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val div : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val add_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val sub_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val mul_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val div_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val scalar_add : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                          val scalar_sub : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                          val scalar_mul : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                          val scalar_div : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                          val equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val not_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val greater : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val less : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val greater_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val less_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          +M (owl-base.Owl_operator.Make_Basic.M)

                                                          Parameter Make_Basic.M

                                                          type ('a, 'b) t
                                                          val add : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val sub : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val mul : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val div : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val add_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val sub_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val mul_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val div_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val scalar_add : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                          val scalar_sub : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                          val scalar_mul : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                          val scalar_div : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                          val equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val not_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val greater : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val less : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val greater_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val less_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                          diff --git a/docs/owl-base/Owl_operator/Make_Basic/index.html b/docs/owl-base/Owl_operator/Make_Basic/index.html index ede60bf78..ad7546af9 100644 --- a/docs/owl-base/Owl_operator/Make_Basic/index.html +++ b/docs/owl-base/Owl_operator/Make_Basic/index.html @@ -1,2 +1,2 @@ -Make_Basic (owl-base.Owl_operator.Make_Basic)

                                                          Module Owl_operator.Make_Basic

                                                          Parameters

                                                          Signature

                                                          val (+) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of add

                                                          val (-) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of sub

                                                          val (*) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of mul

                                                          val (/) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of div

                                                          val (+$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of add_scalar

                                                          val (-$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of sub_scalar

                                                          val (*$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of mul_scalar

                                                          val (/$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of div_scalar

                                                          val ($+) : 'a -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of scalar_add

                                                          val ($-) : 'a -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of scalar_sub

                                                          val ($*) : 'a -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of scalar_mul

                                                          val ($/) : 'a -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of scalar_div

                                                          val (=) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of equal

                                                          val (!=) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of not_equal

                                                          val (<>) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of not_equal

                                                          val (>) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of greater

                                                          val (<) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of less

                                                          val (>=) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of greater_equal

                                                          val (<=) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of less_equal

                                                          +Make_Basic (owl-base.Owl_operator.Make_Basic)

                                                          Module Owl_operator.Make_Basic

                                                          Parameters

                                                          Signature

                                                          val (+) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of add

                                                          val (-) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of sub

                                                          val (*) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of mul

                                                          val (/) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of div

                                                          val (+$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of add_scalar

                                                          val (-$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of sub_scalar

                                                          val (*$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of mul_scalar

                                                          val (/$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of div_scalar

                                                          val ($+) : 'a -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of scalar_add

                                                          val ($-) : 'a -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of scalar_sub

                                                          val ($*) : 'a -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of scalar_mul

                                                          val ($/) : 'a -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of scalar_div

                                                          val (=) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of equal

                                                          val (!=) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of not_equal

                                                          val (<>) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of not_equal

                                                          val (>) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of greater

                                                          val (<) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of less

                                                          val (>=) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of greater_equal

                                                          val (<=) : ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of less_equal

                                                          diff --git a/docs/owl-base/Owl_operator/Make_Extend/argument-1-M/index.html b/docs/owl-base/Owl_operator/Make_Extend/argument-1-M/index.html index 423383c40..2bbea5f27 100644 --- a/docs/owl-base/Owl_operator/Make_Extend/argument-1-M/index.html +++ b/docs/owl-base/Owl_operator/Make_Extend/argument-1-M/index.html @@ -1,2 +1,2 @@ -M (owl-base.Owl_operator.Make_Extend.M)

                                                          Parameter Make_Extend.M

                                                          type ('a, 'b) t
                                                          val equal_scalar : ('a, 'b) t -> 'a -> bool
                                                          val not_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                          val less_scalar : ('a, 'b) t -> 'a -> bool
                                                          val greater_scalar : ('a, 'b) t -> 'a -> bool
                                                          val less_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                          val greater_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                          val elt_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_not_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_less : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_greater : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_less_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_greater_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_not_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_less_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_greater_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_less_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_greater_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val fmod : (float, 'a) t -> (float, 'a) t -> (float, 'a) t
                                                          val fmod_scalar : (float, 'a) t -> float -> (float, 'a) t
                                                          val pow : (float, 'a) t -> (float, 'a) t -> (float, 'a) t
                                                          val scalar_pow : float -> (float, 'a) t -> (float, 'a) t
                                                          val pow_scalar : (float, 'a) t -> float -> (float, 'a) t
                                                          val approx_equal : ?eps:float -> ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val approx_equal_scalar : ?eps:float -> ('a, 'b) t -> 'a -> bool
                                                          val approx_elt_equal : ?eps:float -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val approx_elt_equal_scalar : ?eps:float -> ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                          val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                          val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                          val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                          val concat_vertical : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val concat_horizontal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val get_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t
                                                          val set_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val get_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t
                                                          val set_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          +M (owl-base.Owl_operator.Make_Extend.M)

                                                          Parameter Make_Extend.M

                                                          type ('a, 'b) t
                                                          val equal_scalar : ('a, 'b) t -> 'a -> bool
                                                          val not_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                          val less_scalar : ('a, 'b) t -> 'a -> bool
                                                          val greater_scalar : ('a, 'b) t -> 'a -> bool
                                                          val less_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                          val greater_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                          val elt_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_not_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_less : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_greater : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_less_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_greater_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val elt_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_not_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_less_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_greater_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_less_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val elt_greater_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val fmod : (float, 'a) t -> (float, 'a) t -> (float, 'a) t
                                                          val fmod_scalar : (float, 'a) t -> float -> (float, 'a) t
                                                          val pow : (float, 'a) t -> (float, 'a) t -> (float, 'a) t
                                                          val scalar_pow : float -> (float, 'a) t -> (float, 'a) t
                                                          val pow_scalar : (float, 'a) t -> float -> (float, 'a) t
                                                          val approx_equal : ?eps:float -> ('a, 'b) t -> ('a, 'b) t -> bool
                                                          val approx_equal_scalar : ?eps:float -> ('a, 'b) t -> 'a -> bool
                                                          val approx_elt_equal : ?eps:float -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val approx_elt_equal_scalar : ?eps:float -> ('a, 'b) t -> 'a -> ('a, 'b) t
                                                          val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                          val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                          val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                          val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                          val concat_vertical : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val concat_horizontal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          val get_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t
                                                          val set_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          val get_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t
                                                          val set_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                          diff --git a/docs/owl-base/Owl_operator/Make_Extend/index.html b/docs/owl-base/Owl_operator/Make_Extend/index.html index bafc2d7cb..b4a9126b0 100644 --- a/docs/owl-base/Owl_operator/Make_Extend/index.html +++ b/docs/owl-base/Owl_operator/Make_Extend/index.html @@ -1,2 +1,2 @@ -Make_Extend (owl-base.Owl_operator.Make_Extend)

                                                          Module Owl_operator.Make_Extend

                                                          Parameters

                                                          Signature

                                                          val (=$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of equal_scalar

                                                          val (!=$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of not_equal_scalar

                                                          val (<>$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of not_equal_scalar

                                                          val (<$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of less_scalar

                                                          val (>$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of greater_scalar

                                                          val (<=$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of less_equal_scalar

                                                          val (>=$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of greater_equal_scalar

                                                          val (=.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_equal

                                                          val (!=.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_not_equal

                                                          val (<>.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_not_equal

                                                          val (<.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_less

                                                          val (>.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_greater

                                                          val (<=.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_less_equal

                                                          val (>=.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_greater_equal

                                                          val (=.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_equal_scalar

                                                          val (!=.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_not_equal_scalar

                                                          val (<>.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_not_equal_scalar

                                                          val (<.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_less_scalar

                                                          val (>.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_greater_scalar

                                                          val (<=.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_less_equal_scalar

                                                          val (>=.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_greater_equal_scalar

                                                          val (=~) : ?eps:float -> ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of approx_equal

                                                          val (=~$) : ?eps:float -> ('a, 'b) M.t -> 'a -> bool

                                                          Operator of approx_equal_scalar

                                                          val (=~.) : ?eps:float -> ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of approx_elt_equal

                                                          val (=~.$) : ?eps:float -> ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of approx_elt_equal_scalar

                                                          val (%) : (float, 'a) M.t -> (float, 'a) M.t -> (float, 'a) M.t

                                                          Operator of fmod

                                                          val (%$) : (float, 'a) M.t -> float -> (float, 'a) M.t

                                                          Operator of fmod_scalar

                                                          val (**) : (float, 'a) M.t -> (float, 'a) M.t -> (float, 'a) M.t

                                                          Operator of pow

                                                          val ($**) : float -> (float, 'a) M.t -> (float, 'a) M.t

                                                          Operator of scalar_pow

                                                          val (**$) : (float, 'a) M.t -> float -> (float, 'a) M.t

                                                          Operator of pow_scalar

                                                          val (+=) : ('a, 'b) M.t -> ('a, 'b) M.t -> unit

                                                          Operator of add_

                                                          val (-=) : ('a, 'b) M.t -> ('a, 'b) M.t -> unit

                                                          Operator of sub_

                                                          val (*=) : ('a, 'b) M.t -> ('a, 'b) M.t -> unit

                                                          Operator of mul_

                                                          val (/=) : ('a, 'b) M.t -> ('a, 'b) M.t -> unit

                                                          Operator of div_

                                                          val (+$=) : ('a, 'b) M.t -> 'a -> unit

                                                          Operator of add_scalar_

                                                          val (-$=) : ('a, 'b) M.t -> 'a -> unit

                                                          Operator of sub_scalar_

                                                          val (*$=) : ('a, 'b) M.t -> 'a -> unit

                                                          Operator of mul_scalar_

                                                          val (/$=) : ('a, 'b) M.t -> 'a -> unit

                                                          Operator of div_scalar_

                                                          val (@=) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of concat_vertical

                                                          val (@||) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of concat_horizontal

                                                          val (.!{;..}) : ('a, 'b) M.t -> Owl_types.index array -> ('a, 'b) M.t

                                                          Operator of get_fancy

                                                          val (.!{;..}<-) : ('a, 'b) M.t -> Owl_types.index array -> ('a, 'b) M.t -> unit

                                                          Operator of set_fancy

                                                          val (.${}) : ('a, 'b) M.t -> int list -> ('a, 'b) M.t
                                                          val (.${;..}) : ('a, 'b) M.t -> int list array -> ('a, 'b) M.t

                                                          Operator of get_slice

                                                          val (.${}<-) : ('a, 'b) M.t -> int list -> ('a, 'b) M.t -> unit
                                                          val (.${;..}<-) : ('a, 'b) M.t -> int list array -> ('a, 'b) M.t -> unit

                                                          Operator of set_slice

                                                          +Make_Extend (owl-base.Owl_operator.Make_Extend)

                                                          Module Owl_operator.Make_Extend

                                                          Parameters

                                                          Signature

                                                          val (=$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of equal_scalar

                                                          val (!=$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of not_equal_scalar

                                                          val (<>$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of not_equal_scalar

                                                          val (<$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of less_scalar

                                                          val (>$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of greater_scalar

                                                          val (<=$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of less_equal_scalar

                                                          val (>=$) : ('a, 'b) M.t -> 'a -> bool

                                                          Operator of greater_equal_scalar

                                                          val (=.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_equal

                                                          val (!=.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_not_equal

                                                          val (<>.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_not_equal

                                                          val (<.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_less

                                                          val (>.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_greater

                                                          val (<=.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_less_equal

                                                          val (>=.) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of elt_greater_equal

                                                          val (=.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_equal_scalar

                                                          val (!=.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_not_equal_scalar

                                                          val (<>.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_not_equal_scalar

                                                          val (<.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_less_scalar

                                                          val (>.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_greater_scalar

                                                          val (<=.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_less_equal_scalar

                                                          val (>=.$) : ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of elt_greater_equal_scalar

                                                          val (=~) : ?eps:float -> ('a, 'b) M.t -> ('a, 'b) M.t -> bool

                                                          Operator of approx_equal

                                                          val (=~$) : ?eps:float -> ('a, 'b) M.t -> 'a -> bool

                                                          Operator of approx_equal_scalar

                                                          val (=~.) : ?eps:float -> ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of approx_elt_equal

                                                          val (=~.$) : ?eps:float -> ('a, 'b) M.t -> 'a -> ('a, 'b) M.t

                                                          Operator of approx_elt_equal_scalar

                                                          val (%) : (float, 'a) M.t -> (float, 'a) M.t -> (float, 'a) M.t

                                                          Operator of fmod

                                                          val (%$) : (float, 'a) M.t -> float -> (float, 'a) M.t

                                                          Operator of fmod_scalar

                                                          val (**) : (float, 'a) M.t -> (float, 'a) M.t -> (float, 'a) M.t

                                                          Operator of pow

                                                          val ($**) : float -> (float, 'a) M.t -> (float, 'a) M.t

                                                          Operator of scalar_pow

                                                          val (**$) : (float, 'a) M.t -> float -> (float, 'a) M.t

                                                          Operator of pow_scalar

                                                          val (+=) : ('a, 'b) M.t -> ('a, 'b) M.t -> unit

                                                          Operator of add_

                                                          val (-=) : ('a, 'b) M.t -> ('a, 'b) M.t -> unit

                                                          Operator of sub_

                                                          val (*=) : ('a, 'b) M.t -> ('a, 'b) M.t -> unit

                                                          Operator of mul_

                                                          val (/=) : ('a, 'b) M.t -> ('a, 'b) M.t -> unit

                                                          Operator of div_

                                                          val (+$=) : ('a, 'b) M.t -> 'a -> unit

                                                          Operator of add_scalar_

                                                          val (-$=) : ('a, 'b) M.t -> 'a -> unit

                                                          Operator of sub_scalar_

                                                          val (*$=) : ('a, 'b) M.t -> 'a -> unit

                                                          Operator of mul_scalar_

                                                          val (/$=) : ('a, 'b) M.t -> 'a -> unit

                                                          Operator of div_scalar_

                                                          val (@=) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of concat_vertical

                                                          val (@||) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of concat_horizontal

                                                          val (.!{;..}) : ('a, 'b) M.t -> Owl_types.index array -> ('a, 'b) M.t

                                                          Operator of get_fancy

                                                          val (.!{;..}<-) : ('a, 'b) M.t -> Owl_types.index array -> ('a, 'b) M.t -> unit

                                                          Operator of set_fancy

                                                          val (.${}) : ('a, 'b) M.t -> int list -> ('a, 'b) M.t
                                                          val (.${;..}) : ('a, 'b) M.t -> int list array -> ('a, 'b) M.t

                                                          Operator of get_slice

                                                          val (.${}<-) : ('a, 'b) M.t -> int list -> ('a, 'b) M.t -> unit
                                                          val (.${;..}<-) : ('a, 'b) M.t -> int list array -> ('a, 'b) M.t -> unit

                                                          Operator of set_slice

                                                          diff --git a/docs/owl-base/Owl_operator/Make_Linalg/argument-1-M/index.html b/docs/owl-base/Owl_operator/Make_Linalg/argument-1-M/index.html index 20bd9bd3c..36336f1f7 100644 --- a/docs/owl-base/Owl_operator/Make_Linalg/argument-1-M/index.html +++ b/docs/owl-base/Owl_operator/Make_Linalg/argument-1-M/index.html @@ -1,5 +1,5 @@ -M (owl-base.Owl_operator.Make_Linalg.M)

                                                          Parameter Make_Linalg.M

                                                          type ('a, 'b) t
                                                          val mpow : ('a, 'b) t -> float -> ('a, 'b) t
                                                          val linsolve : +M (owl-base.Owl_operator.Make_Linalg.M)

                                                          Parameter Make_Linalg.M

                                                          type ('a, 'b) t
                                                          val mpow : ('a, 'b) t -> float -> ('a, 'b) t
                                                          val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> ('a, 'b) t -> diff --git a/docs/owl-base/Owl_operator/Make_Linalg/index.html b/docs/owl-base/Owl_operator/Make_Linalg/index.html index 7dc31af1f..b1bcec628 100644 --- a/docs/owl-base/Owl_operator/Make_Linalg/index.html +++ b/docs/owl-base/Owl_operator/Make_Linalg/index.html @@ -1,2 +1,2 @@ -Make_Linalg (owl-base.Owl_operator.Make_Linalg)

                                                          Module Owl_operator.Make_Linalg

                                                          Parameters

                                                          Signature

                                                          val (**@) : ('a, 'b) M.t -> float -> ('a, 'b) M.t

                                                          Operator of mpow, i.e. matrix power.

                                                          val (/@) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of linsolve a b, i.e. for solving a linear system a * x = b.

                                                          +Make_Linalg (owl-base.Owl_operator.Make_Linalg)

                                                          Module Owl_operator.Make_Linalg

                                                          Parameters

                                                          Signature

                                                          val (**@) : ('a, 'b) M.t -> float -> ('a, 'b) M.t

                                                          Operator of mpow, i.e. matrix power.

                                                          val (/@) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of linsolve a b, i.e. for solving a linear system a * x = b.

                                                          diff --git a/docs/owl-base/Owl_operator/Make_Matrix/argument-1-M/index.html b/docs/owl-base/Owl_operator/Make_Matrix/argument-1-M/index.html index 71e219b08..ad7f31f11 100644 --- a/docs/owl-base/Owl_operator/Make_Matrix/argument-1-M/index.html +++ b/docs/owl-base/Owl_operator/Make_Matrix/argument-1-M/index.html @@ -1,2 +1,2 @@ -M (owl-base.Owl_operator.Make_Matrix.M)

                                                          Parameter Make_Matrix.M

                                                          type ('a, 'b) t
                                                          val get : ('a, 'b) t -> int -> int -> 'a
                                                          val set : ('a, 'b) t -> int -> int -> 'a -> unit
                                                          val dot : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          +M (owl-base.Owl_operator.Make_Matrix.M)

                                                          Parameter Make_Matrix.M

                                                          type ('a, 'b) t
                                                          val get : ('a, 'b) t -> int -> int -> 'a
                                                          val set : ('a, 'b) t -> int -> int -> 'a -> unit
                                                          val dot : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                          diff --git a/docs/owl-base/Owl_operator/Make_Matrix/index.html b/docs/owl-base/Owl_operator/Make_Matrix/index.html index dc2ebc70d..6275f5ff2 100644 --- a/docs/owl-base/Owl_operator/Make_Matrix/index.html +++ b/docs/owl-base/Owl_operator/Make_Matrix/index.html @@ -1,2 +1,2 @@ -Make_Matrix (owl-base.Owl_operator.Make_Matrix)

                                                          Module Owl_operator.Make_Matrix

                                                          Parameters

                                                          Signature

                                                          val (*@) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of dot a b, i.e. matrix multiplication a * b.

                                                          val (.%{}) : ('a, 'b) M.t -> (int * int) -> 'a
                                                          val (.%{;..}) : ('a, 'b) M.t -> int array -> 'a

                                                          Operator of get

                                                          val (.%{}<-) : ('a, 'b) M.t -> (int * int) -> 'a -> unit
                                                          val (.%{;..}<-) : ('a, 'b) M.t -> int array -> 'a -> unit

                                                          Operator of set

                                                          +Make_Matrix (owl-base.Owl_operator.Make_Matrix)

                                                          Module Owl_operator.Make_Matrix

                                                          Parameters

                                                          Signature

                                                          val (*@) : ('a, 'b) M.t -> ('a, 'b) M.t -> ('a, 'b) M.t

                                                          Operator of dot a b, i.e. matrix multiplication a * b.

                                                          val (.%{}) : ('a, 'b) M.t -> (int * int) -> 'a
                                                          val (.%{;..}) : ('a, 'b) M.t -> int array -> 'a

                                                          Operator of get

                                                          val (.%{}<-) : ('a, 'b) M.t -> (int * int) -> 'a -> unit
                                                          val (.%{;..}<-) : ('a, 'b) M.t -> int array -> 'a -> unit

                                                          Operator of set

                                                          diff --git a/docs/owl-base/Owl_operator/Make_Ndarray/argument-1-M/index.html b/docs/owl-base/Owl_operator/Make_Ndarray/argument-1-M/index.html index e7954c96a..8e198b511 100644 --- a/docs/owl-base/Owl_operator/Make_Ndarray/argument-1-M/index.html +++ b/docs/owl-base/Owl_operator/Make_Ndarray/argument-1-M/index.html @@ -1,2 +1,2 @@ -M (owl-base.Owl_operator.Make_Ndarray.M)

                                                          Parameter Make_Ndarray.M

                                                          type ('a, 'b) t
                                                          val get : ('a, 'b) t -> int array -> 'a
                                                          val set : ('a, 'b) t -> int array -> 'a -> unit
                                                          +M (owl-base.Owl_operator.Make_Ndarray.M)

                                                          Parameter Make_Ndarray.M

                                                          type ('a, 'b) t
                                                          val get : ('a, 'b) t -> int array -> 'a
                                                          val set : ('a, 'b) t -> int array -> 'a -> unit
                                                          diff --git a/docs/owl-base/Owl_operator/Make_Ndarray/index.html b/docs/owl-base/Owl_operator/Make_Ndarray/index.html index 168f3df1e..37c39d0f9 100644 --- a/docs/owl-base/Owl_operator/Make_Ndarray/index.html +++ b/docs/owl-base/Owl_operator/Make_Ndarray/index.html @@ -1,2 +1,2 @@ -Make_Ndarray (owl-base.Owl_operator.Make_Ndarray)

                                                          Module Owl_operator.Make_Ndarray

                                                          Parameters

                                                          Signature

                                                          val (.%{}) : ('a, 'b) M.t -> int -> 'a
                                                          val (.%{;..}) : ('a, 'b) M.t -> int array -> 'a

                                                          Operator of get

                                                          val (.%{}<-) : ('a, 'b) M.t -> int -> 'a -> unit
                                                          val (.%{;..}<-) : ('a, 'b) M.t -> int array -> 'a -> unit

                                                          Operator of set

                                                          +Make_Ndarray (owl-base.Owl_operator.Make_Ndarray)

                                                          Module Owl_operator.Make_Ndarray

                                                          Parameters

                                                          Signature

                                                          val (.%{}) : ('a, 'b) M.t -> int -> 'a
                                                          val (.%{;..}) : ('a, 'b) M.t -> int array -> 'a

                                                          Operator of get

                                                          val (.%{}<-) : ('a, 'b) M.t -> int -> 'a -> unit
                                                          val (.%{;..}<-) : ('a, 'b) M.t -> int array -> 'a -> unit

                                                          Operator of set

                                                          diff --git a/docs/owl-base/Owl_operator/index.html b/docs/owl-base/Owl_operator/index.html index 7eb5fe13d..8f5c4cfce 100644 --- a/docs/owl-base/Owl_operator/index.html +++ b/docs/owl-base/Owl_operator/index.html @@ -1,2 +1,2 @@ -Owl_operator (owl-base.Owl_operator)

                                                          Module Owl_operator

                                                          Basic operators
                                                          module Make_Basic (M : Owl_types_operator.BasicSig) : sig ... end
                                                          Extended operators
                                                          module Make_Extend (M : Owl_types_operator.ExtendSig) : sig ... end
                                                          Matrix-specific operators
                                                          module Make_Matrix (M : Owl_types_operator.MatrixSig) : sig ... end
                                                          Ndarray-specific operators
                                                          Linalg-specific operators
                                                          module Make_Linalg (M : Owl_types_operator.LinalgSig) : sig ... end
                                                          +Owl_operator (owl-base.Owl_operator)

                                                          Module Owl_operator

                                                          Basic operators
                                                          module Make_Basic (M : Owl_types_operator.BasicSig) : sig ... end
                                                          Extended operators
                                                          module Make_Extend (M : Owl_types_operator.ExtendSig) : sig ... end
                                                          Matrix-specific operators
                                                          module Make_Matrix (M : Owl_types_operator.MatrixSig) : sig ... end
                                                          Ndarray-specific operators
                                                          Linalg-specific operators
                                                          module Make_Linalg (M : Owl_types_operator.LinalgSig) : sig ... end
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/Batch/index.html b/docs/owl-base/Owl_optimise_generic/Make/Batch/index.html index 75344e68a..35ecaae6f 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Batch/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl-base.Owl_optimise_generic.Make.Batch)

                                                          Module Make.Batch

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val batches : typ -> Algodiff.t -> int
                                                          val to_string : typ -> string
                                                          +Batch (owl-base.Owl_optimise_generic.Make.Batch)

                                                          Module Make.Batch

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val batches : typ -> Algodiff.t -> int
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/Checkpoint/index.html b/docs/owl-base/Owl_optimise_generic/Make/Checkpoint/index.html index c50944967..551d25525 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Checkpoint/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl-base.Owl_optimise_generic.Make.Checkpoint)

                                                          Module Make.Checkpoint

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }
                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None
                                                          val init_state : int -> float -> state
                                                          val default_checkpoint_fun : (string -> 'a) -> 'b
                                                          val print_state_info : state -> unit
                                                          val print_summary : state -> unit
                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit
                                                          val to_string : typ -> string
                                                          +Checkpoint (owl-base.Owl_optimise_generic.Make.Checkpoint)

                                                          Module Make.Checkpoint

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }
                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None
                                                          val init_state : int -> float -> state
                                                          val default_checkpoint_fun : (string -> 'a) -> 'b
                                                          val print_state_info : state -> unit
                                                          val print_summary : state -> unit
                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/Clipping/index.html b/docs/owl-base/Owl_optimise_generic/Make/Clipping/index.html index 94ae5f9b6..cc34d77df 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Clipping/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl-base.Owl_optimise_generic.Make.Clipping)

                                                          Module Make.Clipping

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          +Clipping (owl-base.Owl_optimise_generic.Make.Clipping)

                                                          Module Make.Clipping

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/Gradient/index.html b/docs/owl-base/Owl_optimise_generic/Make/Gradient/index.html index 5a368bcc7..f9eae906f 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Gradient/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_optimise_generic.Make.Gradient)

                                                          Module Make.Gradient

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton
                                                          val run : +Gradient (owl-base.Owl_optimise_generic.Make.Gradient)

                                                          Module Make.Gradient

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton
                                                          val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl-base/Owl_optimise_generic/Make/Learning_Rate/index.html b/docs/owl-base/Owl_optimise_generic/Make/Learning_Rate/index.html index d4ce9c7be..3b679437e 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Learning_Rate/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl-base.Owl_optimise_generic.Make.Learning_Rate)

                                                          Module Make.Learning_Rate

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                          val default : typ -> typ
                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                          val to_string : typ -> string
                                                          +Learning_Rate (owl-base.Owl_optimise_generic.Make.Learning_Rate)

                                                          Module Make.Learning_Rate

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                          val default : typ -> typ
                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/Loss/index.html b/docs/owl-base/Owl_optimise_generic/Make/Loss/index.html index 8018633cd..bfc6da1e8 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Loss/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl-base.Owl_optimise_generic.Make.Loss)

                                                          Module Make.Loss

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          +Loss (owl-base.Owl_optimise_generic.Make.Loss)

                                                          Module Make.Loss

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/Momentum/index.html b/docs/owl-base/Owl_optimise_generic/Make/Momentum/index.html index 241b3563a..0dad72a72 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Momentum/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl-base.Owl_optimise_generic.Make.Momentum)

                                                          Module Make.Momentum

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          +Momentum (owl-base.Owl_optimise_generic.Make.Momentum)

                                                          Module Make.Momentum

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/Params/index.html b/docs/owl-base/Owl_optimise_generic/Make/Params/index.html index 9d5c34762..5d7575261 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Params/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_optimise_generic.Make.Params)

                                                          Module Make.Params

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }
                                                          val default : unit -> typ
                                                          val config : +Params (owl-base.Owl_optimise_generic.Make.Params)

                                                          Module Make.Params

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }
                                                          val default : unit -> typ
                                                          val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl-base/Owl_optimise_generic/Make/Regularisation/index.html b/docs/owl-base/Owl_optimise_generic/Make/Regularisation/index.html index 9a95e1259..8ae962042 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Regularisation/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl-base.Owl_optimise_generic.Make.Regularisation)

                                                          Module Make.Regularisation

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          +Regularisation (owl-base.Owl_optimise_generic.Make.Regularisation)

                                                          Module Make.Regularisation

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None
                                                          val run : typ -> Algodiff.t -> Algodiff.t
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/Stopping/index.html b/docs/owl-base/Owl_optimise_generic/Make/Stopping/index.html index 82126789f..4b9ab4f6a 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Stopping/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl-base.Owl_optimise_generic.Make.Stopping)

                                                          Module Make.Stopping

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None
                                                          val run : typ -> float -> bool
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          +Stopping (owl-base.Owl_optimise_generic.Make.Stopping)

                                                          Module Make.Stopping

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None
                                                          val run : typ -> float -> bool
                                                          val default : typ -> typ
                                                          val to_string : typ -> string
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/Utils/index.html b/docs/owl-base/Owl_optimise_generic/Make/Utils/index.html index 48d183ea3..1528a6320 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/Utils/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_optimise_generic.Make.Utils)

                                                          Module Make.Utils

                                                          val sample_num : Algodiff.t -> int
                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val get_chunk : +Utils (owl-base.Owl_optimise_generic.Make.Utils)

                                                          Module Make.Utils

                                                          val sample_num : Algodiff.t -> int
                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                          val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Linalg/index.html index 74d82d72b..d8008c737 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_optimise_generic.Make.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_optimise_generic.Make.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Mat/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Mat/index.html index 3b1599bbb..345b3430c 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_optimise_generic.Make.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_optimise_generic.Make.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Scalar/index.html index 7968fb883..5510cd8aa 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_optimise_generic.Make.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_optimise_generic.Make.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/index.html index d69c733a8..b7d2f9b42 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_optimise_generic.Make.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_optimise_generic.Make.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Arr/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Arr/index.html index 4f1a55560..0db9148c4 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_optimise_generic.Make.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          +Arr (owl-base.Owl_optimise_generic.Make.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/index.html index 3203dd8ab..868d10c62 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_optimise_generic.Make.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          +Builder (owl-base.Owl_optimise_generic.Make.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Aiso/index.html index 6c01ede73..cf8e20704 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          +Aiso (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Piso/index.html index 9d1223021..e656efe8d 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          +Piso (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Siao/index.html index 3b8ee8dd9..56016f6ce 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          +Siao (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Sipo/index.html index e9bfe107a..887843772 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sipo (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Siso/index.html index 485c11c83..03a393e74 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          +Siso (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Sito/index.html index d5c1b10e2..05f989fd7 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sito (owl-base.Owl_optimise_generic.Make.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Linalg/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Linalg/index.html index 6c6f80b41..c3073b645 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_optimise_generic.Make.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_optimise_generic.Make.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Mat/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Mat/index.html index 6e054fa9f..40fa21a45 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_optimise_generic.Make.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          +Mat (owl-base.Owl_optimise_generic.Make.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Maths/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Maths/index.html index 758fffb18..b2125a124 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_optimise_generic.Make.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          +Maths (owl-base.Owl_optimise_generic.Make.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/NN/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/NN/index.html index 52c2e6009..88deebfb3 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/NN/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_optimise_generic.Make.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : +NN (owl-base.Owl_optimise_generic.Make.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/index.html b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/index.html index 3bec29571..cf40a37d9 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/argument-1-Algodiff/index.html @@ -1,5 +1,5 @@ -Algodiff (owl-base.Owl_optimise_generic.Make.Algodiff)

                                                          Parameter Make.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig +Algodiff (owl-base.Owl_optimise_generic.Make.Algodiff)

                                                          Parameter Make.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl-base/Owl_optimise_generic/Make/index.html b/docs/owl-base/Owl_optimise_generic/Make/index.html index ee6c262d4..2fd64c451 100644 --- a/docs/owl-base/Owl_optimise_generic/Make/index.html +++ b/docs/owl-base/Owl_optimise_generic/Make/index.html @@ -1,5 +1,5 @@ -Make (owl-base.Owl_optimise_generic.Make)

                                                          Module Owl_optimise_generic.Make

                                                          Parameters

                                                          Signature

                                                          module Algodiff = Algodiff
                                                          module Utils : sig ... end
                                                          module Learning_Rate : sig ... end
                                                          module Batch : sig ... end
                                                          module Loss : sig ... end
                                                          module Gradient : sig ... end
                                                          module Momentum : sig ... end
                                                          module Regularisation : sig ... end
                                                          module Clipping : sig ... end
                                                          module Stopping : sig ... end
                                                          module Checkpoint : sig ... end
                                                          module Params : sig ... end
                                                          val minimise_weight : +Make (owl-base.Owl_optimise_generic.Make)

                                                          Module Owl_optimise_generic.Make

                                                          Parameters

                                                          Signature

                                                          module Algodiff = Algodiff
                                                          module Utils : sig ... end
                                                          module Learning_Rate : sig ... end
                                                          module Batch : sig ... end
                                                          module Loss : sig ... end
                                                          module Gradient : sig ... end
                                                          module Momentum : sig ... end
                                                          module Regularisation : sig ... end
                                                          module Clipping : sig ... end
                                                          module Stopping : sig ... end
                                                          module Checkpoint : sig ... end
                                                          module Params : sig ... end
                                                          val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl-base/Owl_optimise_generic/index.html b/docs/owl-base/Owl_optimise_generic/index.html index 80ea38e0c..dc8b5382c 100644 --- a/docs/owl-base/Owl_optimise_generic/index.html +++ b/docs/owl-base/Owl_optimise_generic/index.html @@ -1,2 +1,2 @@ -Owl_optimise_generic (owl-base.Owl_optimise_generic)

                                                          Module Owl_optimise_generic

                                                          Optimisation engine

                                                          This module provides fundamental supports for Owl's regression and neural network module. The module supports both single and double precision float numbers.

                                                          module Make (Algodiff : Owl_algodiff_generic_sig.Sig) : sig ... end
                                                          +Owl_optimise_generic (owl-base.Owl_optimise_generic)

                                                          Module Owl_optimise_generic

                                                          Optimisation engine

                                                          This module provides fundamental supports for Owl's regression and neural network module. The module supports both single and double precision float numbers.

                                                          module Make (Algodiff : Owl_algodiff_generic_sig.Sig) : sig ... end
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/index.html b/docs/owl-base/Owl_optimise_generic_sig/index.html index 1a3adb4b8..5f981934e 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/index.html @@ -1,2 +1,2 @@ -Owl_optimise_generic_sig (owl-base.Owl_optimise_generic_sig)

                                                          Module Owl_optimise_generic_sig

                                                          module type Sig = sig ... end
                                                          +Owl_optimise_generic_sig (owl-base.Owl_optimise_generic_sig)

                                                          Module Owl_optimise_generic_sig

                                                          module type Sig = sig ... end
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Linalg/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Linalg/index.html index 49d850e4b..935a077aa 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Linalg/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Mat/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Mat/index.html index eec9e656d..609f81c31 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Mat/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Scalar/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Scalar/index.html index 98343b786..e85cabb14 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Scalar/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/index.html index 03b50d798..c42caa289 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.A)

                                                          Module Algodiff.A

                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Arr/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Arr/index.html index 421262879..7e1b2db75 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Arr/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          +Arr (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Arr)

                                                          Module Algodiff.Arr

                                                          val empty : int array -> t
                                                          val zeros : int array -> t
                                                          val ones : int array -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                          val shape : t -> int array
                                                          val numel : t -> int
                                                          val reset : t -> unit
                                                          val reshape : t -> int array -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/index.html index c68bd7f70..3da622f0a 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          +Builder (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder)

                                                          Module Algodiff.Builder

                                                          Ops Builder
                                                          module type Siso = sig ... end
                                                          val build_siso : (module Siso) -> t -> t

                                                          build single input single output operations

                                                          module type Sipo = sig ... end
                                                          val build_sipo : (module Sipo) -> t -> t * t

                                                          build single input pair outputs operations

                                                          module type Sito = sig ... end
                                                          val build_sito : (module Sito) -> t -> t * t * t

                                                          build single input triple outputs operations

                                                          module type Siao = sig ... end
                                                          val build_siao : (module Siao) -> t -> t array

                                                          build single input array output operations

                                                          module type Piso = sig ... end
                                                          val build_piso : (module Piso) -> t -> t -> t

                                                          build pair inputs single output operations

                                                          module type Aiso = sig ... end
                                                          val build_aiso : (module Aiso) -> t array -> t

                                                          build array input single output operations

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Aiso/index.html index 3d12a84b1..4183a11a0 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          +Aiso (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Aiso)

                                                          Module type Builder.Aiso

                                                          val label : string
                                                          val ff : t array -> t
                                                          val df : int list -> t -> t array -> t array -> t
                                                          val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Piso/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Piso/index.html index 2edb5625f..05e959db8 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          +Piso (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Piso)

                                                          Module type Builder.Piso

                                                          val label : string
                                                          val ff_aa : A.elt -> A.elt -> t
                                                          val ff_ab : A.elt -> A.arr -> t
                                                          val ff_ba : A.arr -> A.elt -> t
                                                          val ff_bb : A.arr -> A.arr -> t
                                                          val df_da : t -> t -> t -> t -> t
                                                          val df_db : t -> t -> t -> t -> t
                                                          val df_dab : t -> t -> t -> t -> t -> t
                                                          val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                          val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                          val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Siao/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Siao/index.html index 7426985b7..07adbbb6c 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          +Siao (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Siao)

                                                          Module type Builder.Siao

                                                          val label : string
                                                          val ff_f : A.elt -> t array
                                                          val ff_arr : A.arr -> t array
                                                          val df : t array -> t -> t -> t array
                                                          val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Sipo/index.html index 4777330b2..70a89b817 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sipo (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Sipo)

                                                          Module type Builder.Sipo

                                                          val label : string
                                                          val ff_f : A.elt -> t * t
                                                          val ff_arr : A.arr -> t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Siso/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Siso/index.html index b179de9b8..5a104f29b 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          +Siso (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Siso)

                                                          Module type Builder.Siso

                                                          val label : string
                                                          val ff_f : A.elt -> t
                                                          val ff_arr : A.arr -> t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> t Stdlib.ref -> t
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Sito/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Sito/index.html index 671557025..1bdbb51c2 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : +Sito (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Builder.Sito)

                                                          Module type Builder.Sito

                                                          val label : string
                                                          val ff_f : A.elt -> t * t * t
                                                          val ff_arr : A.arr -> t * t * t
                                                          val df : t -> t -> t -> t
                                                          val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Linalg/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Linalg/index.html index 863a05b5a..3258a7431 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Linalg)

                                                          Module Algodiff.Linalg

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val logdet : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val chol : ?upper:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val qr : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lq : t -> t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val svd : ?thin:bool -> t -> t * t * t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sylvester : t -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val lyapunov : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Mat/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Mat/index.html index 713b1e92d..1b9cc3165 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          +Mat (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Mat)

                                                          Module Algodiff.Mat

                                                          val empty : int -> int -> t
                                                          val zeros : int -> int -> t
                                                          val eye : int -> t
                                                          val ones : int -> int -> t
                                                          val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                          val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                          val shape : t -> int * int
                                                          val numel : t -> int
                                                          val row_num : t -> int
                                                          val col_num : t -> int
                                                          val reset : t -> unit
                                                          val reshape : int -> int -> t -> t
                                                          val get : t -> int -> int -> t
                                                          val set : t -> int -> int -> t -> t
                                                          val row : t -> int -> t
                                                          val mean : t -> t
                                                          val add : t -> t -> t
                                                          val sub : t -> t -> t
                                                          val mul : t -> t -> t
                                                          val div : t -> t -> t
                                                          val dot : t -> t -> t
                                                          val map_by_row : (t -> t) -> t -> t
                                                          val of_arrays : A.elt array array -> t
                                                          val init_2d : int -> int -> (int -> int -> t) -> t
                                                          val print : t -> unit
                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Maths/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Maths/index.html index df31a219c..39cb5eb2b 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Maths/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          +Maths (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.Maths)

                                                          Module Algodiff.Maths

                                                          val (+) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (-) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (/) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (*@) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val (**) : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val add : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sub : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mul : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val div : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val kron : t -> t -> t

                                                          Refer to :doc:`owl_dense_matrix_generic`

                                                          val dot : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val pow : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val min2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val max2 : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cross_entropy : t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val inv : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val neg : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val abs : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val signum : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val floor : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val ceil : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val round : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqr : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sqrt : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log2 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log10 : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val exp : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val cosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asin : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acos : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atan : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val asinh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val acosh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val atanh : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sum_reduce : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val mean : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val transpose : ?axis:int array -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val swap : int -> int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l1norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val l2norm_sqr' : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val sigmoid : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val relu : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dawsn : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softplus : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softsign : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val softmax : ?axis:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val reshape : t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val flatten : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_item : t -> int -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_row : t -> int -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concat : axis:int -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val split : axis:int -> int array -> t -> t array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val of_arrays : t array array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val to_arrays : t -> t array array

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val concatenate : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val stack : axis:int -> t array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_slice : int list list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_slice : int list list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val get_fancy : Owl_types.index list -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val set_fancy : Owl_types.index list -> t -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diag : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val diagm : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val trace : t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val triu : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val tril : ?k:int -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/NN/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/NN/index.html index 80f4169e4..9d299cd76 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/NN/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : +NN (owl-base.Owl_optimise_generic_sig.Sig.Algodiff.NN)

                                                          Module Algodiff.NN

                                                          val dropout : ?rate:float -> t -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                          Refer to :doc:`owl_dense_ndarray_generic`

                                                          val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/index.html index 529951856..b74f6483b 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Algodiff/index.html @@ -1,5 +1,5 @@ -Algodiff (owl-base.Owl_optimise_generic_sig.Sig.Algodiff)

                                                          Module Sig.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig +Algodiff (owl-base.Owl_optimise_generic_sig.Sig.Algodiff)

                                                          Module Sig.Algodiff

                                                          include Owl_algodiff_core_sig.Sig
                                                          Type definition
                                                          include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                          type t =
                                                          1. | F of A.elt
                                                          2. | Arr of A.arr
                                                          3. | DF of t * t * int
                                                          4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                          and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                          and register = t list -> t list
                                                          and label = string * t list
                                                          and op = adjoint * register * label
                                                          Core functions
                                                          val tag : unit -> int

                                                          TODO

                                                          val primal : t -> t

                                                          TODO

                                                          val primal' : t -> t

                                                          TODO

                                                          val zero : t -> t

                                                          TODO

                                                          val reset_zero : t -> t

                                                          TODO

                                                          val tangent : t -> t

                                                          TODO

                                                          val adjref : t -> t Stdlib.ref

                                                          TODO

                                                          val adjval : t -> t

                                                          TODO

                                                          val shape : t -> int array

                                                          TODO

                                                          val is_float : t -> bool

                                                          TODO

                                                          val is_arr : t -> bool

                                                          TODO

                                                          val row_num : t -> int

                                                          number of rows

                                                          val col_num : t -> int

                                                          number of columns

                                                          val numel : t -> int

                                                          number of elements

                                                          val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val clip_by_l2norm : A.elt -> t -> t

                                                          other functions, without tracking gradient

                                                          val copy_primal' : t -> t

                                                          TODO

                                                          val tile : t -> int array -> t

                                                          TODO

                                                          val repeat : t -> int array -> t

                                                          TODO

                                                          val pack_elt : A.elt -> t

                                                          convert from elt type to t type.

                                                          val unpack_elt : t -> A.elt

                                                          convert from t type to elt type.

                                                          val pack_flt : float -> t

                                                          convert from float type to t type.

                                                          val _f : float -> t

                                                          A shortcut function for F A.(float_to_elt x).

                                                          val unpack_flt : t -> float

                                                          convert from t type to float type.

                                                          val pack_arr : A.arr -> t

                                                          convert from arr type to t type.

                                                          val unpack_arr : t -> A.arr

                                                          convert from t type to arr type.

                                                          val deep_info : t -> string

                                                          TODO

                                                          val type_info : t -> string

                                                          TODO

                                                          val error_binop : string -> t -> t -> 'a

                                                          TODO

                                                          val error_uniop : string -> t -> 'a

                                                          TODO

                                                          val make_forward : t -> t -> int -> t

                                                          TODO

                                                          val make_reverse : t -> int -> t

                                                          TODO

                                                          val reverse_prop : t -> t -> unit

                                                          TODO

                                                          val diff : (t -> t) -> t -> t

                                                          diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                          Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                          val diff' : (t -> t) -> t -> t * t

                                                          similar to diff, but return (f x, diff f x).

                                                          val grad : (t -> t) -> t -> t

                                                          gradient of f : (vector -> scalar) at x, reverse ad.

                                                          val grad' : (t -> t) -> t -> t * t

                                                          similar to grad, but return (f x, grad f x).

                                                          val jacobian : (t -> t) -> t -> t

                                                          jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                          val jacobian' : (t -> t) -> t -> t * t

                                                          similar to jacobian, but return (f x, jacobian f x)

                                                          val jacobianv : (t -> t) -> t -> t -> t

                                                          jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                          val jacobianv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianv', but return (f x, jacobianv f x v)

                                                          val jacobianTv : (t -> t) -> t -> t -> t

                                                          transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                          val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                          similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                          val hessian : (t -> t) -> t -> t

                                                          hessian of f : (scalar -> scalar) at x.

                                                          val hessian' : (t -> t) -> t -> t * t

                                                          simiarl to hessian, but return (f x, hessian f x)

                                                          val hessianv : (t -> t) -> t -> t -> t

                                                          hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                          val hessianv' : (t -> t) -> t -> t -> t * t

                                                          similar to hessianv, but return (f x, hessianv f x v).

                                                          val laplacian : (t -> t) -> t -> t

                                                          laplacian of f : (scalar -> scalar) at x.

                                                          val laplacian' : (t -> t) -> t -> t * t

                                                          similar to laplacian, but return (f x, laplacian f x).

                                                          val gradhessian : (t -> t) -> t -> t * t

                                                          return (grad f x, hessian f x), f : (scalar -> scalar)

                                                          val gradhessian' : (t -> t) -> t -> t * t * t

                                                          return (f x, grad f x, hessian f x)

                                                          val gradhessianv : (t -> t) -> t -> t -> t * t

                                                          return (grad f x v, hessian f x v)

                                                          val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                          return (f x, grad f x v, hessian f x v)

                                                          include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Batch/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Batch/index.html index 66d0485d3..93b96bc19 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Batch/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl-base.Owl_optimise_generic_sig.Sig.Batch)

                                                          Module Sig.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Batch (owl-base.Owl_optimise_generic_sig.Sig.Batch)

                                                          Module Sig.Batch

                                                          Batch module

                                                          type typ =
                                                          1. | Full
                                                          2. | Mini of int
                                                          3. | Sample of int
                                                          4. | Stochastic

                                                          Types of batches.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val batches : typ -> Algodiff.t -> int

                                                          Return the total number of batches given a batch typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Checkpoint/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Checkpoint/index.html index e853e0aaa..ad66d93d4 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Checkpoint/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl-base.Owl_optimise_generic_sig.Sig.Checkpoint)

                                                          Module Sig.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Checkpoint (owl-base.Owl_optimise_generic_sig.Sig.Checkpoint)

                                                          Module Sig.Checkpoint

                                                          Checkpoint module

                                                          type state = {
                                                          1. mutable current_batch : int;
                                                          2. mutable batches_per_epoch : int;
                                                          3. mutable epochs : float;
                                                          4. mutable batches : int;
                                                          5. mutable loss : Algodiff.t array;
                                                          6. mutable start_at : float;
                                                          7. mutable stop : bool;
                                                          8. mutable gs : Algodiff.t array array;
                                                          9. mutable ps : Algodiff.t array array;
                                                          10. mutable us : Algodiff.t array array;
                                                          11. mutable ch : Algodiff.t array array array;
                                                          }

                                                          Type definition of checkpoint

                                                          type typ =
                                                          1. | Batch of int
                                                          2. | Epoch of float
                                                          3. | Custom of state -> unit
                                                          4. | None

                                                          Batch type.

                                                          val init_state : int -> float -> state

                                                          init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                          val default_checkpoint_fun : (string -> 'a) -> 'a

                                                          This function is used for saving intermediate files during optimisation.

                                                          val print_state_info : state -> unit

                                                          Print out the detail information of current state.

                                                          val print_summary : state -> unit

                                                          Print out the summary of current state.

                                                          val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Clipping/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Clipping/index.html index cc52cf5e3..ab5b47aa0 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Clipping/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl-base.Owl_optimise_generic_sig.Sig.Clipping)

                                                          Module Sig.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Clipping (owl-base.Owl_optimise_generic_sig.Sig.Clipping)

                                                          Module Sig.Clipping

                                                          Clipping module

                                                          type typ =
                                                          1. | L2norm of float
                                                          2. | Value of float * float
                                                          3. | None

                                                          Types of clipping functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Gradient/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Gradient/index.html index 9fcd583e3..60c204123 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Gradient/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl-base.Owl_optimise_generic_sig.Sig.Gradient)

                                                          Module Sig.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : +Gradient (owl-base.Owl_optimise_generic_sig.Sig.Gradient)

                                                          Module Sig.Gradient

                                                          Gradient module

                                                          type typ =
                                                          1. | GD
                                                          2. | CG
                                                          3. | CD
                                                          4. | NonlinearCG
                                                          5. | DaiYuanCG
                                                          6. | NewtonCG
                                                          7. | Newton

                                                          Types of gradient function.

                                                          val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Learning_Rate/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Learning_Rate/index.html index c81581803..c7255d749 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Learning_Rate/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl-base.Owl_optimise_generic_sig.Sig.Learning_Rate)

                                                          Module Sig.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Learning_Rate (owl-base.Owl_optimise_generic_sig.Sig.Learning_Rate)

                                                          Module Sig.Learning_Rate

                                                          Strategies for learning rate update

                                                          type typ =
                                                          1. | Adagrad of float
                                                          2. | Const of float
                                                          3. | Decay of float * float
                                                          4. | Exp_decay of float * float
                                                          5. | RMSprop of float * float
                                                          6. | Adam of float * float * float
                                                          7. | Schedule of float array

                                                          Representation of learning rate update strategies. Possible values include:

                                                          • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                          val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                          Update the cache of gradients.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Loss/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Loss/index.html index b7a7afa0c..abca2055a 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Loss/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl-base.Owl_optimise_generic_sig.Sig.Loss)

                                                          Module Sig.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Loss (owl-base.Owl_optimise_generic_sig.Sig.Loss)

                                                          Module Sig.Loss

                                                          Loss module

                                                          type typ =
                                                          1. | Hinge
                                                          2. | L1norm
                                                          3. | L2norm
                                                          4. | Quadratic
                                                          5. | Cross_entropy
                                                          6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Types of loss functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Momentum/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Momentum/index.html index 17889c25f..fcef77f6f 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Momentum/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl-base.Owl_optimise_generic_sig.Sig.Momentum)

                                                          Module Sig.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Momentum (owl-base.Owl_optimise_generic_sig.Sig.Momentum)

                                                          Module Sig.Momentum

                                                          Momentum module

                                                          type typ =
                                                          1. | Standard of float
                                                          2. | Nesterov of float
                                                          3. | None

                                                          Types of momentum functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Params/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Params/index.html index 37c0fba7f..9330bdd25 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Params/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Params/index.html @@ -1,5 +1,5 @@ -Params (owl-base.Owl_optimise_generic_sig.Sig.Params)

                                                          Module Sig.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : +Params (owl-base.Owl_optimise_generic_sig.Sig.Params)

                                                          Module Sig.Params

                                                          Params module

                                                          type typ = {
                                                          1. mutable epochs : float;
                                                          2. mutable batch : Batch.typ;
                                                          3. mutable gradient : Gradient.typ;
                                                          4. mutable loss : Loss.typ;
                                                          5. mutable learning_rate : Learning_Rate.typ;
                                                          6. mutable regularisation : Regularisation.typ;
                                                          7. mutable momentum : Momentum.typ;
                                                          8. mutable clipping : Clipping.typ;
                                                          9. mutable stopping : Stopping.typ;
                                                          10. mutable checkpoint : Checkpoint.typ;
                                                          11. mutable verbosity : bool;
                                                          }

                                                          Type definition of parameter.

                                                          val default : unit -> typ

                                                          Create module typ with default values.

                                                          val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Regularisation/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Regularisation/index.html index b6f2ec46d..01b68b402 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Regularisation/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl-base.Owl_optimise_generic_sig.Sig.Regularisation)

                                                          Module Sig.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Regularisation (owl-base.Owl_optimise_generic_sig.Sig.Regularisation)

                                                          Module Sig.Regularisation

                                                          Regularisation module

                                                          type typ =
                                                          1. | L1norm of float
                                                          2. | L2norm of float
                                                          3. | Elastic_net of float * float
                                                          4. | None

                                                          Types of regularisation functions.

                                                          val run : typ -> Algodiff.t -> Algodiff.t

                                                          Execute the computations defined in module typ.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Stopping/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Stopping/index.html index 802d3d07b..5583e0509 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Stopping/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl-base.Owl_optimise_generic_sig.Sig.Stopping)

                                                          Module Sig.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          +Stopping (owl-base.Owl_optimise_generic_sig.Sig.Stopping)

                                                          Module Sig.Stopping

                                                          Stopping module

                                                          type typ =
                                                          1. | Const of float
                                                          2. | Early of int * int
                                                          3. | None

                                                          Types of stopping functions.

                                                          val run : typ -> float -> bool

                                                          Execute the computations defined in module typ.

                                                          val default : typ -> typ

                                                          Create module typ with default values.

                                                          val to_string : typ -> string

                                                          Convert the module typ to its string representation.

                                                          diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Utils/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Utils/index.html index ec2a08a7f..0565a4dc5 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Utils/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl-base.Owl_optimise_generic_sig.Sig.Utils)

                                                          Module Sig.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : +Utils (owl-base.Owl_optimise_generic_sig.Sig.Utils)

                                                          Module Sig.Utils

                                                          Utils module

                                                          val sample_num : Algodiff.t -> int

                                                          Return the total number of samples in passed in ndarray.

                                                          val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                          draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                          val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/index.html b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/index.html index c3b89b06c..b1e68801e 100644 --- a/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/index.html +++ b/docs/owl-base/Owl_optimise_generic_sig/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_optimise_generic_sig.Sig)

                                                          Module type Owl_optimise_generic_sig.Sig

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : +Sig (owl-base.Owl_optimise_generic_sig.Sig)

                                                          Module type Owl_optimise_generic_sig.Sig

                                                          module Utils : sig ... end

                                                          Utils module

                                                          module Learning_Rate : sig ... end

                                                          Strategies for learning rate update

                                                          module Batch : sig ... end

                                                          Batch module

                                                          module Loss : sig ... end

                                                          Loss module

                                                          module Gradient : sig ... end

                                                          Gradient module

                                                          module Momentum : sig ... end

                                                          Momentum module

                                                          module Regularisation : sig ... end

                                                          Regularisation module

                                                          module Clipping : sig ... end

                                                          Clipping module

                                                          module Stopping : sig ... end

                                                          Stopping module

                                                          module Checkpoint : sig ... end

                                                          Checkpoint module

                                                          module Params : sig ... end

                                                          Params module

                                                          Core functions
                                                          val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> @@ -28,4 +28,4 @@ (string -> unit) -> Algodiff.t -> Algodiff.t -> - Checkpoint.state

                                                          TODO

                                                          + Checkpoint.state

                                                          This function is minimize the weights in a compiled neural network of graph structure.

                                                          diff --git a/docs/owl-base/Owl_pretty/index.html b/docs/owl-base/Owl_pretty/index.html index 79625ac4c..4031cccec 100644 --- a/docs/owl-base/Owl_pretty/index.html +++ b/docs/owl-base/Owl_pretty/index.html @@ -1,5 +1,5 @@ -Owl_pretty (owl-base.Owl_pretty)

                                                          Module Owl_pretty

                                                          Pretty print the n-dimensional array

                                                          Ndarray pretty printer
                                                          val pp_dsnda : +Owl_pretty (owl-base.Owl_pretty)

                                                          Module Owl_pretty

                                                          Pretty print the n-dimensional array

                                                          Ndarray pretty printer
                                                          val pp_dsnda : Stdlib.Format.formatter -> ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> unit

                                                          pp_dsnda is the pretty printer for n-dimensional arrays.

                                                          val dsnda_to_string : diff --git a/docs/owl-base/Owl_types/index.html b/docs/owl-base/Owl_types/index.html index 1a1985701..a5e6b44ab 100644 --- a/docs/owl-base/Owl_types/index.html +++ b/docs/owl-base/Owl_types/index.html @@ -1,3 +1,3 @@ -Owl_types (owl-base.Owl_types)

                                                          Module Owl_types

                                                          This module defines the types shared by various sub-libraries in Owl. Note that they just wrappers, to find the exact module signature, please refer to the definition in the corresponding module.

                                                          include module type of struct include Owl_types_common end
                                                          type number = Owl_types_common.number =
                                                          1. | F32
                                                          2. | F64
                                                          3. | C32
                                                          4. | C64
                                                          type ('a, 'b) owl_arr = +Owl_types (owl-base.Owl_types)

                                                          Module Owl_types

                                                          This module defines the types shared by various sub-libraries in Owl. Note that they just wrappers, to find the exact module signature, please refer to the definition in the corresponding module.

                                                          include module type of struct include Owl_types_common end
                                                          type number = Owl_types_common.number =
                                                          1. | F32
                                                          2. | F64
                                                          3. | C32
                                                          4. | C64
                                                          type ('a, 'b) owl_arr = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                          type index = Owl_types_common.index =
                                                          1. | I of int
                                                          2. | L of int list
                                                          3. | R of int list
                                                          type slice = index list
                                                          type index_ = Owl_types_common.index_ =
                                                          1. | I_ of int
                                                          2. | L_ of int array
                                                          3. | R_ of int array
                                                          type slice_ = index_ array
                                                          type padding = Owl_types_common.padding =
                                                          1. | SAME
                                                          2. | VALID
                                                          type device_type = Owl_types_common.device_type =
                                                          1. | CPU
                                                          2. | OpenCL
                                                          3. | CUDA
                                                          module type Ndarray_Basic = sig ... end
                                                          module type Ndarray_Compare = sig ... end
                                                          module type Ndarray_Mutable = sig ... end
                                                          module type Ndarray_Algodiff = sig ... end
                                                          module type Ndarray_Numdiff = sig ... end
                                                          module type Stats_Dist = sig ... end
                                                          module type Computation_Device = sig ... end
                                                          diff --git a/docs/owl-base/Owl_types/module-type-Computation_Device/A/Linalg/index.html b/docs/owl-base/Owl_types/module-type-Computation_Device/A/Linalg/index.html index 9d67c7bd7..4a1165b12 100644 --- a/docs/owl-base/Owl_types/module-type-Computation_Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_types/module-type-Computation_Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_types.Computation_Device.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_types.Computation_Device.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_types/module-type-Computation_Device/A/Mat/index.html b/docs/owl-base/Owl_types/module-type-Computation_Device/A/Mat/index.html index 50a9491d5..6ae7543e4 100644 --- a/docs/owl-base/Owl_types/module-type-Computation_Device/A/Mat/index.html +++ b/docs/owl-base/Owl_types/module-type-Computation_Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types.Computation_Device.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_types.Computation_Device.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_types/module-type-Computation_Device/A/Scalar/index.html b/docs/owl-base/Owl_types/module-type-Computation_Device/A/Scalar/index.html index f14179107..bdfd04fcb 100644 --- a/docs/owl-base/Owl_types/module-type-Computation_Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_types/module-type-Computation_Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_types.Computation_Device.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_types.Computation_Device.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_types/module-type-Computation_Device/A/index.html b/docs/owl-base/Owl_types/module-type-Computation_Device/A/index.html index e050773ae..35c8aba00 100644 --- a/docs/owl-base/Owl_types/module-type-Computation_Device/A/index.html +++ b/docs/owl-base/Owl_types/module-type-Computation_Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_types.Computation_Device.A)

                                                          Module Computation_Device.A

                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_types.Computation_Device.A)

                                                          Module Computation_Device.A

                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types/module-type-Computation_Device/index.html b/docs/owl-base/Owl_types/module-type-Computation_Device/index.html index e0141c698..810840a28 100644 --- a/docs/owl-base/Owl_types/module-type-Computation_Device/index.html +++ b/docs/owl-base/Owl_types/module-type-Computation_Device/index.html @@ -1,2 +1,2 @@ -Computation_Device (owl-base.Owl_types.Computation_Device)

                                                          Module type Owl_types.Computation_Device

                                                          include Owl_types_computation_device.Sig
                                                          Type definition
                                                          type device

                                                          TODO

                                                          type value

                                                          TODO

                                                          Core functions
                                                          val make_device : unit -> device

                                                          TODO

                                                          val arr_to_value : A.arr -> value

                                                          TODO

                                                          val value_to_arr : value -> A.arr

                                                          TODO

                                                          val elt_to_value : A.elt -> value

                                                          TODO

                                                          val value_to_elt : value -> A.elt

                                                          TODO

                                                          val value_to_float : value -> float

                                                          TODO

                                                          val is_arr : value -> bool

                                                          TODO

                                                          val is_elt : value -> bool

                                                          TODO

                                                          +Computation_Device (owl-base.Owl_types.Computation_Device)

                                                          Module type Owl_types.Computation_Device

                                                          include Owl_types_computation_device.Sig
                                                          Type definition
                                                          type device

                                                          TODO

                                                          type value

                                                          TODO

                                                          Core functions
                                                          val make_device : unit -> device

                                                          TODO

                                                          val arr_to_value : A.arr -> value

                                                          TODO

                                                          val value_to_arr : value -> A.arr

                                                          TODO

                                                          val elt_to_value : A.elt -> value

                                                          TODO

                                                          val value_to_elt : value -> A.elt

                                                          TODO

                                                          val value_to_float : value -> float

                                                          TODO

                                                          val is_arr : value -> bool

                                                          TODO

                                                          val is_elt : value -> bool

                                                          TODO

                                                          diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Linalg/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Linalg/index.html index 5ef76c66e..529ae9124 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Linalg/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_types.Ndarray_Algodiff.Linalg)

                                                          Module Ndarray_Algodiff.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_types.Ndarray_Algodiff.Linalg)

                                                          Module Ndarray_Algodiff.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Mat/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Mat/index.html index 791ed5e8f..cbbde88df 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Mat/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types.Ndarray_Algodiff.Mat)

                                                          Module Ndarray_Algodiff.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_types.Ndarray_Algodiff.Mat)

                                                          Module Ndarray_Algodiff.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Scalar/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Scalar/index.html index 21487760e..3b2a83f35 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Scalar/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_types.Ndarray_Algodiff.Scalar)

                                                          Module Ndarray_Algodiff.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_types.Ndarray_Algodiff.Scalar)

                                                          Module Ndarray_Algodiff.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/index.html index e1484e9e6..304ec34f6 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Algodiff/index.html @@ -1,5 +1,5 @@ -Ndarray_Algodiff (owl-base.Owl_types.Ndarray_Algodiff)

                                                          Module type Owl_types.Ndarray_Algodiff

                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +Ndarray_Algodiff (owl-base.Owl_types.Ndarray_Algodiff)

                                                          Module type Owl_types.Ndarray_Algodiff

                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Basic/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Basic/index.html index c75930b50..d12c59f2d 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Basic/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Basic/index.html @@ -1,5 +1,5 @@ -Ndarray_Basic (owl-base.Owl_types.Ndarray_Basic)

                                                          Module type Owl_types.Ndarray_Basic

                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +Ndarray_Basic (owl-base.Owl_types.Ndarray_Basic)

                                                          Module type Owl_types.Ndarray_Basic

                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Compare/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Compare/index.html index e141ee4ce..a8958653a 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Compare/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Compare/index.html @@ -1,5 +1,5 @@ -Ndarray_Compare (owl-base.Owl_types.Ndarray_Compare)

                                                          Module type Owl_types.Ndarray_Compare

                                                          include Owl_types_ndarray_compare.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +Ndarray_Compare (owl-base.Owl_types.Ndarray_Compare)

                                                          Module type Owl_types.Ndarray_Compare

                                                          include Owl_types_ndarray_compare.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Linalg/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Linalg/index.html index 17f3f5d5d..c6647dfd5 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Linalg/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_types.Ndarray_Mutable.Linalg)

                                                          Module Ndarray_Mutable.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_types.Ndarray_Mutable.Linalg)

                                                          Module Ndarray_Mutable.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Mat/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Mat/index.html index 3d1823e55..0fd178509 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Mat/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types.Ndarray_Mutable.Mat)

                                                          Module Ndarray_Mutable.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_types.Ndarray_Mutable.Mat)

                                                          Module Ndarray_Mutable.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Scalar/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Scalar/index.html index 548ee3eb7..6cd2c40d1 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Scalar/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_types.Ndarray_Mutable.Scalar)

                                                          Module Ndarray_Mutable.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_types.Ndarray_Mutable.Scalar)

                                                          Module Ndarray_Mutable.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/index.html index 23403a448..57a651e2c 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Mutable/index.html @@ -1,5 +1,5 @@ -Ndarray_Mutable (owl-base.Owl_types.Ndarray_Mutable)

                                                          Module type Owl_types.Ndarray_Mutable

                                                          include Owl_types_ndarray_mutable.Sig
                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +Ndarray_Mutable (owl-base.Owl_types.Ndarray_Mutable)

                                                          Module type Owl_types.Ndarray_Mutable

                                                          include Owl_types_ndarray_mutable.Sig
                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types/module-type-Ndarray_Numdiff/index.html b/docs/owl-base/Owl_types/module-type-Ndarray_Numdiff/index.html index 15b8edc29..f5c20fba3 100644 --- a/docs/owl-base/Owl_types/module-type-Ndarray_Numdiff/index.html +++ b/docs/owl-base/Owl_types/module-type-Ndarray_Numdiff/index.html @@ -1,5 +1,5 @@ -Ndarray_Numdiff (owl-base.Owl_types.Ndarray_Numdiff)

                                                          Module type Owl_types.Ndarray_Numdiff

                                                          include Owl_types_ndarray_numdiff.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +Ndarray_Numdiff (owl-base.Owl_types.Ndarray_Numdiff)

                                                          Module type Owl_types.Ndarray_Numdiff

                                                          include Owl_types_ndarray_numdiff.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types/module-type-Stats_Dist/Linalg/index.html b/docs/owl-base/Owl_types/module-type-Stats_Dist/Linalg/index.html index 9f04586fd..f166a5ffc 100644 --- a/docs/owl-base/Owl_types/module-type-Stats_Dist/Linalg/index.html +++ b/docs/owl-base/Owl_types/module-type-Stats_Dist/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_types.Stats_Dist.Linalg)

                                                          Module Stats_Dist.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_types.Stats_Dist.Linalg)

                                                          Module Stats_Dist.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_types/module-type-Stats_Dist/Mat/index.html b/docs/owl-base/Owl_types/module-type-Stats_Dist/Mat/index.html index 289212245..11da24c67 100644 --- a/docs/owl-base/Owl_types/module-type-Stats_Dist/Mat/index.html +++ b/docs/owl-base/Owl_types/module-type-Stats_Dist/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types.Stats_Dist.Mat)

                                                          Module Stats_Dist.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_types.Stats_Dist.Mat)

                                                          Module Stats_Dist.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_types/module-type-Stats_Dist/Scalar/index.html b/docs/owl-base/Owl_types/module-type-Stats_Dist/Scalar/index.html index 96a5708c9..c76509fb1 100644 --- a/docs/owl-base/Owl_types/module-type-Stats_Dist/Scalar/index.html +++ b/docs/owl-base/Owl_types/module-type-Stats_Dist/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_types.Stats_Dist.Scalar)

                                                          Module Stats_Dist.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_types.Stats_Dist.Scalar)

                                                          Module Stats_Dist.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_types/module-type-Stats_Dist/index.html b/docs/owl-base/Owl_types/module-type-Stats_Dist/index.html index 4e2061ea7..129773482 100644 --- a/docs/owl-base/Owl_types/module-type-Stats_Dist/index.html +++ b/docs/owl-base/Owl_types/module-type-Stats_Dist/index.html @@ -1,5 +1,5 @@ -Stats_Dist (owl-base.Owl_types.Stats_Dist)

                                                          Module type Owl_types.Stats_Dist

                                                          include Owl_types_stats_dist.Sig
                                                          include Owl_types_ndarray_mutable.Sig
                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +Stats_Dist (owl-base.Owl_types.Stats_Dist)

                                                          Module type Owl_types.Stats_Dist

                                                          include Owl_types_stats_dist.Sig
                                                          include Owl_types_ndarray_mutable.Sig
                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types_common/index.html b/docs/owl-base/Owl_types_common/index.html index 8334f5271..b0ce19055 100644 --- a/docs/owl-base/Owl_types_common/index.html +++ b/docs/owl-base/Owl_types_common/index.html @@ -1,3 +1,3 @@ -Owl_types_common (owl-base.Owl_types_common)

                                                          Module Owl_types_common

                                                          type number =
                                                          1. | F32
                                                          2. | F64
                                                          3. | C32
                                                          4. | C64
                                                          type ('a, 'b) owl_arr = +Owl_types_common (owl-base.Owl_types_common)

                                                          Module Owl_types_common

                                                          type number =
                                                          1. | F32
                                                          2. | F64
                                                          3. | C32
                                                          4. | C64
                                                          type ('a, 'b) owl_arr = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                          type index =
                                                          1. | I of int
                                                          2. | L of int list
                                                          3. | R of int list
                                                          type slice = index list
                                                          type index_ =
                                                          1. | I_ of int
                                                          2. | L_ of int array
                                                          3. | R_ of int array
                                                          type slice_ = index_ array
                                                          type padding =
                                                          1. | SAME
                                                          2. | VALID
                                                          type device_type =
                                                          1. | CPU
                                                          2. | OpenCL
                                                          3. | CUDA
                                                          diff --git a/docs/owl-base/Owl_types_computation_device/index.html b/docs/owl-base/Owl_types_computation_device/index.html index 44d47c2f2..1b3d316ad 100644 --- a/docs/owl-base/Owl_types_computation_device/index.html +++ b/docs/owl-base/Owl_types_computation_device/index.html @@ -1,2 +1,2 @@ -Owl_types_computation_device (owl-base.Owl_types_computation_device)

                                                          Module Owl_types_computation_device

                                                          module type Sig = sig ... end
                                                          +Owl_types_computation_device (owl-base.Owl_types_computation_device)

                                                          Module Owl_types_computation_device

                                                          module type Sig = sig ... end
                                                          diff --git a/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Linalg/index.html b/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Linalg/index.html index b60ada8c9..521b9a4f1 100644 --- a/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Linalg/index.html +++ b/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_types_computation_device.Sig.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_types_computation_device.Sig.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Mat/index.html b/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Mat/index.html index a70925e29..0be3fe247 100644 --- a/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Mat/index.html +++ b/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types_computation_device.Sig.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_types_computation_device.Sig.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Scalar/index.html b/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Scalar/index.html index 77a2366ef..bab12f179 100644 --- a/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Scalar/index.html +++ b/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_types_computation_device.Sig.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_types_computation_device.Sig.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/index.html b/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/index.html index b06736a6d..ff174e7ce 100644 --- a/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/index.html +++ b/docs/owl-base/Owl_types_computation_device/module-type-Sig/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_types_computation_device.Sig.A)

                                                          Module Sig.A

                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_types_computation_device.Sig.A)

                                                          Module Sig.A

                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types_computation_device/module-type-Sig/index.html b/docs/owl-base/Owl_types_computation_device/module-type-Sig/index.html index 25a4b494a..40cf53df6 100644 --- a/docs/owl-base/Owl_types_computation_device/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_computation_device/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_types_computation_device.Sig)

                                                          Module type Owl_types_computation_device.Sig

                                                          Type definition
                                                          type device

                                                          TODO

                                                          type value

                                                          TODO

                                                          Core functions
                                                          val make_device : unit -> device

                                                          TODO

                                                          val arr_to_value : A.arr -> value

                                                          TODO

                                                          val value_to_arr : value -> A.arr

                                                          TODO

                                                          val elt_to_value : A.elt -> value

                                                          TODO

                                                          val value_to_elt : value -> A.elt

                                                          TODO

                                                          val value_to_float : value -> float

                                                          TODO

                                                          val is_arr : value -> bool

                                                          TODO

                                                          val is_elt : value -> bool

                                                          TODO

                                                          +Sig (owl-base.Owl_types_computation_device.Sig)

                                                          Module type Owl_types_computation_device.Sig

                                                          Type definition
                                                          type device

                                                          TODO

                                                          type value

                                                          TODO

                                                          Core functions
                                                          val make_device : unit -> device

                                                          TODO

                                                          val arr_to_value : A.arr -> value

                                                          TODO

                                                          val value_to_arr : value -> A.arr

                                                          TODO

                                                          val elt_to_value : A.elt -> value

                                                          TODO

                                                          val value_to_elt : value -> A.elt

                                                          TODO

                                                          val value_to_float : value -> float

                                                          TODO

                                                          val is_arr : value -> bool

                                                          TODO

                                                          val is_elt : value -> bool

                                                          TODO

                                                          diff --git a/docs/owl-base/Owl_types_computation_engine/index.html b/docs/owl-base/Owl_types_computation_engine/index.html index 9b38918af..14586ba48 100644 --- a/docs/owl-base/Owl_types_computation_engine/index.html +++ b/docs/owl-base/Owl_types_computation_engine/index.html @@ -1,2 +1,2 @@ -Owl_types_computation_engine (owl-base.Owl_types_computation_engine)

                                                          Module Owl_types_computation_engine

                                                          module type Sig = sig ... end
                                                          +Owl_types_computation_engine (owl-base.Owl_types_computation_engine)

                                                          Module Owl_types_computation_engine

                                                          module type Sig = sig ... end
                                                          diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Linalg/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Linalg/index.html index 4ba873254..1d46fb079 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Linalg/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Linalg/index.html @@ -1,33 +1,33 @@ -Linalg (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Linalg)

                                                          Module Operator.Linalg

                                                          val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                          TODO

                                                          val svd : +Linalg (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Linalg)

                                                          Module Operator.Linalg

                                                          inv a computes the inverse of the matrix a. Returns a new array representing the inverse matrix.

                                                          logdet a computes the natural logarithm of the determinant of the matrix a. Returns the logarithm of the determinant as a scalar.

                                                          val chol : ?upper:bool -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                          chol ?upper a performs the Cholesky decomposition of the positive-definite matrix a.

                                                          • upper specifies whether to return the upper or lower triangular matrix. If upper is true, returns the upper triangular matrix, otherwise the lower triangular matrix. Returns a new array representing the Cholesky factor.

                                                          qr a performs the QR decomposition of the matrix a. Returns a tuple of two arrays (Q, R), where Q is an orthogonal matrix and R is an upper triangular matrix.

                                                          lq a performs the LQ decomposition of the matrix a. Returns a tuple of two arrays (L, Q), where L is a lower triangular matrix and Q is an orthogonal matrix.

                                                          svd ?thin a performs the Singular Value Decomposition (SVD) of the matrix a.

                                                          • thin specifies whether to return the reduced form of the SVD. Returns a tuple of three arrays (U, S, V), where U and V are orthogonal matrices, and S is a diagonal matrix containing the singular values.
                                                          val lyapunov : + Symbol.Shape.Type.arr

                                                          sylvester a b c solves the Sylvester equation A*X + X*B = C for the unknown matrix X. Returns a new array representing the solution matrix X.

                                                          val discrete_lyapunov : + Symbol.Shape.Type.arr

                                                          lyapunov a q solves the continuous Lyapunov equation A*X + X*A^T = Q for the unknown matrix X. Returns a new array representing the solution matrix X.

                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          val linsolve : + Symbol.Shape.Type.arr

                                                          discrete_lyapunov ?solver a q solves the discrete Lyapunov equation A*X*A^T - X + Q = 0 for the unknown matrix X.

                                                          • solver specifies the method to use: `default`, `bilinear`, or `direct`. Returns a new array representing the solution matrix X.
                                                          val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                          TODO

                                                          linsolve ?trans ?typ a b solves the linear system A*X = B for the unknown matrix X.

                                                          • trans specifies whether to transpose the matrix A.
                                                          • typ specifies the type of matrix A: `n` for normal, `u` for upper triangular, and `l` for lower triangular. Returns a new array representing the solution matrix X.

                                                          care ?diag_r a b q r solves the Continuous-time Algebraic Riccati Equation (CARE) A*X + X*A^T - X*B*R^-1*B^T*X + Q = 0 for the unknown matrix X.

                                                          • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                                          + Symbol.Shape.Type.arr

                                                          dare ?diag_r a b q r solves the Discrete-time Algebraic Riccati Equation (DARE) A*X*A^T - X - (A*X*B^T)*inv(B*X*B^T + R)*(A*X*B^T)^T + Q = 0 for the unknown matrix X.

                                                          • diag_r if true, R is assumed to be diagonal. Returns a new array representing the solution matrix X.
                                                          diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Mat/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Mat/index.html index 75cbcfacc..db3b33803 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Mat/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Mat)

                                                          Module Operator.Mat

                                                          val eye : int -> Symbol.Shape.Type.arr

                                                          TODO

                                                          TODO

                                                          TODO

                                                          TODO

                                                          +Mat (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Mat)

                                                          Module Operator.Mat

                                                          val eye : int -> Symbol.Shape.Type.arr

                                                          eye n creates an n x n identity matrix, where all the elements on the main diagonal are 1, and all other elements are 0. Returns a new array representing the identity matrix.

                                                          diagm ?k v creates a diagonal matrix from the array v.

                                                          • k specifies the diagonal to fill. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array representing the diagonal matrix.

                                                          triu ?k a returns the upper triangular part of the array a, with elements below the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the upper triangular part.

                                                          tril ?k a returns the lower triangular part of the array a, with elements above the k-th diagonal zeroed. The main diagonal is 0, positive values refer to diagonals above the main, and negative values refer to diagonals below the main. Returns a new array with the lower triangular part.

                                                          diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Scalar/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Scalar/index.html index f21f82748..2d0107ae1 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Scalar/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Scalar/index.html @@ -1,20 +1,20 @@ -Scalar (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Scalar)

                                                          Module Operator.Scalar

                                                          val add : +Scalar (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Scalar)

                                                          Module Operator.Scalar

                                                          add a b returns the sum of the scalars a and b.

                                                          sub a b returns the difference of the scalars a and b.

                                                          mul a b returns the product of the scalars a and b.

                                                          div a b returns the quotient of the scalars a and b.

                                                          val atan2 : + Symbol.Shape.Type.elt

                                                          pow a b returns the scalar a raised to the power of b.

                                                          + Symbol.Shape.Type.elt

                                                          atan2 y x returns the arctangent of y / x, considering the signs of x and y to determine the correct quadrant.

                                                          abs a returns the absolute value of the scalar a.

                                                          neg a returns the negation of the scalar a.

                                                          sqr a returns the square of the scalar a.

                                                          sqrt a returns the square root of the scalar a.

                                                          exp a returns the exponential of the scalar a.

                                                          log a returns the natural logarithm of the scalar a.

                                                          log2 a returns the base-2 logarithm of the scalar a.

                                                          log10 a returns the base-10 logarithm of the scalar a.

                                                          signum a returns the signum function of the scalar a, which is -1 for negative, 0 for zero, and 1 for positive values.

                                                          floor a returns the greatest integer less than or equal to the scalar a.

                                                          ceil a returns the smallest integer greater than or equal to the scalar a.

                                                          round a returns the nearest integer to the scalar a.

                                                          sin a returns the sine of the scalar a.

                                                          cos a returns the cosine of the scalar a.

                                                          tan a returns the tangent of the scalar a.

                                                          sinh a returns the hyperbolic sine of the scalar a.

                                                          cosh a returns the hyperbolic cosine of the scalar a.

                                                          tanh a returns the hyperbolic tangent of the scalar a.

                                                          asin a returns the arcsine of the scalar a.

                                                          acos a returns the arccosine of the scalar a.

                                                          atan a returns the arctangent of the scalar a.

                                                          asinh a returns the inverse hyperbolic sine of the scalar a.

                                                          acosh a returns the inverse hyperbolic cosine of the scalar a.

                                                          atanh a returns the inverse hyperbolic tangent of the scalar a.

                                                          relu a applies the Rectified Linear Unit (ReLU) function to the scalar a, returning max(0, a).

                                                          dawsn a returns Dawson's function of the scalar a.

                                                          sigmoid a returns the sigmoid function of the scalar a.

                                                          diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html index 065ea240b..c41ba441b 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : +Linalg (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Linalg)

                                                          Module A.Linalg

                                                          val inv : arr -> arr
                                                          val logdet : arr -> elt
                                                          val chol : ?upper:bool -> arr -> arr
                                                          val svd : ?thin:bool -> arr -> arr * arr * arr
                                                          val qr : arr -> arr * arr
                                                          val lq : arr -> arr * arr
                                                          val sylvester : arr -> arr -> arr -> arr
                                                          val lyapunov : arr -> arr -> arr
                                                          val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html index 6af83f657..1874e4f61 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          +Mat (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Mat)

                                                          Module A.Mat

                                                          val diagm : ?k:int -> arr -> arr
                                                          val triu : ?k:int -> arr -> arr
                                                          val tril : ?k:int -> arr -> arr
                                                          val eye : int -> arr
                                                          diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html index c70b67a84..c6593e470 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          +Scalar (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A.Scalar)

                                                          Module A.Scalar

                                                          val add : elt -> elt -> elt
                                                          val sub : elt -> elt -> elt
                                                          val mul : elt -> elt -> elt
                                                          val div : elt -> elt -> elt
                                                          val pow : elt -> elt -> elt
                                                          val atan2 : elt -> elt -> elt
                                                          val abs : elt -> elt
                                                          val neg : elt -> elt
                                                          val sqr : elt -> elt
                                                          val sqrt : elt -> elt
                                                          val exp : elt -> elt
                                                          val log : elt -> elt
                                                          val log2 : elt -> elt
                                                          val log10 : elt -> elt
                                                          val signum : elt -> elt
                                                          val floor : elt -> elt
                                                          val ceil : elt -> elt
                                                          val round : elt -> elt
                                                          val sin : elt -> elt
                                                          val cos : elt -> elt
                                                          val tan : elt -> elt
                                                          val sinh : elt -> elt
                                                          val cosh : elt -> elt
                                                          val tanh : elt -> elt
                                                          val asin : elt -> elt
                                                          val acos : elt -> elt
                                                          val atan : elt -> elt
                                                          val asinh : elt -> elt
                                                          val acosh : elt -> elt
                                                          val atanh : elt -> elt
                                                          val relu : elt -> elt
                                                          val dawsn : elt -> elt
                                                          val sigmoid : elt -> elt
                                                          diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html index f1c401bc3..328877eaa 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                                                          Module Device.A

                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : +A (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device.A)

                                                          Module Device.A

                                                          include Owl_types_ndarray_algodiff.Sig
                                                          include Owl_types_ndarray_eltcmp.Sig
                                                          include Owl_types_ndarray_basic.Sig
                                                          type arr
                                                          type elt
                                                          val empty : int array -> arr
                                                          val zeros : int array -> arr
                                                          val ones : int array -> arr
                                                          val create : int array -> elt -> arr
                                                          val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                          val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                          val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                          val bernoulli : ?p:elt -> int array -> arr
                                                          val init : int array -> (int -> elt) -> arr
                                                          val init_nd : int array -> (int array -> elt) -> arr
                                                          val shape : arr -> int array
                                                          val numel : arr -> int
                                                          val get : arr -> int array -> elt
                                                          val set : arr -> int array -> elt -> unit
                                                          val get_slice : int list list -> arr -> arr
                                                          val set_slice : int list list -> arr -> arr -> unit
                                                          val get_fancy : Owl_types_common.index list -> arr -> arr
                                                          val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                          val copy : arr -> arr
                                                          val copy_ : out:arr -> arr -> unit
                                                          val reset : arr -> unit
                                                          val reshape : arr -> int array -> arr
                                                          val reverse : arr -> arr
                                                          val tile : arr -> int array -> arr
                                                          val repeat : arr -> int array -> arr
                                                          val concatenate : ?axis:int -> arr array -> arr
                                                          val stack : ?axis:int -> arr array -> arr
                                                          val split : ?axis:int -> int array -> arr -> arr array
                                                          val expand : ?hi:bool -> arr -> int -> arr
                                                          val squeeze : ?axis:int array -> arr -> arr
                                                          val draw : ?axis:int -> arr -> int -> arr * int array
                                                          val map : (elt -> elt) -> arr -> arr
                                                          val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                          val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                          val one_hot : int -> arr -> arr
                                                          val pad : ?v:elt -> int list list -> arr -> arr
                                                          val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html index 4ce43854e..bbca23023 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/Device/index.html @@ -1,2 +1,2 @@ -Device (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

                                                          Module Type.Device

                                                          Type definition
                                                          type device

                                                          TODO

                                                          type value

                                                          TODO

                                                          Core functions
                                                          val make_device : unit -> device

                                                          TODO

                                                          val arr_to_value : A.arr -> value

                                                          TODO

                                                          val value_to_arr : value -> A.arr

                                                          TODO

                                                          val elt_to_value : A.elt -> value

                                                          TODO

                                                          val value_to_elt : value -> A.elt

                                                          TODO

                                                          val value_to_float : value -> float

                                                          TODO

                                                          val is_arr : value -> bool

                                                          TODO

                                                          val is_elt : value -> bool

                                                          TODO

                                                          +Device (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type.Device)

                                                          Module Type.Device

                                                          Type definition
                                                          type device

                                                          TODO

                                                          type value

                                                          TODO

                                                          Core functions
                                                          val make_device : unit -> device

                                                          TODO

                                                          val arr_to_value : A.arr -> value

                                                          TODO

                                                          val value_to_arr : value -> A.arr

                                                          TODO

                                                          val elt_to_value : A.elt -> value

                                                          TODO

                                                          val value_to_elt : value -> A.elt

                                                          TODO

                                                          val value_to_float : value -> float

                                                          TODO

                                                          val is_arr : value -> bool

                                                          TODO

                                                          val is_elt : value -> bool

                                                          TODO

                                                          diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html index 0a977fdcc..8a1d185e7 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/Type/index.html @@ -1,5 +1,5 @@ -Type (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type)

                                                          Module Shape.Type

                                                          Type definition
                                                          type state =
                                                          1. | Valid
                                                          2. | Invalid
                                                            (*

                                                            TODO

                                                            *)

                                                          TODO

                                                          and block = {
                                                          1. size : int;
                                                          2. block_id : int;
                                                          3. mutable active : t option;
                                                          4. mutable memory : Device.value;
                                                          5. mutable nodes : t list;
                                                          }

                                                          block type keeps a reference to a block of memory and to the nodes sharing that block.

                                                          and attr = {
                                                          1. mutable op : op;
                                                          2. mutable freeze : bool;
                                                          3. mutable reuse : bool;
                                                          4. mutable state : state;
                                                          5. mutable shape : int array option array;
                                                          6. mutable value : Device.value array;
                                                          7. mutable block : block array option;
                                                          }

                                                          TODO

                                                          and arr =
                                                          1. | Arr of t
                                                          and elt =
                                                          1. | Elt of t
                                                          and op =
                                                          1. | Noop
                                                          2. | Var
                                                          3. | Const
                                                          4. | Empty of int array
                                                          5. | Zeros of int array
                                                          6. | Ones of int array
                                                          7. | Create of int array
                                                          8. | Sequential of int array
                                                          9. | Uniform of int array
                                                          10. | Gaussian of int array
                                                          11. | Bernoulli of int array
                                                          12. | Init of int array * int -> elt
                                                          13. | Get of int array
                                                          14. | Set of int array
                                                          15. | GetSlice of int list list
                                                          16. | SetSlice of int list list
                                                          17. | GetFancy of Owl_types_common.index list
                                                          18. | SetFancy of Owl_types_common.index list
                                                          19. | Copy
                                                          20. | Reset
                                                          21. | Reshape of int array
                                                          22. | Reverse
                                                          23. | Tile of int array
                                                          24. | Repeat of int array
                                                          25. | Pad of elt * int list list
                                                          26. | Concatenate of int
                                                          27. | Stack of int
                                                          28. | Split of int * int array
                                                          29. | Draw of int * int
                                                          30. | Map of elt -> elt
                                                          31. | Fold of int * elt -> elt -> elt
                                                          32. | Scan of int * elt -> elt -> elt
                                                          33. | OneHot of int
                                                          34. | OfArray of int array
                                                          35. | Delay of Device.A.arr -> Device.A.arr
                                                          36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                          37. | LazyPrint of int option +Type (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape.Type)

                                                            Module Shape.Type

                                                            Type definition
                                                            type state =
                                                            1. | Valid
                                                            2. | Invalid
                                                              (*

                                                              TODO

                                                              *)

                                                            TODO

                                                            and block = {
                                                            1. size : int;
                                                            2. block_id : int;
                                                            3. mutable active : t option;
                                                            4. mutable memory : Device.value;
                                                            5. mutable nodes : t list;
                                                            }

                                                            block type keeps a reference to a block of memory and to the nodes sharing that block.

                                                            and attr = {
                                                            1. mutable op : op;
                                                            2. mutable freeze : bool;
                                                            3. mutable reuse : bool;
                                                            4. mutable state : state;
                                                            5. mutable shape : int array option array;
                                                            6. mutable value : Device.value array;
                                                            7. mutable block : block array option;
                                                            }

                                                            TODO

                                                            and arr =
                                                            1. | Arr of t
                                                            and elt =
                                                            1. | Elt of t
                                                            and op =
                                                            1. | Noop
                                                            2. | Var
                                                            3. | Const
                                                            4. | Empty of int array
                                                            5. | Zeros of int array
                                                            6. | Ones of int array
                                                            7. | Create of int array
                                                            8. | Sequential of int array
                                                            9. | Uniform of int array
                                                            10. | Gaussian of int array
                                                            11. | Bernoulli of int array
                                                            12. | Init of int array * int -> elt
                                                            13. | Get of int array
                                                            14. | Set of int array
                                                            15. | GetSlice of int list list
                                                            16. | SetSlice of int list list
                                                            17. | GetFancy of Owl_types_common.index list
                                                            18. | SetFancy of Owl_types_common.index list
                                                            19. | Copy
                                                            20. | Reset
                                                            21. | Reshape of int array
                                                            22. | Reverse
                                                            23. | Tile of int array
                                                            24. | Repeat of int array
                                                            25. | Pad of elt * int list list
                                                            26. | Concatenate of int
                                                            27. | Stack of int
                                                            28. | Split of int * int array
                                                            29. | Draw of int * int
                                                            30. | Map of elt -> elt
                                                            31. | Fold of int * elt -> elt -> elt
                                                            32. | Scan of int * elt -> elt -> elt
                                                            33. | OneHot of int
                                                            34. | OfArray of int array
                                                            35. | Delay of Device.A.arr -> Device.A.arr
                                                            36. | DelayArray of int array * Device.A.arr array -> Device.A.arr
                                                            37. | LazyPrint of int option * int option * bool option * (Device.A.elt -> string) option
                                                            38. | Abs
                                                            39. | Neg
                                                            40. | Floor
                                                            41. | Ceil
                                                            42. | Round
                                                            43. | Sqr
                                                            44. | Sqrt
                                                            45. | Log
                                                            46. | Log2
                                                            47. | Log10
                                                            48. | Exp
                                                            49. | Sin
                                                            50. | Cos
                                                            51. | Tan
                                                            52. | Sinh
                                                            53. | Cosh
                                                            54. | Tanh
                                                            55. | Asin
                                                            56. | Acos
                                                            57. | Atan
                                                            58. | Asinh
                                                            59. | Acosh
                                                            60. | Atanh
                                                            61. | Min of bool * int
                                                            62. | Max of bool * int
                                                            63. | Sum of bool * int
                                                            64. | SumReduce of int array
                                                            65. | Signum
                                                            66. | Sigmoid
                                                            67. | Relu
                                                            68. | Dawsn
                                                            69. | Min'
                                                            70. | Max'
                                                            71. | Sum'
                                                            72. | LogSumExp'
                                                            73. | LogSumExp of bool * int
                                                            74. | L1norm'
                                                            75. | L2norm'
                                                            76. | L2NormSqr'
                                                            77. | ClipByValue
                                                            78. | ClipByL2norm
                                                            79. | Pow
                                                            80. | ScalarPow
                                                            81. | PowScalar
                                                            82. | Atan2
                                                            83. | ScalarAtan2
                                                            84. | Atan2Scalar
                                                            85. | Hypot
                                                            86. | Min2
                                                            87. | Max2
                                                            88. | Add
                                                            89. | Sub
                                                            90. | Mul
                                                            91. | Div
                                                            92. | AddScalar
                                                            93. | SubScalar
                                                            94. | MulScalar
                                                            95. | DivScalar
                                                            96. | ScalarAdd
                                                            97. | ScalarSub
                                                            98. | ScalarMul
                                                            99. | ScalarDiv
                                                            100. | FMA
                                                            101. | EltEqual
                                                            102. | EltNotEqual
                                                            103. | EltLess
                                                            104. | EltGreater
                                                            105. | EltLessEqual
                                                            106. | EltGreaterEqual
                                                            107. | EltEqualScalar
                                                            108. | EltNotEqualScalar
                                                            109. | EltLessScalar
                                                            110. | EltGreaterScalar
                                                            111. | EltLessEqualScalar
                                                            112. | EltGreaterEqualScalar
                                                            113. | Conv1d of Owl_types_common.padding * int array
                                                            114. | Conv2d of Owl_types_common.padding * int array
                                                            115. | Conv3d of Owl_types_common.padding * int array
                                                            116. | TransposeConv1d of Owl_types_common.padding * int array
                                                            117. | TransposeConv2d of Owl_types_common.padding * int array
                                                            118. | TransposeConv3d of Owl_types_common.padding * int array
                                                            119. | DilatedConv1d of Owl_types_common.padding * int array * int array
                                                            120. | DilatedConv2d of Owl_types_common.padding * int array * int array
                                                            121. | DilatedConv3d of Owl_types_common.padding * int array * int array
                                                            122. | MaxPool1d of Owl_types_common.padding * int array * int array
                                                            123. | MaxPool2d of Owl_types_common.padding * int array * int array
                                                            124. | MaxPool3d of Owl_types_common.padding * int array * int array
                                                            125. | AvgPool1d of Owl_types_common.padding * int array * int array
                                                            126. | AvgPool2d of Owl_types_common.padding * int array * int array
                                                            127. | AvgPool3d of Owl_types_common.padding * int array * int array
                                                            128. | UpSampling2d of int array
                                                            129. | Conv1dBackwardInput of int array
                                                            130. | Conv1dBackwardKernel of int array
                                                            131. | Conv2dBackwardInput of int array
                                                            132. | Conv2dBackwardKernel of int array
                                                            133. | Conv3dBackwardInput of int array
                                                            134. | Conv3dBackwardKernel of int array
                                                            135. | TransposeConv1dBackwardInput of int array
                                                            136. | TransposeConv1dBackwardKernel of int array
                                                            137. | TransposeConv2dBackwardInput of int array
                                                            138. | TransposeConv2dBackwardKernel of int array
                                                            139. | TransposeConv3dBackwardInput of int array
                                                            140. | TransposeConv3dBackwardKernel of int array
                                                            141. | DilatedConv1dBackwardInput of int array * int array
                                                            142. | DilatedConv1dBackwardKernel of int array * int array
                                                            143. | DilatedConv2dBackwardInput of int array * int array
                                                            144. | DilatedConv2dBackwardKernel of int array * int array
                                                            145. | DilatedConv3dBackwardInput of int array * int array
                                                            146. | DilatedConv3dBackwardKernel of int array * int array
                                                            147. | MaxPool1dBackward of Owl_types_common.padding * int array * int array
                                                            148. | MaxPool2dBackward of Owl_types_common.padding * int array * int array
                                                            149. | MaxPool3dBackward of Owl_types_common.padding * int array * int array
                                                            150. | AvgPool1dBackward of Owl_types_common.padding * int array * int array
                                                            151. | AvgPool2dBackward of Owl_types_common.padding * int array * int array
                                                            152. | AvgPool3dBackward of Owl_types_common.padding * int array * int array
                                                            153. | UpSampling2dBackward of int array
                                                            154. | RowNum
                                                            155. | ColNum
                                                            156. | Row
                                                            157. | Rows of int array
                                                            158. | CopyRowTo
                                                            159. | CopyColTo
                                                            160. | Dot of bool * bool * elt * elt
                                                            161. | Inv
                                                            162. | Trace
                                                            163. | Transpose of int array
                                                            164. | ToRows
                                                            165. | OfRows
                                                            166. | Scalar_Add
                                                            167. | Scalar_Sub
                                                            168. | Scalar_Mul
                                                            169. | Scalar_Div
                                                            170. | Scalar_Pow
                                                            171. | Scalar_Atan2
                                                            172. | Scalar_Abs
                                                            173. | Scalar_Neg
                                                            174. | Scalar_Sqr
                                                            175. | Scalar_Sqrt
                                                            176. | Scalar_Exp
                                                            177. | Scalar_Log
                                                            178. | Scalar_Log2
                                                            179. | Scalar_Log10
                                                            180. | Scalar_Signum
                                                            181. | Scalar_Floor
                                                            182. | Scalar_Ceil
                                                            183. | Scalar_Round
                                                            184. | Scalar_Sin
                                                            185. | Scalar_Cos
                                                            186. | Scalar_Tan
                                                            187. | Scalar_Sinh
                                                            188. | Scalar_Cosh
                                                            189. | Scalar_Tanh
                                                            190. | Scalar_Asin
                                                            191. | Scalar_Acos
                                                            192. | Scalar_Atan
                                                            193. | Scalar_Asinh
                                                            194. | Scalar_Acosh
                                                            195. | Scalar_Atanh
                                                            196. | Scalar_Relu
                                                            197. | Scalar_Dawsn
                                                            198. | Scalar_Sigmoid
                                                            199. | Fused_Adagrad of float * float
                                                              (*

                                                              TODO

                                                              *)
                                                            diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/index.html index 0b8414671..3b7b023dd 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/Shape/index.html @@ -1,5 +1,5 @@ -Shape (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape)

                                                            Module Symbol.Shape

                                                            Core functions
                                                            val infer_shape : +Shape (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol.Shape)

                                                            Module Symbol.Shape

                                                            Core functions
                                                            val infer_shape : Type.op -> Type.attr Owl_graph.node array -> int array option array

                                                            TODO

                                                            diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/index.html index ac6d8f0ae..5980aea16 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/Symbol/index.html @@ -1,5 +1,5 @@ -Symbol (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol)

                                                            Module Operator.Symbol

                                                            Core functions
                                                            val op_to_str : Shape.Type.op -> string

                                                            TODO

                                                            val is_random_variable : Shape.Type.op -> bool

                                                            TODO

                                                            val refnum : 'a Owl_graph.node -> int

                                                            TODO

                                                            val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                                            TODO

                                                            val node_numel : Shape.Type.attr Owl_graph.node -> int

                                                            TODO

                                                            val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                                            TODO

                                                            val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                                            TODO

                                                            val shape_to_str : int array option array -> string

                                                            TODO

                                                            val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                                            TODO

                                                            val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                                            TODO

                                                            val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                                            TODO

                                                            val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                                            TODO

                                                            val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                                            TODO

                                                            val make_node : +Symbol (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator.Symbol)

                                                            Module Operator.Symbol

                                                            Core functions
                                                            val op_to_str : Shape.Type.op -> string

                                                            TODO

                                                            val is_random_variable : Shape.Type.op -> bool

                                                            TODO

                                                            val refnum : 'a Owl_graph.node -> int

                                                            TODO

                                                            val node_shape : Shape.Type.attr Owl_graph.node -> int array

                                                            TODO

                                                            val node_numel : Shape.Type.attr Owl_graph.node -> int

                                                            TODO

                                                            val is_shape_unknown : Shape.Type.attr Owl_graph.node -> bool

                                                            TODO

                                                            val infer_shape_graph : Shape.Type.attr Owl_graph.node array -> unit

                                                            TODO

                                                            val shape_to_str : int array option array -> string

                                                            TODO

                                                            val node_to_str : Shape.Type.attr Owl_graph.node -> string

                                                            TODO

                                                            val node_to_arr : Shape.Type.t -> Shape.Type.arr

                                                            TODO

                                                            val arr_to_node : Shape.Type.arr -> Shape.Type.t

                                                            TODO

                                                            val node_to_elt : Shape.Type.t -> Shape.Type.elt

                                                            TODO

                                                            val elt_to_node : Shape.Type.elt -> Shape.Type.t

                                                            TODO

                                                            val make_node : ?name:string -> ?value:Shape.Type.Device.value array -> ?shape:int array option array -> diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/index.html index 5a61ef707..a08af42fc 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/Operator/index.html @@ -1,58 +1,58 @@ -Operator (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator)

                                                            Module Optimiser.Operator

                                                            Vectorised functions
                                                            val empty : int array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val zeros : int array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val ones : int array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val sequential : +Operator (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser.Operator)

                                                            Module Optimiser.Operator

                                                            Vectorised functions

                                                            noop arr performs no operation on the array arr and returns it as is. This can be useful as a placeholder function. Returns the input array arr.

                                                            val empty : int array -> Symbol.Shape.Type.arr

                                                            empty shape creates an uninitialized array with the specified shape. The contents of the array are undefined. Returns a new array with the given shape.

                                                            val zeros : int array -> Symbol.Shape.Type.arr

                                                            zeros shape creates an array with the specified shape, filled with zeros. Returns a new array with all elements initialized to zero.

                                                            val ones : int array -> Symbol.Shape.Type.arr

                                                            ones shape creates an array with the specified shape, filled with ones. Returns a new array with all elements initialized to one.

                                                            val create : int array -> Symbol.Shape.Type.elt -> Symbol.Shape.Type.arr

                                                            create shape value creates an array with the specified shape, filled with the given value. Returns a new array with all elements initialized to value.

                                                            val sequential : ?a:Symbol.Shape.Type.elt -> ?step:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val uniform : + Symbol.Shape.Type.arr

                                                            sequential ?a ?step shape creates an array with the specified shape, filled with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1. Returns a new array with sequential values.

                                                            val uniform : ?a:Symbol.Shape.Type.elt -> ?b:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val gaussian : + Symbol.Shape.Type.arr

                                                            uniform ?a ?b shape creates an array with the specified shape, filled with random values drawn from a uniform distribution over [a, b\). If a and b are not provided, the default range is [0, 1\) . Returns a new array with uniform random values.

                                                            val gaussian : ?mu:Symbol.Shape.Type.elt -> ?sigma:Symbol.Shape.Type.elt -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val init_nd : + Symbol.Shape.Type.arr

                                                            gaussian ?mu ?sigma shape creates an array with the specified shape, filled with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1. Returns a new array with Gaussian random values.

                                                            val bernoulli : ?p:Symbol.Shape.Type.elt -> int array -> Symbol.Shape.Type.arr

                                                            bernoulli ?p shape creates an array with the specified shape, filled with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. Returns a new array with Bernoulli random values.

                                                            val init : int array -> (int -> Symbol.Shape.Type.elt) -> Symbol.Shape.Type.arr

                                                            init shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the linear index of the element as input. Returns a new array with elements initialized by the function f.

                                                            val init_nd : int array -> (int array -> Symbol.Shape.Type.elt) -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val shape : Symbol.Shape.Type.arr -> int array

                                                            TODO

                                                            val numel : Symbol.Shape.Type.arr -> int

                                                            TODO

                                                            TODO

                                                            val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                                            TODO

                                                            val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val set_slice : + Symbol.Shape.Type.arr

                                                            init_nd shape f creates an array with the specified shape, where each element is initialized using the function f. The function f takes the multidimensional index of the element as input. Returns a new array with elements initialized by the function f.

                                                            val shape : Symbol.Shape.Type.arr -> int array

                                                            shape arr returns the shape of the array arr as an array of integers, each representing the size of the corresponding dimension.

                                                            val numel : Symbol.Shape.Type.arr -> int

                                                            numel arr returns the total number of elements in the array arr.

                                                            get arr index retrieves the element at the specified multidimensional index in the array arr. Returns the value of the element at the given index.

                                                            val set : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.elt -> unit

                                                            set arr index value sets the element at the specified multidimensional index in the array arr to the given value.

                                                            val get_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                            get_slice slices arr extracts a slice from the array arr according to the list of slices. Each element in slices specifies the range for the corresponding dimension. Returns a new array with the extracted slice.

                                                            val set_slice : int list list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                                            TODO

                                                            val get_fancy : + unit

                                                            set_slice slices src dest sets the slice in dest defined by slices with the values from the source array src.

                                                            val set_fancy : + Symbol.Shape.Type.arr

                                                            get_fancy indices arr extracts elements from the array arr according to the list of indices. Each element in indices specifies an advanced indexing method. Returns a new array with the extracted elements.

                                                            val set_fancy : Owl_types.index list -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> - unit

                                                            TODO

                                                            val copy_ : out:'a -> 'b -> 'c

                                                            TODO

                                                            val reset : Symbol.Shape.Type.arr -> unit

                                                            TODO

                                                            val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val pad : + unit

                                                            set_fancy indices src dest sets the elements in dest defined by indices with the values from the source array src.

                                                            copy arr creates a deep copy of the array arr. Returns a new array that is a copy of arr.

                                                            val copy_ : out:'a -> 'b -> 'c

                                                            copy_ ~out src copies the contents of the array src into the pre-allocated array out.

                                                            val reset : Symbol.Shape.Type.arr -> unit

                                                            reset arr sets all elements of the array arr to zero.

                                                            val reshape : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            reshape arr shape reshapes the array arr into the new shape. The total number of elements must remain the same. Returns a new array with the specified shape.

                                                            reverse arr reverses the elements of the array arr along each dimension. Returns a new array with the elements reversed.

                                                            val tile : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            tile arr reps replicates the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the tiled data.

                                                            val repeat : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            repeat arr reps repeats the elements of the array arr according to the number of repetitions specified in reps for each dimension. Returns a new array with the repeated data.

                                                            TODO

                                                            val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val concatenate : + Symbol.Shape.Type.arr

                                                            pad ?v padding arr pads the array arr with the value v according to the padding specification for each dimension. If v is not provided, the default padding value is zero. Returns a new array with the padded data.

                                                            val expand : ?hi:bool -> Symbol.Shape.Type.arr -> int -> Symbol.Shape.Type.arr

                                                            expand ?hi arr n expands the dimensions of the array arr by inserting a new dimension of size n. If hi is true, the new dimension is added at the beginning; otherwise, it is added at the end. Returns a new array with the expanded dimensions.

                                                            val squeeze : ?axis:int array -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr

                                                            squeeze ?axis arr removes single-dimensional entries from the shape of the array arr. If axis is provided, only the specified dimensions are removed. Returns a new array with the squeezed shape.

                                                            val concatenate : ?axis:int -> Symbol.Shape.Type.arr array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val concat : + Symbol.Shape.Type.arr

                                                            concatenate ?axis arrays concatenates a sequence of arrays along the specified axis. If axis is not provided, the arrays are concatenated along the first axis. Returns a new array with the concatenated data.

                                                            val stack : ?axis:int -> Symbol.Shape.Type.arr array -> Symbol.Shape.Type.arr

                                                            stack ?axis arrays stacks a sequence of arrays along a new dimension at the specified axis. If axis is not provided, the arrays are stacked along the first axis. Returns a new array with the stacked data.

                                                            val split : ?axis:int -> 'a -> 'b -> 'c

                                                            TODO

                                                            concat ~axis a b concatenates the arrays a and b along the specified axis. Returns a new array with the concatenated data.

                                                            val split : ?axis:int -> 'a -> 'b -> 'c

                                                            split ?axis src num_or_sections splits the array src into multiple sub-arrays along the specified axis.

                                                            • num_or_sections specifies the number of equal-sized sub-arrays or the indices where to split. Returns an array of sub-arrays.
                                                            val draw : ?axis:int -> Symbol.Shape.Type.arr -> int -> - Symbol.Shape.Type.arr * 'a array

                                                            TODO

                                                            val map : + Symbol.Shape.Type.arr * 'a array

                                                            draw ?axis arr n randomly draws n samples from the array arr along the specified axis. Returns a tuple containing the sampled array and an array of indices from which the samples were drawn.

                                                            map f arr applies the function f to each element of the array arr. Returns a new array with the results of applying f.

                                                            fold ?axis f init arr reduces the array arr along the specified axis using the function f and an initial value init. If axis is not provided, the reduction is performed on all elements. Returns a new array with the reduced values.

                                                            TODO

                                                            val delay : + Symbol.Shape.Type.arr

                                                            scan ?axis f arr performs a cumulative reduction of the array arr along the specified axis using the function f. Returns a new array with the cumulative results.

                                                            one_hot depth arr converts the array arr into a one-hot encoded array with a specified depth. Returns a new array with one-hot encoding.

                                                            delay f x returns f x. It allows to use a function that is not tracked by the computation graph and delay its evaluation. The output must have the same shape as the input.

                                                            val delay_array : @@ -65,356 +65,356 @@ ?header:bool -> ?fmt:(Symbol.Shape.Type.Device.A.elt -> string) -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                                            val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                                            TODO

                                                            lazy_print x prints the output of x when it is evaluated. Is implemented as an identity node. For information about the optional parameters, refer to the print function of the Ndarray module.

                                                            val print : ?max_row:'a -> ?max_col:'b -> ?header:'c -> ?fmt:'d -> 'e -> unit

                                                            print ?max_row ?max_col ?header ?fmt data prints a representation of the given data.

                                                            • max_row is an optional parameter specifying the maximum number of rows to print.
                                                            • max_col is an optional parameter specifying the maximum number of columns to print.
                                                            • header is an optional parameter to include a header in the output.
                                                            • fmt is an optional parameter to specify the format of the output.

                                                            abs arr computes the absolute value of each element in the array arr. Returns a new array with the absolute values.

                                                            neg arr negates each element in the array arr. Returns a new array with each element negated.

                                                            floor arr applies the floor function to each element in the array arr. Returns a new array with the floor of each element.

                                                            ceil arr applies the ceiling function to each element in the array arr. Returns a new array with the ceiling of each element.

                                                            round arr rounds each element in the array arr to the nearest integer. Returns a new array with each element rounded to the nearest integer.

                                                            sqr arr computes the square of each element in the array arr. Returns a new array with the square of each element.

                                                            sqrt arr computes the square root of each element in the array arr. Returns a new array with the square roots of the elements.

                                                            log arr computes the natural logarithm of each element in the array arr. Returns a new array with the natural logarithms of the elements.

                                                            log2 arr computes the base-2 logarithm of each element in the array arr. Returns a new array with the base-2 logarithms of the elements.

                                                            log10 arr computes the base-10 logarithm of each element in the array arr. Returns a new array with the base-10 logarithms of the elements.

                                                            exp arr computes the exponential function of each element in the array arr. Returns a new array with the exponentials of the elements.

                                                            sin arr computes the sine of each element in the array arr. Returns a new array with the sines of the elements.

                                                            cos arr computes the cosine of each element in the array arr. Returns a new array with the cosines of the elements.

                                                            tan arr computes the tangent of each element in the array arr. Returns a new array with the tangents of the elements.

                                                            sinh arr computes the hyperbolic sine of each element in the array arr. Returns a new array with the hyperbolic sines of the elements.

                                                            cosh arr computes the hyperbolic cosine of each element in the array arr. Returns a new array with the hyperbolic cosines of the elements.

                                                            tanh arr computes the hyperbolic tangent of each element in the array arr. Returns a new array with the hyperbolic tangents of the elements.

                                                            asin arr computes the arcsine of each element in the array arr. Returns a new array with the arcsines of the elements.

                                                            acos arr computes the arccosine of each element in the array arr. Returns a new array with the arccosines of the elements.

                                                            atan arr computes the arctangent of each element in the array arr. Returns a new array with the arctangents of the elements.

                                                            asinh arr computes the inverse hyperbolic sine of each element in the array arr. Returns a new array with the inverse hyperbolic sines of the elements.

                                                            acosh arr computes the inverse hyperbolic cosine of each element in the array arr. Returns a new array with the inverse hyperbolic cosines of the elements.

                                                            atanh arr computes the inverse hyperbolic tangent of each element in the array arr. Returns a new array with the inverse hyperbolic tangents of the elements.

                                                            val min : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            min ?axis ?keep_dims arr computes the minimum value along the specified axis of the array arr.

                                                            • axis specifies the axis along which to compute the minimum.
                                                            • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the minimum values.
                                                            val max : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            max ?axis ?keep_dims arr computes the maximum value along the specified axis of the array arr.

                                                            • axis specifies the axis along which to compute the maximum.
                                                            • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the maximum values.
                                                            val sum : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val sum_reduce : + Symbol.Shape.Type.arr

                                                            sum ?axis ?keep_dims arr computes the sum of elements along the specified axis of the array arr.

                                                            • axis specifies the axis along which to compute the sum.
                                                            • keep_dims specifies whether to keep the reduced dimensions. Returns a new array with the sum of elements.
                                                            val sum_reduce : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val log_sum_exp : + Symbol.Shape.Type.arr

                                                            sum_reduce ?axis arr computes the sum of elements along the specified axes of the array arr.

                                                            • axis specifies the axes along which to compute the sum. Returns a new array with the sum of elements.

                                                            signum arr computes the signum function of each element in the array arr. Returns a new array where each element is -1, 0, or 1, depending on the sign of the corresponding element in arr.

                                                            sigmoid arr computes the sigmoid function of each element in the array arr. Returns a new array with the sigmoid values.

                                                            relu arr applies the Rectified Linear Unit (ReLU) function to each element in the array arr. Returns a new array where each element is the maximum of 0 and the corresponding element in arr.

                                                            dawsn arr computes Dawson's function of each element in the array arr. Returns a new array with Dawson's function values.

                                                            min' arr computes the minimum value in the array arr. Returns the minimum value as an element.

                                                            max' arr computes the maximum value in the array arr. Returns the maximum value as an element.

                                                            sum' arr computes the sum of all elements in the array arr. Returns the sum as an element.

                                                            log_sum_exp' arr computes the log-sum-exp of all elements in the array arr. Returns the log-sum-exp as an element.

                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val clip_by_value : + Symbol.Shape.Type.arr

                                                            log_sum_exp ?axis ?keep_dims arr computes the log of the sum of exponentials of elements along the specified axis of the array arr.

                                                            • axis specifies the axis along which to compute the log-sum-exp. If not specified, computes over all elements.
                                                            • keep_dims if true, retains reduced dimensions with size 1. Returns a new array with the log-sum-exp values.

                                                            l1norm' arr computes the L1 norm (sum of absolute values) of all elements in the array arr. Returns the L1 norm as an element.

                                                            l2norm' arr computes the L2 norm (Euclidean norm) of all elements in the array arr. Returns the L2 norm as an element.

                                                            l2norm_sqr' arr computes the squared L2 norm (sum of squared values) of all elements in the array arr. Returns the squared L2 norm as an element.

                                                            val clip_by_l2norm : + Symbol.Shape.Type.arr

                                                            clip_by_value ?amin ?amax arr clips the values in the array arr to the range amin, amax.

                                                            • amin specifies the minimum value to clip to.
                                                            • amax specifies the maximum value to clip to. Returns a new array with the values clipped to the specified range.

                                                            clip_by_l2norm max_norm arr clips the values in the array arr so that the L2 norm does not exceed max_norm. Returns a new array with the values clipped by the specified L2 norm.

                                                            val scalar_pow : + Symbol.Shape.Type.arr

                                                            pow base exp computes each element of the array base raised to the power of the corresponding element in exp. Returns a new array with the power values.

                                                            val pow_scalar : + Symbol.Shape.Type.arr

                                                            scalar_pow scalar arr raises the scalar value scalar to the power of each element in the array arr. Returns a new array with the power values.

                                                            val atan2 : + Symbol.Shape.Type.arr

                                                            pow_scalar arr scalar raises each element in the array arr to the power of the scalar value scalar. Returns a new array with the power values.

                                                            val scalar_atan2 : + Symbol.Shape.Type.arr

                                                            atan2 y x computes the element-wise arctangent of y / x, using the signs of the elements to determine the correct quadrant. Returns a new array with the arctangent values.

                                                            val atan2_scalar : + Symbol.Shape.Type.arr

                                                            scalar_atan2 scalar arr computes the element-wise arctangent of scalar / each element in the array arr. Returns a new array with the arctangent values.

                                                            val hypot : + Symbol.Shape.Type.arr

                                                            atan2_scalar arr scalar computes the element-wise arctangent of each element in the array arr / scalar. Returns a new array with the arctangent values.

                                                            hypot x y computes the hypotenuse (sqrt(x^2 + y^2)) for each element in the arrays x and y. Returns a new array with the hypotenuse values.

                                                            min2 a b computes the element-wise minimum of arrays a and b. Returns a new array with the minimum values.

                                                            max2 a b computes the element-wise maximum of arrays a and b. Returns a new array with the maximum values.

                                                            add a b computes the element-wise addition of arrays a and b. Returns a new array with the sum of elements.

                                                            sub a b computes the element-wise subtraction of arrays a and b. Returns a new array with the difference of elements.

                                                            mul a b computes the element-wise multiplication of arrays a and b. Returns a new array with the product of elements.

                                                            val add_scalar : + Symbol.Shape.Type.arr

                                                            div a b computes the element-wise division of arrays a and b. Returns a new array with the quotient of elements.

                                                            val sub_scalar : + Symbol.Shape.Type.arr

                                                            add_scalar arr scalar adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                                            val mul_scalar : + Symbol.Shape.Type.arr

                                                            sub_scalar arr scalar subtracts the scalar value scalar from each element in the array arr. Returns a new array with the resulting values.

                                                            val div_scalar : + Symbol.Shape.Type.arr

                                                            mul_scalar arr scalar multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                                            val scalar_add : + Symbol.Shape.Type.arr

                                                            div_scalar arr scalar divides each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                                            val scalar_sub : + Symbol.Shape.Type.arr

                                                            scalar_add scalar arr adds the scalar value scalar to each element in the array arr. Returns a new array with the resulting values.

                                                            val scalar_mul : + Symbol.Shape.Type.arr

                                                            scalar_sub scalar arr subtracts each element in the array arr from the scalar value scalar. Returns a new array with the resulting values.

                                                            val scalar_div : + Symbol.Shape.Type.arr

                                                            scalar_mul scalar arr multiplies each element in the array arr by the scalar value scalar. Returns a new array with the resulting values.

                                                            scalar_div scalar arr divides the scalar value scalar by each element in the array arr. Returns a new array with the resulting values.

                                                            val elt_equal : + Symbol.Shape.Type.arr

                                                            fma a b c computes the fused multiply-add operation, multiplying arrays a and b, then adding array c. Returns a new array with the resulting values.

                                                            val elt_not_equal : + Symbol.Shape.Type.arr

                                                            elt_equal a b performs element-wise equality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are equal, and false otherwise.

                                                            val elt_less : + Symbol.Shape.Type.arr

                                                            elt_not_equal a b performs element-wise inequality comparison between arrays a and b. Returns a new array where each element is true if the corresponding elements in a and b are not equal, and false otherwise.

                                                            val elt_greater : + Symbol.Shape.Type.arr

                                                            elt_less a b performs element-wise less-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than that in b, and false otherwise.

                                                            val elt_less_equal : + Symbol.Shape.Type.arr

                                                            elt_greater a b performs element-wise greater-than comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than that in b, and false otherwise.

                                                            val elt_greater_equal : + Symbol.Shape.Type.arr

                                                            elt_less_equal a b performs element-wise less-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is less than or equal to that in b, and false otherwise.

                                                            val elt_equal_scalar : + Symbol.Shape.Type.arr

                                                            elt_greater_equal a b performs element-wise greater-than-or-equal-to comparison between arrays a and b. Returns a new array where each element is true if the corresponding element in a is greater than or equal to that in b, and false otherwise.

                                                            val elt_not_equal_scalar : + Symbol.Shape.Type.arr

                                                            elt_equal_scalar arr scalar performs element-wise equality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it equals scalar, and false otherwise.

                                                            val elt_less_scalar : + Symbol.Shape.Type.arr

                                                            elt_not_equal_scalar arr scalar performs element-wise inequality comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it does not equal scalar, and false otherwise.

                                                            val elt_greater_scalar : + Symbol.Shape.Type.arr

                                                            elt_less_scalar arr scalar performs element-wise less-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than scalar, and false otherwise.

                                                            val elt_less_equal_scalar : + Symbol.Shape.Type.arr

                                                            elt_greater_scalar arr scalar performs element-wise greater-than comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than scalar, and false otherwise.

                                                            TODO

                                                            val elt_greater_equal_scalar : + Symbol.Shape.Type.arr

                                                            elt_less_equal_scalar arr scalar performs element-wise less-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is less than or equal to scalar, and false otherwise.

                                                            TODO

                                                            val conv1d : + Symbol.Shape.Type.arr

                                                            elt_greater_equal_scalar arr scalar performs element-wise greater-than-or-equal-to comparison between each element in the array arr and the scalar value scalar. Returns a new array where each element is true if it is greater than or equal to scalar, and false otherwise.

                                                            val conv2d : + Symbol.Shape.Type.arr

                                                            conv1d ?padding input kernel strides performs a 1-dimensional convolution on the input array using the specified kernel.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • strides specifies the stride length. Returns a new array with the result of the convolution.
                                                            val conv3d : + Symbol.Shape.Type.arr

                                                            conv2d ?padding input kernel strides performs a 2-dimensional convolution on the input array using the specified kernel.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • strides specifies the stride length. Returns a new array with the result of the convolution.
                                                            val transpose_conv1d : + Symbol.Shape.Type.arr

                                                            conv3d ?padding input kernel strides performs a 3-dimensional convolution on the input array using the specified kernel.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • strides specifies the stride length. Returns a new array with the result of the convolution.
                                                            val transpose_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val transpose_conv2d : + Symbol.Shape.Type.arr

                                                            transpose_conv1d ?padding input kernel strides performs a 1-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                                            val transpose_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val transpose_conv3d : + Symbol.Shape.Type.arr

                                                            transpose_conv2d ?padding input kernel strides performs a 2-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                                            val transpose_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val dilated_conv1d : + Symbol.Shape.Type.arr

                                                            transpose_conv3d ?padding input kernel strides performs a 3-dimensional transposed convolution (also known as deconvolution) on the input array using the specified kernel.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • strides specifies the stride length. Returns a new array with the result of the transposed convolution.
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val dilated_conv2d : + Symbol.Shape.Type.arr

                                                            dilated_conv1d ?padding input kernel strides dilations performs a 1-dimensional dilated convolution on the input array using the specified kernel.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • strides specifies the stride length.
                                                            • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                                            val dilated_conv2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val dilated_conv3d : + Symbol.Shape.Type.arr

                                                            dilated_conv2d ?padding input kernel strides dilations performs a 2-dimensional dilated convolution on the input array using the specified kernel.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • strides specifies the stride length.
                                                            • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                                            val dilated_conv3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val max_pool1d : + Symbol.Shape.Type.arr

                                                            dilated_conv3d ?padding input kernel strides dilations performs a 3-dimensional dilated convolution on the input array using the specified kernel.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • strides specifies the stride length.
                                                            • dilations specifies the dilation rate. Returns a new array with the result of the dilated convolution.
                                                            val max_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val max_pool2d : + Symbol.Shape.Type.arr

                                                            max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation on the input array.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                                            val max_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val max_pool3d : + Symbol.Shape.Type.arr

                                                            max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation on the input array.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                                            val max_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val avg_pool1d : + Symbol.Shape.Type.arr

                                                            max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation on the input array.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length. Returns a new array with the result of the max pooling.
                                                            val avg_pool1d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val avg_pool2d : + Symbol.Shape.Type.arr

                                                            avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation on the input array.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                                            val avg_pool2d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val avg_pool3d : + Symbol.Shape.Type.arr

                                                            avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation on the input array.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                                            val avg_pool3d : ?padding:Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val conv1d_backward_input : + Symbol.Shape.Type.arr

                                                            avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation on the input array.

                                                            • padding specifies the padding strategy (default is "valid").
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length. Returns a new array with the result of the average pooling.
                                                            val upsampling2d : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            upsampling2d input size performs a 2-dimensional upsampling on the input array.

                                                            • size specifies the upsampling factors for each dimension. Returns a new array with the upsampled data.

                                                            TODO

                                                            val conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                                            conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array.

                                                            • input is the original input array.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                                            val conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val conv2d_backward_input : + Symbol.Shape.Type.arr

                                                            conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional convolutional kernel.

                                                            • input is the original input array.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                                            TODO

                                                            val conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                                            conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array.

                                                            • input is the original input array.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                                            val conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val conv3d_backward_input : + Symbol.Shape.Type.arr

                                                            conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional convolutional kernel.

                                                            • input is the original input array.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.

                                                            TODO

                                                            val conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                                            conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array.

                                                            • input is the original input array.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the input.
                                                            val conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val transpose_conv1d_backward_input : + Symbol.Shape.Type.arr

                                                            conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional convolutional kernel.

                                                            • input is the original input array.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns a new array with the gradients of the kernel.
                                                            val transpose_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val transpose_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                                            transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the transposed convolution operation.

                                                            • input is the original input array.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                                            val transpose_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val transpose_conv2d_backward_input : + Symbol.Shape.Type.arr

                                                            transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 1-dimensional transposed convolutional kernel.

                                                            • input is the original input array.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                                            val transpose_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val transpose_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                                            transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the transposed convolution operation.

                                                            • input is the original input array.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                                            val transpose_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val transpose_conv3d_backward_input : + Symbol.Shape.Type.arr

                                                            transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 2-dimensional transposed convolutional kernel.

                                                            • input is the original input array.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                                            val transpose_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val transpose_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                                            transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the transposed convolution operation.

                                                            • input is the original input array.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the input.
                                                            val transpose_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val dilated_conv1d_backward_input : + Symbol.Shape.Type.arr

                                                            transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the 3-dimensional transposed convolutional kernel.

                                                            • input is the original input array.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns a new array with the gradients of the kernel.
                                                            val dilated_conv1d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val dilated_conv1d_backward_kernel : + Symbol.Shape.Type.arr

                                                            dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional input array for the dilated convolution operation.

                                                            • input is the original input array.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • dilations specifies the dilation rate.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                                            val dilated_conv1d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val dilated_conv2d_backward_input : + Symbol.Shape.Type.arr

                                                            dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 1-dimensional dilated convolutional kernel.

                                                            • input is the original input array.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • dilations specifies the dilation rate.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                                            val dilated_conv2d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val dilated_conv2d_backward_kernel : + Symbol.Shape.Type.arr

                                                            dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional input array for the dilated convolution operation.

                                                            • input is the original input array.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • dilations specifies the dilation rate.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                                            val dilated_conv2d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val dilated_conv3d_backward_input : + Symbol.Shape.Type.arr

                                                            dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 2-dimensional dilated convolutional kernel.

                                                            • input is the original input array.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • dilations specifies the dilation rate.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                                            val dilated_conv3d_backward_input : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val dilated_conv3d_backward_kernel : + Symbol.Shape.Type.arr

                                                            dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional input array for the dilated convolution operation.

                                                            • input is the original input array.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • dilations specifies the dilation rate.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the input.
                                                            val dilated_conv3d_backward_kernel : Symbol.Shape.Type.arr -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val max_pool1d_backward : + Symbol.Shape.Type.arr

                                                            dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the 3-dimensional dilated convolutional kernel.

                                                            • input is the original input array.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specifies the stride length.
                                                            • dilations specifies the dilation rate.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns a new array with the gradients of the kernel.
                                                            val max_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val max_pool2d_backward : + Symbol.Shape.Type.arr

                                                            max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after max pooling.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input array.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                                            val max_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val max_pool3d_backward : + Symbol.Shape.Type.arr

                                                            max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after max pooling.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input array.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                                            val max_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val avg_pool1d_backward : + Symbol.Shape.Type.arr

                                                            max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after max pooling.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input array.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns a new array with the gradients of the input.
                                                            val avg_pool1d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val avg_pool2d_backward : + Symbol.Shape.Type.arr

                                                            avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 1-dimensional input array after average pooling.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input array.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                                            val avg_pool2d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val avg_pool3d_backward : + Symbol.Shape.Type.arr

                                                            avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 2-dimensional input array after average pooling.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input array.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                                            val avg_pool3d_backward : Owl_types.padding -> Symbol.Shape.Type.arr -> int array -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val upsampling2d_backward : + Symbol.Shape.Type.arr

                                                            avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the 3-dimensional input array after average pooling.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input array.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specifies the stride length.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns a new array with the gradients of the input.
                                                            val upsampling2d_backward : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val row_num : Symbol.Shape.Type.arr -> int

                                                            TODO

                                                            val col_num : Symbol.Shape.Type.arr -> int

                                                            TODO

                                                            val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                                            TODO

                                                            val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                                            TODO

                                                            TODO

                                                            upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input array after 2-dimensional upsampling.

                                                            • input is the original input array.
                                                            • size specifies the upsampling factors for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns a new array with the gradients of the input.
                                                            val row_num : Symbol.Shape.Type.arr -> int

                                                            row_num arr returns the number of rows in the array arr.

                                                            val col_num : Symbol.Shape.Type.arr -> int

                                                            col_num arr returns the number of columns in the array arr.

                                                            row arr idx extracts the row at index idx from the array arr. Returns a new array containing the specified row.

                                                            val rows : Symbol.Shape.Type.arr -> int array -> Symbol.Shape.Type.arr

                                                            rows arr indices extracts multiple rows specified by indices from the array arr. Returns a new array containing the selected rows.

                                                            val copy_row_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                                            copy_row_to src src_idx dest_idx copies the row at index src_idx in the array src to the row at index dest_idx.

                                                            val copy_col_to : Symbol.Shape.Type.arr -> 'a -> 'b -> unit

                                                            copy_col_to src src_idx dest_idx copies the column at index src_idx in the array src to the column at index dest_idx.

                                                            diag ?k arr extracts the k-th diagonal from the array arr. If k is not provided, the main diagonal is extracted. Returns a new array containing the diagonal elements.

                                                            trace arr computes the sum of the elements on the main diagonal of the array arr. Returns the trace as an element.

                                                            val transpose : + Symbol.Shape.Type.arr

                                                            dot a b computes the dot product of the arrays a and b. Returns a new array with the result of the dot product.

                                                            val transpose : ?axis:int array -> Symbol.Shape.Type.arr -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val to_rows : Symbol.Shape.Type.arr -> 'a array

                                                            TODO

                                                            TODO

                                                            val to_cols : Symbol.Shape.Type.arr -> 'a array

                                                            TODO

                                                            TODO

                                                            val of_array : + Symbol.Shape.Type.arr

                                                            transpose ?axis arr transposes the array arr. If axis is provided, the transpose is performed according to the specified axes. Returns a new array with the transposed data.

                                                            val to_rows : Symbol.Shape.Type.arr -> 'a array

                                                            to_rows arr converts the array arr into an array of row vectors. Returns an array where each element is a row from the original array.

                                                            of_rows rows creates an array by stacking the row vectors in rows. Returns a new array constructed from the row vectors.

                                                            val to_cols : Symbol.Shape.Type.arr -> 'a array

                                                            to_cols arr converts the array arr into an array of column vectors. Returns an array where each element is a column from the original array.

                                                            of_cols cols creates an array by stacking the column vectors in cols. Returns a new array constructed from the column vectors.

                                                            val of_array : Symbol.Shape.Type.elt array -> int array -> - Symbol.Shape.Type.arr

                                                            TODO

                                                            val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                                            TODO

                                                            val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                                            TODO

                                                            Scalar functions
                                                            module Scalar : sig ... end
                                                            module Mat : sig ... end
                                                            module Linalg : sig ... end
                                                            + Symbol.Shape.Type.arr

                                                            of_array data shape creates an array from a flat array data with the specified shape. Returns a new array with the data arranged according to the shape.

                                                            val of_arrays : Symbol.Shape.Type.elt array array -> Symbol.Shape.Type.arr

                                                            of_arrays data creates an array from a 2D array data, where each sub-array represents a row. Returns a new array with the data from the 2D array.

                                                            val to_arrays : Symbol.Shape.Type.arr -> Symbol.Shape.Type.elt array array

                                                            to_arrays arr converts the array arr into a 2D array where each sub-array represents a row. Returns a 2D array with the data from the original array.

                                                            Scalar functions
                                                            module Scalar : sig ... end
                                                            module Mat : sig ... end
                                                            module Linalg : sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/index.html index b1e46fbef..44745fe22 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/Optimiser/index.html @@ -1,4 +1,4 @@ -Optimiser (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser)

                                                            Module Graph.Optimiser

                                                            Core functions
                                                            val estimate_complexity : 'a Owl_graph.node array -> int * int

                                                            TODO

                                                            val optimise_nodes : +Optimiser (owl-base.Owl_types_computation_engine.Sig.Graph.Optimiser)

                                                            Module Graph.Optimiser

                                                            Core functions
                                                            val estimate_complexity : 'a Owl_graph.node array -> int * int

                                                            TODO

                                                            val optimise_nodes : Operator.Symbol.Shape.Type.attr Owl_graph.node array -> unit

                                                            TODO

                                                            diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/index.html index b3ea635f8..cca269e39 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl-base.Owl_types_computation_engine.Sig.Graph)

                                                            Module Sig.Graph

                                                            Type definition
                                                            type graph

                                                            TODO

                                                            Core functions
                                                            val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                                                            TODO

                                                            val graph_to_dot : graph -> string

                                                            TODO

                                                            val graph_to_trace : graph -> string

                                                            TODO

                                                            val save_graph : 'a -> string -> unit

                                                            TODO

                                                            val load_graph : string -> 'a * 'b

                                                            TODO

                                                            val collect_rvs : +Graph (owl-base.Owl_types_computation_engine.Sig.Graph)

                                                            Module Sig.Graph

                                                            Type definition
                                                            type graph

                                                            TODO

                                                            Core functions
                                                            val shape_or_value : Optimiser.Operator.Symbol.Shape.Type.t -> string

                                                            TODO

                                                            val graph_to_dot : graph -> string

                                                            TODO

                                                            val graph_to_trace : graph -> string

                                                            TODO

                                                            val save_graph : 'a -> string -> unit

                                                            TODO

                                                            val load_graph : string -> 'a * 'b

                                                            TODO

                                                            val invalidate_rvs : graph -> unit

                                                            TODO

                                                            val make_graph : input:Optimiser.Operator.Symbol.Shape.Type.attr Owl_graph.node array -> diff --git a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/index.html b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/index.html index 491255d0a..a7656b9b3 100644 --- a/docs/owl-base/Owl_types_computation_engine/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_computation_engine/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_types_computation_engine.Sig)

                                                            Module type Owl_types_computation_engine.Sig

                                                            Core evaluation functions of the engine

                                                            TODO

                                                            TODO

                                                            val eval_graph : Graph.graph -> unit

                                                            TODO

                                                            +Sig (owl-base.Owl_types_computation_engine.Sig)

                                                            Module type Owl_types_computation_engine.Sig

                                                            Core evaluation functions of the engine

                                                            TODO

                                                            TODO

                                                            val eval_graph : Graph.graph -> unit

                                                            TODO

                                                            diff --git a/docs/owl-base/Owl_types_maths_basic/index.html b/docs/owl-base/Owl_types_maths_basic/index.html index 371e4ffc3..a0c48246d 100644 --- a/docs/owl-base/Owl_types_maths_basic/index.html +++ b/docs/owl-base/Owl_types_maths_basic/index.html @@ -1,2 +1,2 @@ -Owl_types_maths_basic (owl-base.Owl_types_maths_basic)

                                                            Module Owl_types_maths_basic

                                                            module type Sig = sig ... end
                                                            +Owl_types_maths_basic (owl-base.Owl_types_maths_basic)

                                                            Module Owl_types_maths_basic

                                                            module type Sig = sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_maths_basic/module-type-Sig/index.html b/docs/owl-base/Owl_types_maths_basic/module-type-Sig/index.html index 2b3424584..be88356a2 100644 --- a/docs/owl-base/Owl_types_maths_basic/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_maths_basic/module-type-Sig/index.html @@ -1,2 +1,2 @@ -Sig (owl-base.Owl_types_maths_basic.Sig)

                                                            Module type Owl_types_maths_basic.Sig

                                                            type elt
                                                            val add : elt -> elt -> elt
                                                            +Sig (owl-base.Owl_types_maths_basic.Sig)

                                                            Module type Owl_types_maths_basic.Sig

                                                            type elt
                                                            val add : elt -> elt -> elt
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_algodiff/index.html b/docs/owl-base/Owl_types_ndarray_algodiff/index.html index 22ba13752..1ac57bfa8 100644 --- a/docs/owl-base/Owl_types_ndarray_algodiff/index.html +++ b/docs/owl-base/Owl_types_ndarray_algodiff/index.html @@ -1,2 +1,2 @@ -Owl_types_ndarray_algodiff (owl-base.Owl_types_ndarray_algodiff)

                                                            Module Owl_types_ndarray_algodiff

                                                            module type Sig = sig ... end
                                                            +Owl_types_ndarray_algodiff (owl-base.Owl_types_ndarray_algodiff)

                                                            Module Owl_types_ndarray_algodiff

                                                            module type Sig = sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Linalg/index.html b/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Linalg/index.html index 62c05d89d..d5d538286 100644 --- a/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Linalg/index.html +++ b/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_types_ndarray_algodiff.Sig.Linalg)

                                                            Module Sig.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl-base.Owl_types_ndarray_algodiff.Sig.Linalg)

                                                            Module Sig.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Mat/index.html b/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Mat/index.html index 876cfb1a3..5af97ff78 100644 --- a/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Mat/index.html +++ b/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types_ndarray_algodiff.Sig.Mat)

                                                            Module Sig.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl-base.Owl_types_ndarray_algodiff.Sig.Mat)

                                                            Module Sig.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Scalar/index.html b/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Scalar/index.html index 5ac7d00ad..4669bc4e2 100644 --- a/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Scalar/index.html +++ b/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_types_ndarray_algodiff.Sig.Scalar)

                                                            Module Sig.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl-base.Owl_types_ndarray_algodiff.Sig.Scalar)

                                                            Module Sig.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/index.html b/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/index.html index d723a8ec2..36c882c20 100644 --- a/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_ndarray_algodiff/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_types_ndarray_algodiff.Sig)

                                                            Module type Owl_types_ndarray_algodiff.Sig

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +Sig (owl-base.Owl_types_ndarray_algodiff.Sig)

                                                            Module type Owl_types_ndarray_algodiff.Sig

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types_ndarray_basic/index.html b/docs/owl-base/Owl_types_ndarray_basic/index.html index 8a677c680..0cbd54e21 100644 --- a/docs/owl-base/Owl_types_ndarray_basic/index.html +++ b/docs/owl-base/Owl_types_ndarray_basic/index.html @@ -1,2 +1,2 @@ -Owl_types_ndarray_basic (owl-base.Owl_types_ndarray_basic)

                                                            Module Owl_types_ndarray_basic

                                                            module type Sig = sig ... end
                                                            +Owl_types_ndarray_basic (owl-base.Owl_types_ndarray_basic)

                                                            Module Owl_types_ndarray_basic

                                                            module type Sig = sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_basic/module-type-Sig/index.html b/docs/owl-base/Owl_types_ndarray_basic/module-type-Sig/index.html index 101291127..a50c81611 100644 --- a/docs/owl-base/Owl_types_ndarray_basic/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_ndarray_basic/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_types_ndarray_basic.Sig)

                                                            Module type Owl_types_ndarray_basic.Sig

                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +Sig (owl-base.Owl_types_ndarray_basic.Sig)

                                                            Module type Owl_types_ndarray_basic.Sig

                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types_ndarray_compare/index.html b/docs/owl-base/Owl_types_ndarray_compare/index.html index a75b9a99d..af7a4d2d5 100644 --- a/docs/owl-base/Owl_types_ndarray_compare/index.html +++ b/docs/owl-base/Owl_types_ndarray_compare/index.html @@ -1,2 +1,2 @@ -Owl_types_ndarray_compare (owl-base.Owl_types_ndarray_compare)

                                                            Module Owl_types_ndarray_compare

                                                            module type Sig = sig ... end
                                                            +Owl_types_ndarray_compare (owl-base.Owl_types_ndarray_compare)

                                                            Module Owl_types_ndarray_compare

                                                            module type Sig = sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_compare/module-type-Sig/index.html b/docs/owl-base/Owl_types_ndarray_compare/module-type-Sig/index.html index 0d0b6fcb3..1216a0159 100644 --- a/docs/owl-base/Owl_types_ndarray_compare/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_ndarray_compare/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_types_ndarray_compare.Sig)

                                                            Module type Owl_types_ndarray_compare.Sig

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +Sig (owl-base.Owl_types_ndarray_compare.Sig)

                                                            Module type Owl_types_ndarray_compare.Sig

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types_ndarray_eltcmp/index.html b/docs/owl-base/Owl_types_ndarray_eltcmp/index.html index 2bb7b692e..23a4d057e 100644 --- a/docs/owl-base/Owl_types_ndarray_eltcmp/index.html +++ b/docs/owl-base/Owl_types_ndarray_eltcmp/index.html @@ -1,2 +1,2 @@ -Owl_types_ndarray_eltcmp (owl-base.Owl_types_ndarray_eltcmp)

                                                            Module Owl_types_ndarray_eltcmp

                                                            module type Sig = sig ... end
                                                            +Owl_types_ndarray_eltcmp (owl-base.Owl_types_ndarray_eltcmp)

                                                            Module Owl_types_ndarray_eltcmp

                                                            module type Sig = sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_eltcmp/module-type-Sig/index.html b/docs/owl-base/Owl_types_ndarray_eltcmp/module-type-Sig/index.html index 696a50bd2..ab9d96c43 100644 --- a/docs/owl-base/Owl_types_ndarray_eltcmp/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_ndarray_eltcmp/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_types_ndarray_eltcmp.Sig)

                                                            Module type Owl_types_ndarray_eltcmp.Sig

                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +Sig (owl-base.Owl_types_ndarray_eltcmp.Sig)

                                                            Module type Owl_types_ndarray_eltcmp.Sig

                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types_ndarray_mutable/index.html b/docs/owl-base/Owl_types_ndarray_mutable/index.html index e40df6d08..14cf1fb4a 100644 --- a/docs/owl-base/Owl_types_ndarray_mutable/index.html +++ b/docs/owl-base/Owl_types_ndarray_mutable/index.html @@ -1,2 +1,2 @@ -Owl_types_ndarray_mutable (owl-base.Owl_types_ndarray_mutable)

                                                            Module Owl_types_ndarray_mutable

                                                            module type Sig = sig ... end
                                                            +Owl_types_ndarray_mutable (owl-base.Owl_types_ndarray_mutable)

                                                            Module Owl_types_ndarray_mutable

                                                            module type Sig = sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Linalg/index.html b/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Linalg/index.html index a4ae8d535..e6becce4f 100644 --- a/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Linalg/index.html +++ b/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_types_ndarray_mutable.Sig.Linalg)

                                                            Module Sig.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl-base.Owl_types_ndarray_mutable.Sig.Linalg)

                                                            Module Sig.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Mat/index.html b/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Mat/index.html index d537e3a81..67ce1d675 100644 --- a/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Mat/index.html +++ b/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types_ndarray_mutable.Sig.Mat)

                                                            Module Sig.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl-base.Owl_types_ndarray_mutable.Sig.Mat)

                                                            Module Sig.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Scalar/index.html b/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Scalar/index.html index 49e11be66..d4c538eb3 100644 --- a/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Scalar/index.html +++ b/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_types_ndarray_mutable.Sig.Scalar)

                                                            Module Sig.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl-base.Owl_types_ndarray_mutable.Sig.Scalar)

                                                            Module Sig.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/index.html b/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/index.html index ba547dcfb..f5476ccb0 100644 --- a/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_ndarray_mutable/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_types_ndarray_mutable.Sig)

                                                            Module type Owl_types_ndarray_mutable.Sig

                                                            include Owl_types_ndarray_algodiff.Sig
                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +Sig (owl-base.Owl_types_ndarray_mutable.Sig)

                                                            Module type Owl_types_ndarray_mutable.Sig

                                                            include Owl_types_ndarray_algodiff.Sig
                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types_ndarray_numdiff/index.html b/docs/owl-base/Owl_types_ndarray_numdiff/index.html index b173b8cd0..0261f70f2 100644 --- a/docs/owl-base/Owl_types_ndarray_numdiff/index.html +++ b/docs/owl-base/Owl_types_ndarray_numdiff/index.html @@ -1,2 +1,2 @@ -Owl_types_ndarray_numdiff (owl-base.Owl_types_ndarray_numdiff)

                                                            Module Owl_types_ndarray_numdiff

                                                            module type Sig = sig ... end
                                                            +Owl_types_ndarray_numdiff (owl-base.Owl_types_ndarray_numdiff)

                                                            Module Owl_types_ndarray_numdiff

                                                            module type Sig = sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_ndarray_numdiff/module-type-Sig/index.html b/docs/owl-base/Owl_types_ndarray_numdiff/module-type-Sig/index.html index 52e89b144..f146593f5 100644 --- a/docs/owl-base/Owl_types_ndarray_numdiff/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_ndarray_numdiff/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_types_ndarray_numdiff.Sig)

                                                            Module type Owl_types_ndarray_numdiff.Sig

                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +Sig (owl-base.Owl_types_ndarray_numdiff.Sig)

                                                            Module type Owl_types_ndarray_numdiff.Sig

                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_types_operator/index.html b/docs/owl-base/Owl_types_operator/index.html index f2b478054..243cd488d 100644 --- a/docs/owl-base/Owl_types_operator/index.html +++ b/docs/owl-base/Owl_types_operator/index.html @@ -1,2 +1,2 @@ -Owl_types_operator (owl-base.Owl_types_operator)

                                                            Module Owl_types_operator

                                                            Operator definitions such as add, sub, mul, and div. This signature defines the functions need to be implemented.

                                                            module type BasicSig = sig ... end
                                                            module type ExtendSig = sig ... end
                                                            module type MatrixSig = sig ... end
                                                            module type NdarraySig = sig ... end
                                                            module type LinalgSig = sig ... end
                                                            +Owl_types_operator (owl-base.Owl_types_operator)

                                                            Module Owl_types_operator

                                                            Operator definitions such as add, sub, mul, and div. This signature defines the functions need to be implemented.

                                                            module type BasicSig = sig ... end
                                                            module type ExtendSig = sig ... end
                                                            module type MatrixSig = sig ... end
                                                            module type NdarraySig = sig ... end
                                                            module type LinalgSig = sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_operator/module-type-BasicSig/index.html b/docs/owl-base/Owl_types_operator/module-type-BasicSig/index.html index 0870b9561..5e20059ae 100644 --- a/docs/owl-base/Owl_types_operator/module-type-BasicSig/index.html +++ b/docs/owl-base/Owl_types_operator/module-type-BasicSig/index.html @@ -1,2 +1,2 @@ -BasicSig (owl-base.Owl_types_operator.BasicSig)

                                                            Module type Owl_types_operator.BasicSig

                                                            type ('a, 'b) t
                                                            val add : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val sub : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val mul : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val div : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val add_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val sub_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val mul_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val div_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val scalar_add : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                            val scalar_sub : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                            val scalar_mul : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                            val scalar_div : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                            val equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val not_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val greater : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val less : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val greater_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val less_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            +BasicSig (owl-base.Owl_types_operator.BasicSig)

                                                            Module type Owl_types_operator.BasicSig

                                                            type ('a, 'b) t
                                                            val add : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val sub : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val mul : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val div : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val add_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val sub_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val mul_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val div_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val scalar_add : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                            val scalar_sub : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                            val scalar_mul : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                            val scalar_div : 'a -> ('a, 'b) t -> ('a, 'b) t
                                                            val equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val not_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val greater : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val less : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val greater_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val less_equal : ('a, 'b) t -> ('a, 'b) t -> bool
                                                            diff --git a/docs/owl-base/Owl_types_operator/module-type-ExtendSig/index.html b/docs/owl-base/Owl_types_operator/module-type-ExtendSig/index.html index 4100aad63..9e2bf8f87 100644 --- a/docs/owl-base/Owl_types_operator/module-type-ExtendSig/index.html +++ b/docs/owl-base/Owl_types_operator/module-type-ExtendSig/index.html @@ -1,2 +1,2 @@ -ExtendSig (owl-base.Owl_types_operator.ExtendSig)

                                                            Module type Owl_types_operator.ExtendSig

                                                            type ('a, 'b) t
                                                            val equal_scalar : ('a, 'b) t -> 'a -> bool
                                                            val not_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                            val less_scalar : ('a, 'b) t -> 'a -> bool
                                                            val greater_scalar : ('a, 'b) t -> 'a -> bool
                                                            val less_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                            val greater_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                            val elt_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_not_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_less : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_greater : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_less_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_greater_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_not_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_less_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_greater_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_less_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_greater_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val fmod : (float, 'a) t -> (float, 'a) t -> (float, 'a) t
                                                            val fmod_scalar : (float, 'a) t -> float -> (float, 'a) t
                                                            val pow : (float, 'a) t -> (float, 'a) t -> (float, 'a) t
                                                            val scalar_pow : float -> (float, 'a) t -> (float, 'a) t
                                                            val pow_scalar : (float, 'a) t -> float -> (float, 'a) t
                                                            val approx_equal : ?eps:float -> ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val approx_equal_scalar : ?eps:float -> ('a, 'b) t -> 'a -> bool
                                                            val approx_elt_equal : ?eps:float -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val approx_elt_equal_scalar : ?eps:float -> ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                            val concat_vertical : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val concat_horizontal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val get_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t
                                                            val set_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val get_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t
                                                            val set_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            +ExtendSig (owl-base.Owl_types_operator.ExtendSig)

                                                            Module type Owl_types_operator.ExtendSig

                                                            type ('a, 'b) t
                                                            val equal_scalar : ('a, 'b) t -> 'a -> bool
                                                            val not_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                            val less_scalar : ('a, 'b) t -> 'a -> bool
                                                            val greater_scalar : ('a, 'b) t -> 'a -> bool
                                                            val less_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                            val greater_equal_scalar : ('a, 'b) t -> 'a -> bool
                                                            val elt_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_not_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_less : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_greater : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_less_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_greater_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val elt_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_not_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_less_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_greater_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_less_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val elt_greater_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val fmod : (float, 'a) t -> (float, 'a) t -> (float, 'a) t
                                                            val fmod_scalar : (float, 'a) t -> float -> (float, 'a) t
                                                            val pow : (float, 'a) t -> (float, 'a) t -> (float, 'a) t
                                                            val scalar_pow : float -> (float, 'a) t -> (float, 'a) t
                                                            val pow_scalar : (float, 'a) t -> float -> (float, 'a) t
                                                            val approx_equal : ?eps:float -> ('a, 'b) t -> ('a, 'b) t -> bool
                                                            val approx_equal_scalar : ?eps:float -> ('a, 'b) t -> 'a -> bool
                                                            val approx_elt_equal : ?eps:float -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val approx_elt_equal_scalar : ?eps:float -> ('a, 'b) t -> 'a -> ('a, 'b) t
                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit
                                                            val concat_vertical : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val concat_horizontal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            val get_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t
                                                            val set_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            val get_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t
                                                            val set_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t -> unit
                                                            diff --git a/docs/owl-base/Owl_types_operator/module-type-LinalgSig/index.html b/docs/owl-base/Owl_types_operator/module-type-LinalgSig/index.html index 6b0f5ee6b..663e0d105 100644 --- a/docs/owl-base/Owl_types_operator/module-type-LinalgSig/index.html +++ b/docs/owl-base/Owl_types_operator/module-type-LinalgSig/index.html @@ -1,5 +1,5 @@ -LinalgSig (owl-base.Owl_types_operator.LinalgSig)

                                                            Module type Owl_types_operator.LinalgSig

                                                            type ('a, 'b) t
                                                            val mpow : ('a, 'b) t -> float -> ('a, 'b) t
                                                            val linsolve : +LinalgSig (owl-base.Owl_types_operator.LinalgSig)

                                                            Module type Owl_types_operator.LinalgSig

                                                            type ('a, 'b) t
                                                            val mpow : ('a, 'b) t -> float -> ('a, 'b) t
                                                            val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> ('a, 'b) t -> diff --git a/docs/owl-base/Owl_types_operator/module-type-MatrixSig/index.html b/docs/owl-base/Owl_types_operator/module-type-MatrixSig/index.html index d41791f57..a6da13dc9 100644 --- a/docs/owl-base/Owl_types_operator/module-type-MatrixSig/index.html +++ b/docs/owl-base/Owl_types_operator/module-type-MatrixSig/index.html @@ -1,2 +1,2 @@ -MatrixSig (owl-base.Owl_types_operator.MatrixSig)

                                                            Module type Owl_types_operator.MatrixSig

                                                            type ('a, 'b) t
                                                            val get : ('a, 'b) t -> int -> int -> 'a
                                                            val set : ('a, 'b) t -> int -> int -> 'a -> unit
                                                            val dot : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            +MatrixSig (owl-base.Owl_types_operator.MatrixSig)

                                                            Module type Owl_types_operator.MatrixSig

                                                            type ('a, 'b) t
                                                            val get : ('a, 'b) t -> int -> int -> 'a
                                                            val set : ('a, 'b) t -> int -> int -> 'a -> unit
                                                            val dot : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
                                                            diff --git a/docs/owl-base/Owl_types_operator/module-type-NdarraySig/index.html b/docs/owl-base/Owl_types_operator/module-type-NdarraySig/index.html index 1425af6db..f7c078652 100644 --- a/docs/owl-base/Owl_types_operator/module-type-NdarraySig/index.html +++ b/docs/owl-base/Owl_types_operator/module-type-NdarraySig/index.html @@ -1,2 +1,2 @@ -NdarraySig (owl-base.Owl_types_operator.NdarraySig)

                                                            Module type Owl_types_operator.NdarraySig

                                                            type ('a, 'b) t
                                                            val get : ('a, 'b) t -> int array -> 'a
                                                            val set : ('a, 'b) t -> int array -> 'a -> unit
                                                            +NdarraySig (owl-base.Owl_types_operator.NdarraySig)

                                                            Module type Owl_types_operator.NdarraySig

                                                            type ('a, 'b) t
                                                            val get : ('a, 'b) t -> int array -> 'a
                                                            val set : ('a, 'b) t -> int array -> 'a -> unit
                                                            diff --git a/docs/owl-base/Owl_types_stats_basic/index.html b/docs/owl-base/Owl_types_stats_basic/index.html index 13ffdbc27..0464b819b 100644 --- a/docs/owl-base/Owl_types_stats_basic/index.html +++ b/docs/owl-base/Owl_types_stats_basic/index.html @@ -1,2 +1,2 @@ -Owl_types_stats_basic (owl-base.Owl_types_stats_basic)

                                                            Module Owl_types_stats_basic

                                                            +Owl_types_stats_basic (owl-base.Owl_types_stats_basic)

                                                            Module Owl_types_stats_basic

                                                            diff --git a/docs/owl-base/Owl_types_stats_dist/index.html b/docs/owl-base/Owl_types_stats_dist/index.html index 658db2215..ee0d7eb6b 100644 --- a/docs/owl-base/Owl_types_stats_dist/index.html +++ b/docs/owl-base/Owl_types_stats_dist/index.html @@ -1,2 +1,2 @@ -Owl_types_stats_dist (owl-base.Owl_types_stats_dist)

                                                            Module Owl_types_stats_dist

                                                            module type Sig = sig ... end
                                                            +Owl_types_stats_dist (owl-base.Owl_types_stats_dist)

                                                            Module Owl_types_stats_dist

                                                            module type Sig = sig ... end
                                                            diff --git a/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Linalg/index.html b/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Linalg/index.html index f1db26b92..c25c92032 100644 --- a/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Linalg/index.html +++ b/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl-base.Owl_types_stats_dist.Sig.Linalg)

                                                            Module Sig.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl-base.Owl_types_stats_dist.Sig.Linalg)

                                                            Module Sig.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Mat/index.html b/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Mat/index.html index 22e8dae47..230dde368 100644 --- a/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Mat/index.html +++ b/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl-base.Owl_types_stats_dist.Sig.Mat)

                                                            Module Sig.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl-base.Owl_types_stats_dist.Sig.Mat)

                                                            Module Sig.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Scalar/index.html b/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Scalar/index.html index b5ae19eaf..08d08e188 100644 --- a/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Scalar/index.html +++ b/docs/owl-base/Owl_types_stats_dist/module-type-Sig/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl-base.Owl_types_stats_dist.Sig.Scalar)

                                                            Module Sig.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl-base.Owl_types_stats_dist.Sig.Scalar)

                                                            Module Sig.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl-base/Owl_types_stats_dist/module-type-Sig/index.html b/docs/owl-base/Owl_types_stats_dist/module-type-Sig/index.html index 500d3de51..e282b22a2 100644 --- a/docs/owl-base/Owl_types_stats_dist/module-type-Sig/index.html +++ b/docs/owl-base/Owl_types_stats_dist/module-type-Sig/index.html @@ -1,5 +1,5 @@ -Sig (owl-base.Owl_types_stats_dist.Sig)

                                                            Module type Owl_types_stats_dist.Sig

                                                            include Owl_types_ndarray_mutable.Sig
                                                            include Owl_types_ndarray_algodiff.Sig
                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +Sig (owl-base.Owl_types_stats_dist.Sig)

                                                            Module type Owl_types_stats_dist.Sig

                                                            include Owl_types_ndarray_mutable.Sig
                                                            include Owl_types_ndarray_algodiff.Sig
                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_utils/index.html b/docs/owl-base/Owl_utils/index.html index b0890d756..9fdbc898a 100644 --- a/docs/owl-base/Owl_utils/index.html +++ b/docs/owl-base/Owl_utils/index.html @@ -1,5 +1,5 @@ -Owl_utils (owl-base.Owl_utils)

                                                            Module Owl_utils

                                                            Helper functions used in the library

                                                            include module type of struct include Owl_utils_ndarray end
                                                            val elt_to_str : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> string
                                                            val elt_of_str : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> string -> 'a
                                                            val numel : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int
                                                            val calc_stride : int array -> int array
                                                            val calc_slice : int array -> int array
                                                            val index_1d_nd : int -> int array -> int array -> unit
                                                            val index_nd_1d : int array -> int array -> int
                                                            val ind : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int -> int array
                                                            val i1d : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int array -> int
                                                            val adjust_index : int -> int -> int
                                                            val reduce_params : +Owl_utils (owl-base.Owl_utils)

                                                            Module Owl_utils

                                                            Helper functions used in the library

                                                            include module type of struct include Owl_utils_ndarray end
                                                            val elt_to_str : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> string
                                                            val elt_of_str : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> string -> 'a
                                                            val numel : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int
                                                            val calc_stride : int array -> int array
                                                            val calc_slice : int array -> int array
                                                            val index_1d_nd : int -> int array -> int array -> unit
                                                            val index_nd_1d : int array -> int array -> int
                                                            val ind : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int -> int array
                                                            val i1d : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int array -> int
                                                            val adjust_index : int -> int -> int
                                                            val reduce_params : int -> ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int * int * int * int array
                                                            val broadcastable : int array -> int array -> bool
                                                            module Stack = Owl_utils_stack
                                                            module Heap = Owl_utils_heap
                                                            module Array = Owl_utils_array
                                                            val range_fold : int -> int -> f:('a -> int -> 'b) -> init:'c -> 'd
                                                            val array_reverse : 'a array -> unit
                                                            val array_insert : 'a array -> int -> 'b -> 'c array
                                                            val get_suffix : string -> string
                                                            val count_dup : 'a list -> ('b * int) list
                                                            val array2_to_array1 : diff --git a/docs/owl-base/Owl_utils_array/index.html b/docs/owl-base/Owl_utils_array/index.html index 234461ffb..961df0cb6 100644 --- a/docs/owl-base/Owl_utils_array/index.html +++ b/docs/owl-base/Owl_utils_array/index.html @@ -1,5 +1,5 @@ -Owl_utils_array (owl-base.Owl_utils_array)

                                                            Module Owl_utils_array

                                                            Basic functions
                                                            val length : 'a array -> int

                                                            Refer to OCaml native array.

                                                            val get : 'a array -> int -> 'a

                                                            Refer to OCaml native array.

                                                            val set : 'a array -> int -> 'a -> unit

                                                            Refer to OCaml native array.

                                                            val make : int -> 'a -> 'a array

                                                            Refer to OCaml native array.

                                                            val create_float : int -> float array

                                                            Refer to OCaml native array.

                                                            val init : int -> (int -> 'a) -> 'a array

                                                            Refer to OCaml native array.

                                                            val make_matrix : int -> int -> 'a -> 'a array array

                                                            Refer to OCaml native array.

                                                            val append : 'a array -> 'a array -> 'a array

                                                            Refer to OCaml native array.

                                                            val concat : 'a array list -> 'a array

                                                            Refer to OCaml native array.

                                                            val sub : 'a array -> int -> int -> 'a array

                                                            Refer to OCaml native array.

                                                            val copy : 'a array -> 'a array

                                                            Refer to OCaml native array.

                                                            val fill : 'a array -> int -> int -> 'a -> unit

                                                            Refer to OCaml native array.

                                                            val blit : 'a array -> int -> 'a array -> int -> int -> unit

                                                            Refer to OCaml native array.

                                                            val to_list : 'a array -> 'a list

                                                            Refer to OCaml native array.

                                                            val of_list : 'a list -> 'a array

                                                            Refer to OCaml native array.

                                                            val iter : ('a -> unit) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val iteri : (int -> 'a -> unit) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val fold_left : ('a -> 'b -> 'a) -> 'a -> 'b array -> 'a

                                                            Refer to OCaml native array.

                                                            val fold_right : ('b -> 'a -> 'a) -> 'b array -> 'a -> 'a

                                                            Refer to OCaml native array.

                                                            val map2 : ('a -> 'b -> 'c) -> 'a array -> 'b array -> 'c array

                                                            Refer to OCaml native array.

                                                            val for_all : ('a -> bool) -> 'a array -> bool

                                                            Refer to OCaml native array.

                                                            val exists : ('a -> bool) -> 'a array -> bool

                                                            Refer to OCaml native array.

                                                            val mem : 'a -> 'a array -> bool

                                                            Refer to OCaml native array.

                                                            val memq : 'a -> 'a array -> bool

                                                            Refer to OCaml native array.

                                                            val min_i : ?cmp:('a -> 'a -> int) -> 'a array -> int

                                                            min_i x returns the index of minimum value in array x. If cmp is not passed in then Stdlib.compare is used as default value.

                                                            val max_i : ?cmp:('a -> 'a -> int) -> 'a array -> int

                                                            max_i x returns the index of minimum value in array x. If cmp is not passed in then Stdlib.compare is used as default value.

                                                            val argsort : ?cmp:('a -> 'a -> int) -> 'a array -> int array

                                                            argsort cmp x sorts x according to the compare function cmp and returns the corresponding indices.

                                                            val sort : ('a -> 'a -> int) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val stable_sort : ('a -> 'a -> int) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val fast_sort : ('a -> 'a -> int) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val sort_fill : ?min:int -> ?max:int -> ?fill:int -> int array -> int array

                                                            sort_fill ~min ~max ~fill x first sorts x, then expands it to an array of length max - min + 1, and fills the holes with fill. E.g., sort_fill ~min:1 ~max:5 ~fill:0 [|4;2|] x returns a new array as follows: [|1; 0; 2; 0; 4; 5|].

                                                            val unsafe_get : 'a array -> int -> 'a

                                                            Refer to OCaml native array.

                                                            val unsafe_set : 'a array -> int -> 'a -> unit

                                                            Refer to OCaml native array.

                                                            Extended functions
                                                            val (@) : 'a array -> 'a array -> 'a array

                                                            Operator of array concatenation.

                                                            val get_slice : int array -> 'a array -> 'a array

                                                            get_slice slice x returns a copy of slice of x defined by slice. The slice definition must have [|start;stop;step|] format. The value of start, stop, and step can be negative, and the boundary is inclusive.

                                                            val set_slice : int array -> 'a array -> 'a array -> unit

                                                            set_slice slice x y sets the elements in x to the corresponding value of the elements in y based on the slice definition slice. Please refer to get_slice function for the information on the format of slice definition.

                                                            val flatten : 'a array array -> 'a array

                                                            Flatten an array array into an array.

                                                            val set_n : 'a array -> int array -> 'a -> unit

                                                            TODO

                                                            val range : int -> int -> int array

                                                            TODO

                                                            val count : 'a array -> 'a -> int

                                                            TODO

                                                            val insert : 'a array -> 'a array -> int -> 'a array

                                                            TODO

                                                            val unique : 'a array -> 'a array

                                                            unique x removes the duplicates in the array x.

                                                            val merge : 'a array -> 'a array -> 'a array

                                                            merge x y merges two arrays and removes the duplicates.

                                                            val remove : 'a array -> int -> 'a array

                                                            TODO

                                                            val replace : int -> int -> 'a array -> 'a array -> 'a array

                                                            TODO

                                                            val reverse : 'a array -> unit

                                                            reverse x reverse the elements in x in place.

                                                            val mapi : (int -> 'a -> 'b) -> 'a array -> 'b array

                                                            TODO

                                                            val map : ('a -> 'b) -> 'a array -> 'b array

                                                            TODO

                                                            val iter2i : (int -> 'a -> 'b -> unit) -> 'a array -> 'b array -> unit

                                                            TODO

                                                            val iter2 : ('a -> 'b -> unit) -> 'a array -> 'b array -> unit

                                                            TODO

                                                            val iter3i : +Owl_utils_array (owl-base.Owl_utils_array)

                                                            Module Owl_utils_array

                                                            Basic functions
                                                            val length : 'a array -> int

                                                            Refer to OCaml native array.

                                                            val get : 'a array -> int -> 'a

                                                            Refer to OCaml native array.

                                                            val set : 'a array -> int -> 'a -> unit

                                                            Refer to OCaml native array.

                                                            val make : int -> 'a -> 'a array

                                                            Refer to OCaml native array.

                                                            val create_float : int -> float array

                                                            Refer to OCaml native array.

                                                            val init : int -> (int -> 'a) -> 'a array

                                                            Refer to OCaml native array.

                                                            val make_matrix : int -> int -> 'a -> 'a array array

                                                            Refer to OCaml native array.

                                                            val append : 'a array -> 'a array -> 'a array

                                                            Refer to OCaml native array.

                                                            val concat : 'a array list -> 'a array

                                                            Refer to OCaml native array.

                                                            val sub : 'a array -> int -> int -> 'a array

                                                            Refer to OCaml native array.

                                                            val copy : 'a array -> 'a array

                                                            Refer to OCaml native array.

                                                            val fill : 'a array -> int -> int -> 'a -> unit

                                                            Refer to OCaml native array.

                                                            val blit : 'a array -> int -> 'a array -> int -> int -> unit

                                                            Refer to OCaml native array.

                                                            val to_list : 'a array -> 'a list

                                                            Refer to OCaml native array.

                                                            val of_list : 'a list -> 'a array

                                                            Refer to OCaml native array.

                                                            val iter : ('a -> unit) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val iteri : (int -> 'a -> unit) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val fold_left : ('a -> 'b -> 'a) -> 'a -> 'b array -> 'a

                                                            Refer to OCaml native array.

                                                            val fold_right : ('b -> 'a -> 'a) -> 'b array -> 'a -> 'a

                                                            Refer to OCaml native array.

                                                            val map2 : ('a -> 'b -> 'c) -> 'a array -> 'b array -> 'c array

                                                            Refer to OCaml native array.

                                                            val for_all : ('a -> bool) -> 'a array -> bool

                                                            Refer to OCaml native array.

                                                            val exists : ('a -> bool) -> 'a array -> bool

                                                            Refer to OCaml native array.

                                                            val mem : 'a -> 'a array -> bool

                                                            Refer to OCaml native array.

                                                            val memq : 'a -> 'a array -> bool

                                                            Refer to OCaml native array.

                                                            val min_i : ?cmp:('a -> 'a -> int) -> 'a array -> int

                                                            min_i x returns the index of minimum value in array x. If cmp is not passed in then Stdlib.compare is used as default value.

                                                            val max_i : ?cmp:('a -> 'a -> int) -> 'a array -> int

                                                            max_i x returns the index of minimum value in array x. If cmp is not passed in then Stdlib.compare is used as default value.

                                                            val argsort : ?cmp:('a -> 'a -> int) -> 'a array -> int array

                                                            argsort cmp x sorts x according to the compare function cmp and returns the corresponding indices.

                                                            val sort : ('a -> 'a -> int) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val stable_sort : ('a -> 'a -> int) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val fast_sort : ('a -> 'a -> int) -> 'a array -> unit

                                                            Refer to OCaml native array.

                                                            val sort_fill : ?min:int -> ?max:int -> ?fill:int -> int array -> int array

                                                            sort_fill ~min ~max ~fill x first sorts x, then expands it to an array of length max - min + 1, and fills the holes with fill. E.g., sort_fill ~min:1 ~max:5 ~fill:0 [|4;2|] x returns a new array as follows: [|1; 0; 2; 0; 4; 5|].

                                                            val unsafe_get : 'a array -> int -> 'a

                                                            Refer to OCaml native array.

                                                            val unsafe_set : 'a array -> int -> 'a -> unit

                                                            Refer to OCaml native array.

                                                            Extended functions
                                                            val (@) : 'a array -> 'a array -> 'a array

                                                            Operator of array concatenation.

                                                            val get_slice : int array -> 'a array -> 'a array

                                                            get_slice slice x returns a copy of slice of x defined by slice. The slice definition must have [|start;stop;step|] format. The value of start, stop, and step can be negative, and the boundary is inclusive.

                                                            val set_slice : int array -> 'a array -> 'a array -> unit

                                                            set_slice slice x y sets the elements in x to the corresponding value of the elements in y based on the slice definition slice. Please refer to get_slice function for the information on the format of slice definition.

                                                            val flatten : 'a array array -> 'a array

                                                            Flatten an array array into an array.

                                                            val set_n : 'a array -> int array -> 'a -> unit

                                                            TODO

                                                            val range : int -> int -> int array

                                                            TODO

                                                            val count : 'a array -> 'a -> int

                                                            TODO

                                                            val insert : 'a array -> 'a array -> int -> 'a array

                                                            TODO

                                                            val unique : 'a array -> 'a array

                                                            unique x removes the duplicates in the array x.

                                                            val merge : 'a array -> 'a array -> 'a array

                                                            merge x y merges two arrays and removes the duplicates.

                                                            val remove : 'a array -> int -> 'a array

                                                            TODO

                                                            val replace : int -> int -> 'a array -> 'a array -> 'a array

                                                            TODO

                                                            val reverse : 'a array -> unit

                                                            reverse x reverse the elements in x in place.

                                                            val mapi : (int -> 'a -> 'b) -> 'a array -> 'b array

                                                            TODO

                                                            val map : ('a -> 'b) -> 'a array -> 'b array

                                                            TODO

                                                            val iter2i : (int -> 'a -> 'b -> unit) -> 'a array -> 'b array -> unit

                                                            TODO

                                                            val iter2 : ('a -> 'b -> unit) -> 'a array -> 'b array -> unit

                                                            TODO

                                                            val iter3i : (int -> 'a -> 'b -> 'c -> unit) -> 'a array -> 'b array -> diff --git a/docs/owl-base/Owl_utils_heap/index.html b/docs/owl-base/Owl_utils_heap/index.html index b149a5147..1f34a76e1 100644 --- a/docs/owl-base/Owl_utils_heap/index.html +++ b/docs/owl-base/Owl_utils_heap/index.html @@ -1,2 +1,2 @@ -Owl_utils_heap (owl-base.Owl_utils_heap)

                                                            Module Owl_utils_heap

                                                            Type definition
                                                            type 'a t

                                                            Type of a min heap.

                                                            Basic functions
                                                            val make : ('a -> 'a -> int) -> 'a t

                                                            make cmp creates an empty min heap, using cmp as a comparison function.

                                                            val make_int : ?initial_size:int -> (int -> int -> int) -> int t

                                                            make_int ?initial_size cmp creates an empty integer heap, using cmp as a comparison function and pre-allocates a space of initial_size elements.

                                                            val make_float : ?initial_size:int -> (float -> float -> int) -> float t

                                                            make_float ?initial_size cmp creates an empty float heap, using cmp as a comparison function and pre-allocates a space of initial_size elements.

                                                            val size : 'a t -> int

                                                            size heap returns the number of elements in the heap.

                                                            val push : 'a t -> 'a -> unit

                                                            push heap x pushes x into heap. Time complexity is O(log(n)), where n is the size of heap.

                                                            val pop : 'a t -> 'a

                                                            pop heap pops the minimal element from heap. It raises an exception if the heap is empty. Time complexity is O(log(n)), where n is the size of heap.

                                                            val peek : 'a t -> 'a

                                                            peek heap returns the value of the minimal element in heap but it does not remove the element from the heap. Raises an exception if the heap is empty. Time complexity is O(1).

                                                            val is_empty : 'a t -> bool

                                                            is_empty heap returns true if heap is empty, false otherwise.

                                                            val to_array : 'a t -> 'a array

                                                            to_array heap returns the elements in heap into an (unsorted) array.

                                                            +Owl_utils_heap (owl-base.Owl_utils_heap)

                                                            Module Owl_utils_heap

                                                            Type definition
                                                            type 'a t

                                                            Type of a min heap.

                                                            Basic functions
                                                            val make : ('a -> 'a -> int) -> 'a t

                                                            make cmp creates an empty min heap, using cmp as a comparison function.

                                                            val make_int : ?initial_size:int -> (int -> int -> int) -> int t

                                                            make_int ?initial_size cmp creates an empty integer heap, using cmp as a comparison function and pre-allocates a space of initial_size elements.

                                                            val make_float : ?initial_size:int -> (float -> float -> int) -> float t

                                                            make_float ?initial_size cmp creates an empty float heap, using cmp as a comparison function and pre-allocates a space of initial_size elements.

                                                            val size : 'a t -> int

                                                            size heap returns the number of elements in the heap.

                                                            val push : 'a t -> 'a -> unit

                                                            push heap x pushes x into heap. Time complexity is O(log(n)), where n is the size of heap.

                                                            val pop : 'a t -> 'a

                                                            pop heap pops the minimal element from heap. It raises an exception if the heap is empty. Time complexity is O(log(n)), where n is the size of heap.

                                                            val peek : 'a t -> 'a

                                                            peek heap returns the value of the minimal element in heap but it does not remove the element from the heap. Raises an exception if the heap is empty. Time complexity is O(1).

                                                            val is_empty : 'a t -> bool

                                                            is_empty heap returns true if heap is empty, false otherwise.

                                                            val to_array : 'a t -> 'a array

                                                            to_array heap returns the elements in heap into an (unsorted) array.

                                                            diff --git a/docs/owl-base/Owl_utils_infer_shape/index.html b/docs/owl-base/Owl_utils_infer_shape/index.html index 949ea4f81..7671e8e01 100644 --- a/docs/owl-base/Owl_utils_infer_shape/index.html +++ b/docs/owl-base/Owl_utils_infer_shape/index.html @@ -1,5 +1,5 @@ -Owl_utils_infer_shape (owl-base.Owl_utils_infer_shape)

                                                            Module Owl_utils_infer_shape

                                                            val require_broadcasting : int array -> int array -> bool
                                                            val calc_conv2d_output_shape : +Owl_utils_infer_shape (owl-base.Owl_utils_infer_shape)

                                                            Module Owl_utils_infer_shape

                                                            val require_broadcasting : int array -> int array -> bool
                                                            val calc_conv2d_output_shape : Owl_types.padding -> int -> int -> diff --git a/docs/owl-base/Owl_utils_multimap/Make/index.html b/docs/owl-base/Owl_utils_multimap/Make/index.html index 70648ac4d..f150d0fce 100644 --- a/docs/owl-base/Owl_utils_multimap/Make/index.html +++ b/docs/owl-base/Owl_utils_multimap/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_utils_multimap.Make)

                                                            Module Owl_utils_multimap.Make

                                                            Parameters

                                                            module Ord : Stdlib.Map.OrderedType

                                                            Signature

                                                            Type definition
                                                            type key = Ord.t

                                                            Type of the multimap keys.

                                                            type 'a t

                                                            Type of a multimap.

                                                            Basic functions
                                                            val empty : 'a t

                                                            The empty multimap.

                                                            val is_empty : 'a t -> bool

                                                            Check whether the multimap is empty.

                                                            val mem : key -> 'a t -> bool

                                                            mem k m returns true is the multimap m contains at least one binding for k, false otherwise.

                                                            val add : key -> 'a -> 'a t -> 'a t

                                                            add k v m returns a multimap containing the same bindings as m, plus a binding from k to v. Previous bindings for k are hidden by the new binding (they can be restored by calling remove k m).

                                                            val remove : key -> 'a t -> 'a t

                                                            remove k v m returns a multimap with the same bindings as m, except for the binding of k: the last value that was bound to it is removed. If there is no binding for k in m, raises `Not_found`.

                                                            val find : key -> 'a t -> 'a

                                                            find k m returns the last added binding of k in m, or raises Not_found if there is no such binding.

                                                            val max_binding : 'a t -> key * 'a

                                                            max_binding m returns the greatest binding in m. Raises Not_found if m is empty.

                                                            val find_first_opt : (key -> bool) -> 'a t -> (key * 'a) option

                                                            find_first_opt f m returns the first binding (k, v) such that f k, or None if no such binding exists. The function f has to be nondecreasing. Time complexity is O(log n).

                                                            +Make (owl-base.Owl_utils_multimap.Make)

                                                            Module Owl_utils_multimap.Make

                                                            Parameters

                                                            module Ord : Stdlib.Map.OrderedType

                                                            Signature

                                                            Type definition
                                                            type key = Ord.t

                                                            Type of the multimap keys.

                                                            type 'a t

                                                            Type of a multimap.

                                                            Basic functions
                                                            val empty : 'a t

                                                            The empty multimap.

                                                            val is_empty : 'a t -> bool

                                                            Check whether the multimap is empty.

                                                            val mem : key -> 'a t -> bool

                                                            mem k m returns true is the multimap m contains at least one binding for k, false otherwise.

                                                            val add : key -> 'a -> 'a t -> 'a t

                                                            add k v m returns a multimap containing the same bindings as m, plus a binding from k to v. Previous bindings for k are hidden by the new binding (they can be restored by calling remove k m).

                                                            val remove : key -> 'a t -> 'a t

                                                            remove k v m returns a multimap with the same bindings as m, except for the binding of k: the last value that was bound to it is removed. If there is no binding for k in m, raises `Not_found`.

                                                            val find : key -> 'a t -> 'a

                                                            find k m returns the last added binding of k in m, or raises Not_found if there is no such binding.

                                                            val max_binding : 'a t -> key * 'a

                                                            max_binding m returns the greatest binding in m. Raises Not_found if m is empty.

                                                            val find_first_opt : (key -> bool) -> 'a t -> (key * 'a) option

                                                            find_first_opt f m returns the first binding (k, v) such that f k, or None if no such binding exists. The function f has to be nondecreasing. Time complexity is O(log n).

                                                            diff --git a/docs/owl-base/Owl_utils_multimap/index.html b/docs/owl-base/Owl_utils_multimap/index.html index 34a3e4a05..595e2abc0 100644 --- a/docs/owl-base/Owl_utils_multimap/index.html +++ b/docs/owl-base/Owl_utils_multimap/index.html @@ -1,2 +1,2 @@ -Owl_utils_multimap (owl-base.Owl_utils_multimap)

                                                            Module Owl_utils_multimap

                                                            module Make (Ord : Stdlib.Map.OrderedType) : sig ... end
                                                            +Owl_utils_multimap (owl-base.Owl_utils_multimap)

                                                            Module Owl_utils_multimap

                                                            module Make (Ord : Stdlib.Map.OrderedType) : sig ... end
                                                            diff --git a/docs/owl-base/Owl_utils_ndarray/index.html b/docs/owl-base/Owl_utils_ndarray/index.html index f3bab2ebd..088f726d1 100644 --- a/docs/owl-base/Owl_utils_ndarray/index.html +++ b/docs/owl-base/Owl_utils_ndarray/index.html @@ -1,5 +1,5 @@ -Owl_utils_ndarray (owl-base.Owl_utils_ndarray)

                                                            Module Owl_utils_ndarray

                                                            val elt_to_str : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> string
                                                            val elt_of_str : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> string -> 'a
                                                            val numel : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int
                                                            val calc_stride : int array -> int array
                                                            val calc_slice : int array -> int array
                                                            val index_1d_nd : int -> int array -> int array -> unit
                                                            val index_nd_1d : int array -> int array -> int
                                                            val ind : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int -> int array
                                                            val i1d : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int array -> int
                                                            val adjust_index : int -> int -> int
                                                            val reduce_params : +Owl_utils_ndarray (owl-base.Owl_utils_ndarray)

                                                            Module Owl_utils_ndarray

                                                            val elt_to_str : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> 'a -> string
                                                            val elt_of_str : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> string -> 'a
                                                            val numel : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int
                                                            val calc_stride : int array -> int array
                                                            val calc_slice : int array -> int array
                                                            val index_1d_nd : int -> int array -> int array -> unit
                                                            val index_nd_1d : int array -> int array -> int
                                                            val ind : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int -> int array
                                                            val i1d : ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int array -> int
                                                            val adjust_index : int -> int -> int
                                                            val reduce_params : int -> ('a, 'b, 'c) Stdlib.Bigarray.Genarray.t -> int * int * int * int array
                                                            val broadcastable : int array -> int array -> bool
                                                            diff --git a/docs/owl-base/Owl_utils_stack/index.html b/docs/owl-base/Owl_utils_stack/index.html index 2e236e4d7..b868926d2 100644 --- a/docs/owl-base/Owl_utils_stack/index.html +++ b/docs/owl-base/Owl_utils_stack/index.html @@ -1,2 +1,2 @@ -Owl_utils_stack (owl-base.Owl_utils_stack)

                                                            Module Owl_utils_stack

                                                            Type definition
                                                            type 'a t

                                                            Type of a stack.

                                                            Basic functions
                                                            val make : unit -> 'a t

                                                            make () creates an empty stack.

                                                            val push : 'a t -> 'a -> unit

                                                            push stack x pushes x into stack.

                                                            val pop : 'a t -> 'a option

                                                            pop stack pops the top element in stack. It returns None if the stack is empty.

                                                            val peek : 'a t -> 'a option

                                                            peek stack returns the value of top element in stack but it does not remove the element from the stack. None is returned if the stack is empty.

                                                            val is_empty : 'a t -> bool

                                                            Returns true if the stack is empty, otherwise false.

                                                            val mem : 'a t -> 'a -> bool

                                                            mem stack x checks whether x exist in stack. The complexity is O(n) where n is the size of the stack.

                                                            val memq : 'a t -> 'a -> bool

                                                            Similar to mem but physical equality is used for comparing values.

                                                            val to_array : 'a t -> 'a array

                                                            to_array stack converts the elements in stack into an array.

                                                            +Owl_utils_stack (owl-base.Owl_utils_stack)

                                                            Module Owl_utils_stack

                                                            Type definition
                                                            type 'a t

                                                            Type of a stack.

                                                            Basic functions
                                                            val make : unit -> 'a t

                                                            make () creates an empty stack.

                                                            val push : 'a t -> 'a -> unit

                                                            push stack x pushes x into stack.

                                                            val pop : 'a t -> 'a option

                                                            pop stack pops the top element in stack. It returns None if the stack is empty.

                                                            val peek : 'a t -> 'a option

                                                            peek stack returns the value of top element in stack but it does not remove the element from the stack. None is returned if the stack is empty.

                                                            val is_empty : 'a t -> bool

                                                            Returns true if the stack is empty, otherwise false.

                                                            val mem : 'a t -> 'a -> bool

                                                            mem stack x checks whether x exist in stack. The complexity is O(n) where n is the size of the stack.

                                                            val memq : 'a t -> 'a -> bool

                                                            Similar to mem but physical equality is used for comparing values.

                                                            val to_array : 'a t -> 'a array

                                                            to_array stack converts the elements in stack into an array.

                                                            diff --git a/docs/owl-base/Owl_view/Make/argument-1-A/index.html b/docs/owl-base/Owl_view/Make/argument-1-A/index.html index 839c13d81..caa15dd26 100644 --- a/docs/owl-base/Owl_view/Make/argument-1-A/index.html +++ b/docs/owl-base/Owl_view/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl-base.Owl_view.Make.A)

                                                            Parameter Make.A

                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl-base.Owl_view.Make.A)

                                                            Parameter Make.A

                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl-base/Owl_view/Make/index.html b/docs/owl-base/Owl_view/Make/index.html index d11e02dbf..2284d39a7 100644 --- a/docs/owl-base/Owl_view/Make/index.html +++ b/docs/owl-base/Owl_view/Make/index.html @@ -1,2 +1,2 @@ -Make (owl-base.Owl_view.Make)

                                                            Module Owl_view.Make

                                                            Parameters

                                                            Signature

                                                            Type definition
                                                            type t

                                                            t is the abstract type to represent a view atop of an ndarray.

                                                            Conversion functions
                                                            val of_arr : A.arr -> t

                                                            of_arr x creates a view from ndarray x.

                                                            val to_arr : t -> A.arr

                                                            to_arr x creates an new ndarray based on the view x.

                                                            Manipulation functions
                                                            val get : t -> int array -> A.elt

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set : t -> int array -> A.elt -> unit

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_slice : int list list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_slice : int list list -> t -> t -> unit

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val shape : t -> int array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val num_dims : t -> int

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val nth_dim : t -> int -> int

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val numel : t -> int

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            Iteration functions
                                                            val iteri : (int -> A.elt -> unit) -> t -> unit

                                                            iteri f x iterates and applies f to every element in x. f has type f : int array -> elt -> unit, the first parameter is index. 1d indices are passed to the user function.

                                                            val iter : (A.elt -> unit) -> t -> unit

                                                            Similar to iteri, the index is not passed in.

                                                            val mapi : (int -> A.elt -> A.elt) -> t -> unit

                                                            mapi f x applies f : int array -> elt -> elt to every element in x, then save the result in place. 1d indices are passed to the user function.

                                                            val map : (A.elt -> A.elt) -> t -> unit

                                                            map f x applies f : elt -> elt to every element in x, then save the the result in place in x.

                                                            val iter2 : (A.elt -> A.elt -> unit) -> t -> t -> unit

                                                            iter2 f x y applies f : elt -> elt -> elt every pair of elements in x and y. The indices are not passed in the user function.

                                                            val map2 : (A.elt -> A.elt -> A.elt) -> t -> t -> unit

                                                            map2 f x y applies f : elt -> elt -> elt every pair of elements in x and y, then saves the result in y. So be careful with the order, it matters, the data reflected by view y will be modified.

                                                            val iteri_nd : (int array -> A.elt -> unit) -> t -> unit

                                                            Similar to `iteri` but n-d indices are passed in. This function is much slower than `iteri`.

                                                            val mapi_nd : (int array -> A.elt -> A.elt) -> t -> unit

                                                            Similar to `mapi` but n-d indices are passed in. This function is much slower than `mapi`.

                                                            Examination & Comparison
                                                            val exists : (A.elt -> bool) -> t -> bool

                                                            exists f x checks all the elements in x using f. If at least one element satisfies f then the function returns true otherwise false.

                                                            val not_exists : (A.elt -> bool) -> t -> bool

                                                            not_exists f x checks all the elements in x, the function returns true only if all the elements fail to satisfy f : float -> bool.

                                                            val for_all : (A.elt -> bool) -> t -> bool

                                                            for_all f x checks all the elements in x, the function returns true if and only if all the elements pass the check of function f.

                                                            val equal : t -> t -> bool

                                                            equal x y returns true if x and y are elementwise equal.

                                                            val not_equal : t -> t -> bool

                                                            not_equal x y returns true if x and y are not elementwise equal.

                                                            +Make (owl-base.Owl_view.Make)

                                                            Module Owl_view.Make

                                                            Parameters

                                                            Signature

                                                            Type definition
                                                            type t

                                                            t is the abstract type to represent a view atop of an ndarray.

                                                            Conversion functions
                                                            val of_arr : A.arr -> t

                                                            of_arr x creates a view from ndarray x.

                                                            val to_arr : t -> A.arr

                                                            to_arr x creates an new ndarray based on the view x.

                                                            Manipulation functions
                                                            val get : t -> int array -> A.elt

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set : t -> int array -> A.elt -> unit

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_slice : int list list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_slice : int list list -> t -> t -> unit

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val shape : t -> int array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val num_dims : t -> int

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val nth_dim : t -> int -> int

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val numel : t -> int

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            Iteration functions
                                                            val iteri : (int -> A.elt -> unit) -> t -> unit

                                                            iteri f x iterates and applies f to every element in x. f has type f : int array -> elt -> unit, the first parameter is index. 1d indices are passed to the user function.

                                                            val iter : (A.elt -> unit) -> t -> unit

                                                            Similar to iteri, the index is not passed in.

                                                            val mapi : (int -> A.elt -> A.elt) -> t -> unit

                                                            mapi f x applies f : int array -> elt -> elt to every element in x, then save the result in place. 1d indices are passed to the user function.

                                                            val map : (A.elt -> A.elt) -> t -> unit

                                                            map f x applies f : elt -> elt to every element in x, then save the the result in place in x.

                                                            val iter2 : (A.elt -> A.elt -> unit) -> t -> t -> unit

                                                            iter2 f x y applies f : elt -> elt -> elt every pair of elements in x and y. The indices are not passed in the user function.

                                                            val map2 : (A.elt -> A.elt -> A.elt) -> t -> t -> unit

                                                            map2 f x y applies f : elt -> elt -> elt every pair of elements in x and y, then saves the result in y. So be careful with the order, it matters, the data reflected by view y will be modified.

                                                            val iteri_nd : (int array -> A.elt -> unit) -> t -> unit

                                                            Similar to `iteri` but n-d indices are passed in. This function is much slower than `iteri`.

                                                            val mapi_nd : (int array -> A.elt -> A.elt) -> t -> unit

                                                            Similar to `mapi` but n-d indices are passed in. This function is much slower than `mapi`.

                                                            Examination & Comparison
                                                            val exists : (A.elt -> bool) -> t -> bool

                                                            exists f x checks all the elements in x using f. If at least one element satisfies f then the function returns true otherwise false.

                                                            val not_exists : (A.elt -> bool) -> t -> bool

                                                            not_exists f x checks all the elements in x, the function returns true only if all the elements fail to satisfy f : float -> bool.

                                                            val for_all : (A.elt -> bool) -> t -> bool

                                                            for_all f x checks all the elements in x, the function returns true if and only if all the elements pass the check of function f.

                                                            val equal : t -> t -> bool

                                                            equal x y returns true if x and y are elementwise equal.

                                                            val not_equal : t -> t -> bool

                                                            not_equal x y returns true if x and y are not elementwise equal.

                                                            diff --git a/docs/owl-base/Owl_view/index.html b/docs/owl-base/Owl_view/index.html index 4a31981b8..2e9b4aea6 100644 --- a/docs/owl-base/Owl_view/index.html +++ b/docs/owl-base/Owl_view/index.html @@ -1,2 +1,2 @@ -Owl_view (owl-base.Owl_view)

                                                            Module Owl_view

                                                            View module This module is used to create views atop of an ndarray. The view creation is very light-weighted and avoids copying actual data. You can further create views atop of existing views using slicing functions.

                                                            All the views share the same underlying ndarray and any modification will be reflected on the original ndarray.

                                                            module Make (A : Owl_types.Ndarray_Basic) : sig ... end
                                                            +Owl_view (owl-base.Owl_view)

                                                            Module Owl_view

                                                            View module This module is used to create views atop of an ndarray. The view creation is very light-weighted and avoids copying actual data. You can further create views atop of existing views using slicing functions.

                                                            All the views share the same underlying ndarray and any modification will be reflected on the original ndarray.

                                                            module Make (A : Owl_types.Ndarray_Basic) : sig ... end
                                                            diff --git a/docs/owl-base/index.html b/docs/owl-base/index.html index 37d004af1..07ad00ef1 100644 --- a/docs/owl-base/index.html +++ b/docs/owl-base/index.html @@ -1,2 +1,2 @@ -index (owl-base.index)

                                                            owl-base index

                                                            Library owl-base

                                                            This library exposes the following toplevel modules:

                                                            +index (owl-base.index)

                                                            owl-base index

                                                            Library owl-base

                                                            This library exposes the following toplevel modules:

                                                            diff --git a/docs/owl-top/Owl_top/index.html b/docs/owl-top/Owl_top/index.html index f4dfee73d..13769972f 100644 --- a/docs/owl-top/Owl_top/index.html +++ b/docs/owl-top/Owl_top/index.html @@ -1,2 +1,2 @@ -Owl_top (owl-top.Owl_top)

                                                            Module Owl_top

                                                            Core functions
                                                            val printers : string list

                                                            List of registered pretty printers for Owl.

                                                            val install_printers : string list -> unit

                                                            Install all the registered pretty printers.

                                                            +Owl_top (owl-top.Owl_top)

                                                            Module Owl_top

                                                            Core functions
                                                            val printers : string list

                                                            List of registered pretty printers for Owl.

                                                            val install_printers : string list -> unit

                                                            Install all the registered pretty printers.

                                                            diff --git a/docs/owl-top/index.html b/docs/owl-top/index.html index 6e20a0b60..2acf4946d 100644 --- a/docs/owl-top/index.html +++ b/docs/owl-top/index.html @@ -1,2 +1,2 @@ -index (owl-top.index)

                                                            owl-top index

                                                            Library owl-top

                                                            The entry point of this library is the module: Owl_top.

                                                            +index (owl-top.index)

                                                            owl-top index

                                                            Library owl-top

                                                            The entry point of this library is the module: Owl_top.

                                                            diff --git a/docs/owl/Owl/Arr/index.html b/docs/owl/Owl/Arr/index.html index 4367c99d8..e80f20f74 100644 --- a/docs/owl/Owl/Arr/index.html +++ b/docs/owl/Owl/Arr/index.html @@ -1,5 +1,5 @@ -Arr (owl.Owl.Arr)

                                                            Module Owl.Arr

                                                            include module type of struct include Owl_dense.Ndarray.D end
                                                            include module type of struct include Owl_dense_ndarray_d end
                                                            type elt = float
                                                            type arr = +Arr (owl.Owl.Arr)

                                                            Module Owl.Arr

                                                            include module type of struct include Owl_dense.Ndarray.D end
                                                            include module type of struct include Owl_dense_ndarray_d end
                                                            type elt = float
                                                            type arr = (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            include Owl_dense_ndarray_intf.Common with type elt := elt and type arr := arr
                                                            include Owl_base_dense_ndarray_intf.Common with type elt := elt diff --git a/docs/owl/Owl/Mat/index.html b/docs/owl/Owl/Mat/index.html index 4a36e09bb..9a867414e 100644 --- a/docs/owl/Owl/Mat/index.html +++ b/docs/owl/Owl/Mat/index.html @@ -1,5 +1,5 @@ -Mat (owl.Owl.Mat)

                                                            Module Owl.Mat

                                                            include module type of struct include Owl_dense.Matrix.D end
                                                            include module type of struct include Owl_dense_matrix_d end
                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float64_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : +Mat (owl.Owl.Mat)

                                                            Module Owl.Mat

                                                            include module type of struct include Owl_dense.Matrix.D end
                                                            include module type of struct include Owl_dense_matrix_d end
                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float64_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            Iterate elements, columns, and rows.
                                                            val iteri : (int -> elt -> unit) -> mat -> unit
                                                            val iter : (elt -> unit) -> mat -> unit
                                                            val mapi : (int -> elt -> elt) -> mat -> mat
                                                            val map : (elt -> elt) -> mat -> mat
                                                            val foldi : ?axis:int -> (int -> elt -> elt -> elt) -> elt -> mat -> mat
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> mat -> mat
                                                            val scani : ?axis:int -> (int -> elt -> elt -> elt) -> mat -> mat
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> mat -> mat
                                                            val filteri : (int -> elt -> bool) -> mat -> int array
                                                            val filter : (elt -> bool) -> mat -> int array
                                                            val iteri_2d : (int -> int -> elt -> unit) -> mat -> unit
                                                            val mapi_2d : (int -> int -> elt -> elt) -> mat -> mat
                                                            val foldi_2d : diff --git a/docs/owl/Owl/index.html b/docs/owl/Owl/index.html index 4ae0c900f..c730421b0 100644 --- a/docs/owl/Owl/index.html +++ b/docs/owl/Owl/index.html @@ -1,5 +1,5 @@ -Owl (owl.Owl)

                                                            Module Owl

                                                            include module type of struct include Owl_types end
                                                            include module type of struct include Owl_types_common end
                                                            type number = Owl_types_common.number =
                                                            1. | F32
                                                            2. | F64
                                                            3. | C32
                                                            4. | C64
                                                            type ('a, 'b) owl_arr = +Owl (owl.Owl)

                                                            Module Owl

                                                            include module type of struct include Owl_types end
                                                            include module type of struct include Owl_types_common end
                                                            type number = Owl_types_common.number =
                                                            1. | F32
                                                            2. | F64
                                                            3. | C32
                                                            4. | C64
                                                            type ('a, 'b) owl_arr = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            type index = Owl_types_common.index =
                                                            1. | I of int
                                                            2. | L of int list
                                                            3. | R of int list
                                                            type slice = index list
                                                            type index_ = Owl_types_common.index_ =
                                                            1. | I_ of int
                                                            2. | L_ of int array
                                                            3. | R_ of int array
                                                            type slice_ = index_ array
                                                            type padding = Owl_types_common.padding =
                                                            1. | SAME
                                                            2. | VALID
                                                            type device_type = Owl_types_common.device_type =
                                                            1. | CPU
                                                            2. | OpenCL
                                                            3. | CUDA
                                                            module type Ndarray_Basic = Owl_types.Ndarray_Basic
                                                            module type Ndarray_Compare = Owl_types.Ndarray_Compare
                                                            module type Ndarray_Mutable = Owl_types.Ndarray_Mutable
                                                            module type Ndarray_Algodiff = Owl_types.Ndarray_Algodiff
                                                            module type Ndarray_Numdiff = Owl_types.Ndarray_Numdiff
                                                            module type Stats_Dist = Owl_types.Stats_Dist
                                                            module type Computation_Device = Owl_types.Computation_Device
                                                            val version : string
                                                            val float32 : (float, Stdlib.Bigarray.float32_elt) Stdlib.Bigarray.kind
                                                            val float64 : (float, Stdlib.Bigarray.float64_elt) Stdlib.Bigarray.kind
                                                            val complex32 : (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) Stdlib.Bigarray.kind
                                                            val complex64 : (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) Stdlib.Bigarray.kind

                                                            Make alias of the modules in Owl for your convenience.

                                                            module Const = Owl_const
                                                            module Exception = Owl_exception
                                                            module Dense = Owl_dense
                                                            module Maths = Owl_maths
                                                            module Stats = Owl_stats
                                                            module Linalg = Owl_linalg
                                                            module Algodiff = Owl_algodiff
                                                            module Optimise = Owl_optimise
                                                            module Regression = Owl_regression
                                                            module Neural = Owl_neural
                                                            module Fft = Owl_fft
                                                            module Cluster = Owl_cluster
                                                            module Utils = Owl_utils
                                                            module Dataset = Owl_dataset
                                                            module Dataframe = Owl_dataframe
                                                            module Lazy = Owl_lazy
                                                            module Graph = Owl_graph
                                                            module Nlp = Owl_nlp
                                                            module Log = Owl_log
                                                            module Computation = Owl_computation
                                                            module Signal = Owl_signal
                                                            module Cblas = Owl_cblas
                                                            module Lapacke = Owl_lapacke
                                                            module Arr : sig ... end
                                                            module Mat : sig ... end
                                                            diff --git a/docs/owl/Owl_algodiff/D/A/Linalg/index.html b/docs/owl/Owl_algodiff/D/A/Linalg/index.html index 923fe4735..eab12a79d 100644 --- a/docs/owl/Owl_algodiff/D/A/Linalg/index.html +++ b/docs/owl/Owl_algodiff/D/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_algodiff.D.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_algodiff.D.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_algodiff/D/A/Mat/index.html b/docs/owl/Owl_algodiff/D/A/Mat/index.html index 7e98942fc..dc3a681f0 100644 --- a/docs/owl/Owl_algodiff/D/A/Mat/index.html +++ b/docs/owl/Owl_algodiff/D/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_algodiff.D.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_algodiff.D.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_algodiff/D/A/Scalar/index.html b/docs/owl/Owl_algodiff/D/A/Scalar/index.html index 8deef5006..95772340e 100644 --- a/docs/owl/Owl_algodiff/D/A/Scalar/index.html +++ b/docs/owl/Owl_algodiff/D/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_algodiff.D.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_algodiff.D.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_algodiff/D/A/index.html b/docs/owl/Owl_algodiff/D/A/index.html index 4a99d52eb..05d760d2f 100644 --- a/docs/owl/Owl_algodiff/D/A/index.html +++ b/docs/owl/Owl_algodiff/D/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_algodiff.D.A)

                                                            Module D.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_algodiff.D.A)

                                                            Module D.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_algodiff/D/Arr/index.html b/docs/owl/Owl_algodiff/D/Arr/index.html index f7da6ae7c..78a421353 100644 --- a/docs/owl/Owl_algodiff/D/Arr/index.html +++ b/docs/owl/Owl_algodiff/D/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_algodiff.D.Arr)

                                                            Module D.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_algodiff.D.Arr)

                                                            Module D.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_algodiff/D/Builder/index.html b/docs/owl/Owl_algodiff/D/Builder/index.html index d782ac69d..ba96fc0cc 100644 --- a/docs/owl/Owl_algodiff/D/Builder/index.html +++ b/docs/owl/Owl_algodiff/D/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_algodiff.D.Builder)

                                                            Module D.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_algodiff.D.Builder)

                                                            Module D.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_algodiff/D/Builder/module-type-Aiso/index.html b/docs/owl/Owl_algodiff/D/Builder/module-type-Aiso/index.html index 8e43f5aad..83dc98d92 100644 --- a/docs/owl/Owl_algodiff/D/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_algodiff/D/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_algodiff.D.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_algodiff.D.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_algodiff/D/Builder/module-type-Piso/index.html b/docs/owl/Owl_algodiff/D/Builder/module-type-Piso/index.html index 9216ea1a2..0f18e5204 100644 --- a/docs/owl/Owl_algodiff/D/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_algodiff/D/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_algodiff.D.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_algodiff.D.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_algodiff/D/Builder/module-type-Siao/index.html b/docs/owl/Owl_algodiff/D/Builder/module-type-Siao/index.html index 33195ed3c..3c7030938 100644 --- a/docs/owl/Owl_algodiff/D/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_algodiff/D/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_algodiff.D.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_algodiff.D.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_algodiff/D/Builder/module-type-Sipo/index.html b/docs/owl/Owl_algodiff/D/Builder/module-type-Sipo/index.html index 3603bab53..b000b43be 100644 --- a/docs/owl/Owl_algodiff/D/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_algodiff/D/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_algodiff.D.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_algodiff.D.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_algodiff/D/Builder/module-type-Siso/index.html b/docs/owl/Owl_algodiff/D/Builder/module-type-Siso/index.html index 022fa3cef..912f066ad 100644 --- a/docs/owl/Owl_algodiff/D/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_algodiff/D/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_algodiff.D.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_algodiff.D.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_algodiff/D/Builder/module-type-Sito/index.html b/docs/owl/Owl_algodiff/D/Builder/module-type-Sito/index.html index 6e7bb8468..1d6ad6390 100644 --- a/docs/owl/Owl_algodiff/D/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_algodiff/D/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_algodiff.D.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_algodiff.D.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_algodiff/D/Linalg/index.html b/docs/owl/Owl_algodiff/D/Linalg/index.html index 50e71be7a..5fb40c1a9 100644 --- a/docs/owl/Owl_algodiff/D/Linalg/index.html +++ b/docs/owl/Owl_algodiff/D/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_algodiff.D.Linalg)

                                                            Module D.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_algodiff.D.Linalg)

                                                            Module D.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_algodiff/D/Mat/index.html b/docs/owl/Owl_algodiff/D/Mat/index.html index e11433bfc..bdd07a00e 100644 --- a/docs/owl/Owl_algodiff/D/Mat/index.html +++ b/docs/owl/Owl_algodiff/D/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_algodiff.D.Mat)

                                                            Module D.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_algodiff.D.Mat)

                                                            Module D.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_algodiff/D/Maths/index.html b/docs/owl/Owl_algodiff/D/Maths/index.html index dad55b877..cd59bd443 100644 --- a/docs/owl/Owl_algodiff/D/Maths/index.html +++ b/docs/owl/Owl_algodiff/D/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_algodiff.D.Maths)

                                                            Module D.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_algodiff.D.Maths)

                                                            Module D.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_algodiff/D/NN/index.html b/docs/owl/Owl_algodiff/D/NN/index.html index edb73ba2b..65e0c9fba 100644 --- a/docs/owl/Owl_algodiff/D/NN/index.html +++ b/docs/owl/Owl_algodiff/D/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_algodiff.D.NN)

                                                            Module D.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_algodiff.D.NN)

                                                            Module D.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_algodiff/D/index.html b/docs/owl/Owl_algodiff/D/index.html index 02521260f..3cab0c1a4 100644 --- a/docs/owl/Owl_algodiff/D/index.html +++ b/docs/owl/Owl_algodiff/D/index.html @@ -1,2 +1,2 @@ -D (owl.Owl_algodiff.D)

                                                            Module Owl_algodiff.D

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            +D (owl.Owl_algodiff.D)

                                                            Module Owl_algodiff.D

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_algodiff/S/A/Linalg/index.html b/docs/owl/Owl_algodiff/S/A/Linalg/index.html index 5abd4fd8d..cf083d786 100644 --- a/docs/owl/Owl_algodiff/S/A/Linalg/index.html +++ b/docs/owl/Owl_algodiff/S/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_algodiff.S.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_algodiff.S.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_algodiff/S/A/Mat/index.html b/docs/owl/Owl_algodiff/S/A/Mat/index.html index 3d05a5909..588d3a6f1 100644 --- a/docs/owl/Owl_algodiff/S/A/Mat/index.html +++ b/docs/owl/Owl_algodiff/S/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_algodiff.S.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_algodiff.S.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_algodiff/S/A/Scalar/index.html b/docs/owl/Owl_algodiff/S/A/Scalar/index.html index 24755a47d..69255c8c6 100644 --- a/docs/owl/Owl_algodiff/S/A/Scalar/index.html +++ b/docs/owl/Owl_algodiff/S/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_algodiff.S.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_algodiff.S.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_algodiff/S/A/index.html b/docs/owl/Owl_algodiff/S/A/index.html index 05bfdde3b..8fa17e8f4 100644 --- a/docs/owl/Owl_algodiff/S/A/index.html +++ b/docs/owl/Owl_algodiff/S/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_algodiff.S.A)

                                                            Module S.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_algodiff.S.A)

                                                            Module S.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_algodiff/S/Arr/index.html b/docs/owl/Owl_algodiff/S/Arr/index.html index d0cf6f640..25750f861 100644 --- a/docs/owl/Owl_algodiff/S/Arr/index.html +++ b/docs/owl/Owl_algodiff/S/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_algodiff.S.Arr)

                                                            Module S.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_algodiff.S.Arr)

                                                            Module S.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_algodiff/S/Builder/index.html b/docs/owl/Owl_algodiff/S/Builder/index.html index ea0dc3222..9e7961f58 100644 --- a/docs/owl/Owl_algodiff/S/Builder/index.html +++ b/docs/owl/Owl_algodiff/S/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_algodiff.S.Builder)

                                                            Module S.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_algodiff.S.Builder)

                                                            Module S.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_algodiff/S/Builder/module-type-Aiso/index.html b/docs/owl/Owl_algodiff/S/Builder/module-type-Aiso/index.html index 95b1e4997..0e5dfbff5 100644 --- a/docs/owl/Owl_algodiff/S/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_algodiff/S/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_algodiff.S.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_algodiff.S.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_algodiff/S/Builder/module-type-Piso/index.html b/docs/owl/Owl_algodiff/S/Builder/module-type-Piso/index.html index 40b70d7f1..c605dc37e 100644 --- a/docs/owl/Owl_algodiff/S/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_algodiff/S/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_algodiff.S.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_algodiff.S.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_algodiff/S/Builder/module-type-Siao/index.html b/docs/owl/Owl_algodiff/S/Builder/module-type-Siao/index.html index 1c1379e36..d2970f665 100644 --- a/docs/owl/Owl_algodiff/S/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_algodiff/S/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_algodiff.S.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_algodiff.S.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_algodiff/S/Builder/module-type-Sipo/index.html b/docs/owl/Owl_algodiff/S/Builder/module-type-Sipo/index.html index 2bed88871..a8a157e07 100644 --- a/docs/owl/Owl_algodiff/S/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_algodiff/S/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_algodiff.S.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_algodiff.S.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_algodiff/S/Builder/module-type-Siso/index.html b/docs/owl/Owl_algodiff/S/Builder/module-type-Siso/index.html index 45d4213b0..d89ea4f6c 100644 --- a/docs/owl/Owl_algodiff/S/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_algodiff/S/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_algodiff.S.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_algodiff.S.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_algodiff/S/Builder/module-type-Sito/index.html b/docs/owl/Owl_algodiff/S/Builder/module-type-Sito/index.html index 23a616101..870f8c3e4 100644 --- a/docs/owl/Owl_algodiff/S/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_algodiff/S/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_algodiff.S.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_algodiff.S.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_algodiff/S/Linalg/index.html b/docs/owl/Owl_algodiff/S/Linalg/index.html index 6b688274e..7b2bf2a39 100644 --- a/docs/owl/Owl_algodiff/S/Linalg/index.html +++ b/docs/owl/Owl_algodiff/S/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_algodiff.S.Linalg)

                                                            Module S.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_algodiff.S.Linalg)

                                                            Module S.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_algodiff/S/Mat/index.html b/docs/owl/Owl_algodiff/S/Mat/index.html index abd92a20e..685b7b0aa 100644 --- a/docs/owl/Owl_algodiff/S/Mat/index.html +++ b/docs/owl/Owl_algodiff/S/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_algodiff.S.Mat)

                                                            Module S.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_algodiff.S.Mat)

                                                            Module S.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_algodiff/S/Maths/index.html b/docs/owl/Owl_algodiff/S/Maths/index.html index 23023188a..2446316cb 100644 --- a/docs/owl/Owl_algodiff/S/Maths/index.html +++ b/docs/owl/Owl_algodiff/S/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_algodiff.S.Maths)

                                                            Module S.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_algodiff.S.Maths)

                                                            Module S.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_algodiff/S/NN/index.html b/docs/owl/Owl_algodiff/S/NN/index.html index 8f516d2b1..dda3903c4 100644 --- a/docs/owl/Owl_algodiff/S/NN/index.html +++ b/docs/owl/Owl_algodiff/S/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_algodiff.S.NN)

                                                            Module S.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_algodiff.S.NN)

                                                            Module S.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_algodiff/S/index.html b/docs/owl/Owl_algodiff/S/index.html index c29955821..e4894574e 100644 --- a/docs/owl/Owl_algodiff/S/index.html +++ b/docs/owl/Owl_algodiff/S/index.html @@ -1,2 +1,2 @@ -S (owl.Owl_algodiff.S)

                                                            Module Owl_algodiff.S

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            +S (owl.Owl_algodiff.S)

                                                            Module Owl_algodiff.S

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_algodiff/index.html b/docs/owl/Owl_algodiff/index.html index 660df4e3d..3bb87e0b6 100644 --- a/docs/owl/Owl_algodiff/index.html +++ b/docs/owl/Owl_algodiff/index.html @@ -1,2 +1,2 @@ -Owl_algodiff (owl.Owl_algodiff)

                                                            Module Owl_algodiff

                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            +Owl_algodiff (owl.Owl_algodiff)

                                                            Module Owl_algodiff

                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            diff --git a/docs/owl/Owl_algodiff_primal_ops/D/Linalg/index.html b/docs/owl/Owl_algodiff_primal_ops/D/Linalg/index.html index 53601d5bc..d6047a5ae 100644 --- a/docs/owl/Owl_algodiff_primal_ops/D/Linalg/index.html +++ b/docs/owl/Owl_algodiff_primal_ops/D/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_algodiff_primal_ops.D.Linalg)

                                                            Module D.Linalg

                                                            include module type of struct include Owl_linalg.D end
                                                            include module type of struct include Owl_linalg_d end
                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_z.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +Linalg (owl.Owl_algodiff_primal_ops.D.Linalg)

                                                            Module D.Linalg

                                                            include module type of struct include Owl_linalg.D end
                                                            include module type of struct include Owl_linalg_d end
                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_z.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat := complex_mat diff --git a/docs/owl/Owl_algodiff_primal_ops/D/Mat/index.html b/docs/owl/Owl_algodiff_primal_ops/D/Mat/index.html index 8eb7abe4f..5034dae90 100644 --- a/docs/owl/Owl_algodiff_primal_ops/D/Mat/index.html +++ b/docs/owl/Owl_algodiff_primal_ops/D/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_algodiff_primal_ops.D.Mat)

                                                            Module D.Mat

                                                            +Mat (owl.Owl_algodiff_primal_ops.D.Mat)

                                                            Module D.Mat

                                                            diff --git a/docs/owl/Owl_algodiff_primal_ops/D/index.html b/docs/owl/Owl_algodiff_primal_ops/D/index.html index 17496b069..01d412ec5 100644 --- a/docs/owl/Owl_algodiff_primal_ops/D/index.html +++ b/docs/owl/Owl_algodiff_primal_ops/D/index.html @@ -1,5 +1,5 @@ -D (owl.Owl_algodiff_primal_ops.D)

                                                            Module Owl_algodiff_primal_ops.D

                                                            include module type of struct include Owl_dense_ndarray.D end
                                                            include module type of struct include Owl_dense_ndarray_d end
                                                            type elt = float
                                                            type arr = +D (owl.Owl_algodiff_primal_ops.D)

                                                            Module Owl_algodiff_primal_ops.D

                                                            include module type of struct include Owl_dense_ndarray.D end
                                                            include module type of struct include Owl_dense_ndarray_d end
                                                            type elt = float
                                                            type arr = (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            include Owl_dense_ndarray_intf.Common with type elt := elt and type arr := arr
                                                            include Owl_base_dense_ndarray_intf.Common with type elt := elt diff --git a/docs/owl/Owl_algodiff_primal_ops/S/Linalg/index.html b/docs/owl/Owl_algodiff_primal_ops/S/Linalg/index.html index 2b6bd506d..30519c6fe 100644 --- a/docs/owl/Owl_algodiff_primal_ops/S/Linalg/index.html +++ b/docs/owl/Owl_algodiff_primal_ops/S/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_algodiff_primal_ops.S.Linalg)

                                                            Module S.Linalg

                                                            include module type of struct include Owl_linalg.S end
                                                            include module type of struct include Owl_linalg_s end
                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_c.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +Linalg (owl.Owl_algodiff_primal_ops.S.Linalg)

                                                            Module S.Linalg

                                                            include module type of struct include Owl_linalg.S end
                                                            include module type of struct include Owl_linalg_s end
                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_c.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat := complex_mat diff --git a/docs/owl/Owl_algodiff_primal_ops/S/Mat/index.html b/docs/owl/Owl_algodiff_primal_ops/S/Mat/index.html index 2136bd5c3..782b2a24c 100644 --- a/docs/owl/Owl_algodiff_primal_ops/S/Mat/index.html +++ b/docs/owl/Owl_algodiff_primal_ops/S/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_algodiff_primal_ops.S.Mat)

                                                            Module S.Mat

                                                            +Mat (owl.Owl_algodiff_primal_ops.S.Mat)

                                                            Module S.Mat

                                                            diff --git a/docs/owl/Owl_algodiff_primal_ops/S/index.html b/docs/owl/Owl_algodiff_primal_ops/S/index.html index 40b5c4ae6..a9c290329 100644 --- a/docs/owl/Owl_algodiff_primal_ops/S/index.html +++ b/docs/owl/Owl_algodiff_primal_ops/S/index.html @@ -1,5 +1,5 @@ -S (owl.Owl_algodiff_primal_ops.S)

                                                            Module Owl_algodiff_primal_ops.S

                                                            include module type of struct include Owl_dense_ndarray.S end
                                                            include module type of struct include Owl_dense_ndarray_s end
                                                            type elt = float
                                                            type arr = +S (owl.Owl_algodiff_primal_ops.S)

                                                            Module Owl_algodiff_primal_ops.S

                                                            include module type of struct include Owl_dense_ndarray.S end
                                                            include module type of struct include Owl_dense_ndarray_s end
                                                            type elt = float
                                                            type arr = (float, Stdlib.Bigarray.float32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            include Owl_dense_ndarray_intf.Common with type elt := elt and type arr := arr
                                                            include Owl_base_dense_ndarray_intf.Common with type elt := elt diff --git a/docs/owl/Owl_algodiff_primal_ops/index.html b/docs/owl/Owl_algodiff_primal_ops/index.html index d24b70d6a..c4076fdfd 100644 --- a/docs/owl/Owl_algodiff_primal_ops/index.html +++ b/docs/owl/Owl_algodiff_primal_ops/index.html @@ -1,2 +1,2 @@ -Owl_algodiff_primal_ops (owl.Owl_algodiff_primal_ops)

                                                            Module Owl_algodiff_primal_ops

                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            +Owl_algodiff_primal_ops (owl.Owl_algodiff_primal_ops)

                                                            Module Owl_algodiff_primal_ops

                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            diff --git a/docs/owl/Owl_cblas/index.html b/docs/owl/Owl_cblas/index.html index 777e75854..6ccd5139b 100644 --- a/docs/owl/Owl_cblas/index.html +++ b/docs/owl/Owl_cblas/index.html @@ -1,5 +1,5 @@ -Owl_cblas (owl.Owl_cblas)

                                                            Module Owl_cblas

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            The default type is Bigarray's Genarray.

                                                            Upper or lower triangular matrix.

                                                            Side type

                                                            Level-1 BLAS: vector-vector operations
                                                            Level-2 BLAS: matrix-vector operations
                                                            val gemv : +Owl_cblas (owl.Owl_cblas)

                                                            Module Owl_cblas

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            The default type is Bigarray's Genarray.

                                                            Upper or lower triangular matrix.

                                                            Side type

                                                            Level-1 BLAS: vector-vector operations
                                                            Level-2 BLAS: matrix-vector operations
                                                            val gemv : ?trans:bool -> ?incx:int -> ?incy:int -> diff --git a/docs/owl/Owl_cblas_basic/index.html b/docs/owl/Owl_cblas_basic/index.html index 9e504c7c7..c797b1e5a 100644 --- a/docs/owl/Owl_cblas_basic/index.html +++ b/docs/owl/Owl_cblas_basic/index.html @@ -1,5 +1,5 @@ -Owl_cblas_basic (owl.Owl_cblas_basic)

                                                            Module Owl_cblas_basic

                                                            CBLAS interface: high-level interface between Owl and level-1, level-2, level-3 BLAS functions.

                                                            Please refer to: Intel Math Kernel Library in the CBLAS interface url: https://software.intel.com/en-us/mkl-developer-reference-c

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Array1.t

                                                            The default type is Bigarray's Array1.t.

                                                            type cblas_layout =
                                                            1. | CblasRowMajor
                                                            2. | CblasColMajor
                                                              (*

                                                              Layout type, Row-major or Column-major.

                                                              *)
                                                            type cblas_transpose =
                                                            1. | CblasNoTrans
                                                            2. | CblasTrans
                                                            3. | CblasConjTrans
                                                              (*

                                                              Transpose type, no transpose, transpose, or conjugate transpose.

                                                              *)
                                                            type cblas_uplo =
                                                            1. | CblasUpper
                                                            2. | CblasLower
                                                              (*

                                                              Upper or lower triangular matrix.

                                                              *)
                                                            type cblas_diag =
                                                            1. | CblasNonUnit
                                                            2. | CblasUnit
                                                              (*

                                                              Diag type, unit triangular.

                                                              *)
                                                            type cblas_side =
                                                            1. | CblasLeft
                                                            2. | CblasRight
                                                              (*

                                                              Side type

                                                              *)
                                                            Level-1 BLAS: vector-vector operations
                                                            val rotg : float -> float -> float * float * float * float

                                                            Computes the parameters for a Givens rotation.

                                                            val rotmg : +Owl_cblas_basic (owl.Owl_cblas_basic)

                                                            Module Owl_cblas_basic

                                                            CBLAS interface: high-level interface between Owl and level-1, level-2, level-3 BLAS functions.

                                                            Please refer to: Intel Math Kernel Library in the CBLAS interface url: https://software.intel.com/en-us/mkl-developer-reference-c

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Array1.t

                                                            The default type is Bigarray's Array1.t.

                                                            type cblas_layout =
                                                            1. | CblasRowMajor
                                                            2. | CblasColMajor
                                                              (*

                                                              Layout type, Row-major or Column-major.

                                                              *)
                                                            type cblas_transpose =
                                                            1. | CblasNoTrans
                                                            2. | CblasTrans
                                                            3. | CblasConjTrans
                                                              (*

                                                              Transpose type, no transpose, transpose, or conjugate transpose.

                                                              *)
                                                            type cblas_uplo =
                                                            1. | CblasUpper
                                                            2. | CblasLower
                                                              (*

                                                              Upper or lower triangular matrix.

                                                              *)
                                                            type cblas_diag =
                                                            1. | CblasNonUnit
                                                            2. | CblasUnit
                                                              (*

                                                              Diag type, unit triangular.

                                                              *)
                                                            type cblas_side =
                                                            1. | CblasLeft
                                                            2. | CblasRight
                                                              (*

                                                              Side type

                                                              *)
                                                            Level-1 BLAS: vector-vector operations
                                                            val rotg : float -> float -> float * float * float * float

                                                            Computes the parameters for a Givens rotation.

                                                            val rotmg : ('a, 'b) Stdlib.Bigarray.kind -> float -> float -> diff --git a/docs/owl/Owl_cblas_generated/index.html b/docs/owl/Owl_cblas_generated/index.html index 483ba11f0..85f9f7d5f 100644 --- a/docs/owl/Owl_cblas_generated/index.html +++ b/docs/owl/Owl_cblas_generated/index.html @@ -1,5 +1,5 @@ -Owl_cblas_generated (owl.Owl_cblas_generated)

                                                            Module Owl_cblas_generated

                                                            auto-generated cblas interface file, timestamp:1582875912

                                                            val sdsdot : +Owl_cblas_generated (owl.Owl_cblas_generated)

                                                            Module Owl_cblas_generated

                                                            auto-generated cblas interface file, timestamp:1582875912

                                                            val sdsdot : n:int -> alpha:float -> x:float Ctypes.ptr -> diff --git a/docs/owl/Owl_cluster/index.html b/docs/owl/Owl_cluster/index.html index 82a96548e..eb2204730 100644 --- a/docs/owl/Owl_cluster/index.html +++ b/docs/owl/Owl_cluster/index.html @@ -1,2 +1,2 @@ -Owl_cluster (owl.Owl_cluster)

                                                            Module Owl_cluster

                                                            module MX = Owl_dense.Matrix.D
                                                            module UT = Owl_utils

                                                            K-means clustering algorithm x is the row-based data points and c is the number of clusters.

                                                            val kmeans : MX.mat -> int -> MX.mat * int array
                                                            +Owl_cluster (owl.Owl_cluster)

                                                            Module Owl_cluster

                                                            module MX = Owl_dense.Matrix.D
                                                            module UT = Owl_utils

                                                            K-means clustering algorithm x is the row-based data points and c is the number of clusters.

                                                            val kmeans : MX.mat -> int -> MX.mat * int array
                                                            diff --git a/docs/owl/Owl_core_types/index.html b/docs/owl/Owl_core_types/index.html index 3ca278608..6bd199462 100644 --- a/docs/owl/Owl_core_types/index.html +++ b/docs/owl/Owl_core_types/index.html @@ -1,5 +1,5 @@ -Owl_core_types (owl.Owl_core_types)

                                                            Module Owl_core_types

                                                            type ('a, 'b) owl_arr = +Owl_core_types (owl.Owl_core_types)

                                                            Module Owl_core_types

                                                            type ('a, 'b) owl_arr = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            type ('a, 'b) owl_arr_op00 = int -> ('a, 'b) owl_arr -> ('a, 'b) owl_arr -> int
                                                            type ('a, 'b) owl_arr_op01 = int -> ('a, 'b) owl_arr -> int
                                                            type ('a, 'b) owl_arr_op02 = int -> ('a, 'b) owl_arr -> float
                                                            type ('a, 'b) owl_arr_op03 = int -> ('a, 'b) owl_arr -> diff --git a/docs/owl/Owl_dataset/index.html b/docs/owl/Owl_dataset/index.html index 8ea3d66c0..09d61fb27 100644 --- a/docs/owl/Owl_dataset/index.html +++ b/docs/owl/Owl_dataset/index.html @@ -1,5 +1,5 @@ -Owl_dataset (owl.Owl_dataset)

                                                            Module Owl_dataset

                                                            Dataset: easy access to various datasets

                                                            val remote_data_path : unit -> string
                                                            val local_data_path : unit -> string
                                                            val download_data : string -> unit
                                                            val download_all : unit -> unit
                                                            val draw_samples : +Owl_dataset (owl.Owl_dataset)

                                                            Module Owl_dataset

                                                            Dataset: easy access to various datasets

                                                            val remote_data_path : unit -> string
                                                            val local_data_path : unit -> string
                                                            val download_data : string -> unit
                                                            val download_all : unit -> unit
                                                            val draw_samples : ('a, 'b) Owl_dense_matrix_generic.t -> ('a, 'b) Owl_dense_matrix_generic.t -> int -> diff --git a/docs/owl/Owl_dense/index.html b/docs/owl/Owl_dense/index.html index 6185470c0..f090a0ffc 100644 --- a/docs/owl/Owl_dense/index.html +++ b/docs/owl/Owl_dense/index.html @@ -1,2 +1,2 @@ -Owl_dense (owl.Owl_dense)

                                                            Module Owl_dense

                                                            Dense data structures: matrix & ndarray

                                                            module Ndarray = Owl_dense_ndarray
                                                            module Matrix = Owl_dense_matrix
                                                            +Owl_dense (owl.Owl_dense)

                                                            Module Owl_dense

                                                            Dense data structures: matrix & ndarray

                                                            module Ndarray = Owl_dense_ndarray
                                                            module Matrix = Owl_dense_matrix
                                                            diff --git a/docs/owl/Owl_dense_matrix/C/index.html b/docs/owl/Owl_dense_matrix/C/index.html index ffab11630..f9cc137cd 100644 --- a/docs/owl/Owl_dense_matrix/C/index.html +++ b/docs/owl/Owl_dense_matrix/C/index.html @@ -1,5 +1,5 @@ -C (owl.Owl_dense_matrix.C)

                                                            Module Owl_dense_matrix.C

                                                            include module type of struct include Owl_dense_matrix_c end
                                                            type elt = Stdlib.Complex.t
                                                            type mat = +C (owl.Owl_dense_matrix.C)

                                                            Module Owl_dense_matrix.C

                                                            include module type of struct include Owl_dense_matrix_c end
                                                            type elt = Stdlib.Complex.t
                                                            type mat = (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) Owl_dense_matrix_generic.t
                                                            type cast_mat = (float, Stdlib.Bigarray.float32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) diff --git a/docs/owl/Owl_dense_matrix/D/index.html b/docs/owl/Owl_dense_matrix/D/index.html index 9820aea5b..11a0dda5a 100644 --- a/docs/owl/Owl_dense_matrix/D/index.html +++ b/docs/owl/Owl_dense_matrix/D/index.html @@ -1,5 +1,5 @@ -D (owl.Owl_dense_matrix.D)

                                                            Module Owl_dense_matrix.D

                                                            include module type of struct include Owl_dense_matrix_d end
                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float64_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : +D (owl.Owl_dense_matrix.D)

                                                            Module Owl_dense_matrix.D

                                                            include module type of struct include Owl_dense_matrix_d end
                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float64_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            Iterate elements, columns, and rows.
                                                            val iteri : (int -> elt -> unit) -> mat -> unit
                                                            val iter : (elt -> unit) -> mat -> unit
                                                            val mapi : (int -> elt -> elt) -> mat -> mat
                                                            val map : (elt -> elt) -> mat -> mat
                                                            val foldi : ?axis:int -> (int -> elt -> elt -> elt) -> elt -> mat -> mat
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> mat -> mat
                                                            val scani : ?axis:int -> (int -> elt -> elt -> elt) -> mat -> mat
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> mat -> mat
                                                            val filteri : (int -> elt -> bool) -> mat -> int array
                                                            val filter : (elt -> bool) -> mat -> int array
                                                            val iteri_2d : (int -> int -> elt -> unit) -> mat -> unit
                                                            val mapi_2d : (int -> int -> elt -> elt) -> mat -> mat
                                                            val foldi_2d : diff --git a/docs/owl/Owl_dense_matrix/Generic/index.html b/docs/owl/Owl_dense_matrix/Generic/index.html index d76384bcb..836f0bb47 100644 --- a/docs/owl/Owl_dense_matrix/Generic/index.html +++ b/docs/owl/Owl_dense_matrix/Generic/index.html @@ -1,5 +1,5 @@ -Generic (owl.Owl_dense_matrix.Generic)

                                                            Module Owl_dense_matrix.Generic

                                                            include module type of struct include Owl_dense_matrix_generic end

                                                            About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

                                                            The generic module supports operations for the following Bigarry element types: Int8_signed, Int8_unsigned, Int16_signed, Int16_unsigned, Int32, Int64, Float32, Float64, Complex32, Complex64.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            N-dimensional array type, i.e. Bigarray Genarray type.

                                                            Create matrices
                                                            val empty : ('a, 'b) Owl_dense_ndarray_generic.kind -> int -> int -> ('a, 'b) t

                                                            empty m n creates an m by n matrix without initialising the values of elements in x.

                                                            val create : +Generic (owl.Owl_dense_matrix.Generic)

                                                            Module Owl_dense_matrix.Generic

                                                            include module type of struct include Owl_dense_matrix_generic end

                                                            About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

                                                            The generic module supports operations for the following Bigarry element types: Int8_signed, Int8_unsigned, Int16_signed, Int16_unsigned, Int32, Int64, Float32, Float64, Complex32, Complex64.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            N-dimensional array type, i.e. Bigarray Genarray type.

                                                            Create matrices
                                                            val empty : ('a, 'b) Owl_dense_ndarray_generic.kind -> int -> int -> ('a, 'b) t

                                                            empty m n creates an m by n matrix without initialising the values of elements in x.

                                                            val create : ('a, 'b) Owl_dense_ndarray_generic.kind -> int -> int -> @@ -210,7 +210,7 @@ (float, Stdlib.Bigarray.float32_elt) t -> (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) t

                                                            cast_s2z x casts x from float32 to complex64.

                                                            val cast_d2c : (float, Stdlib.Bigarray.float64_elt) t -> - (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) t

                                                            cast_d2c x casts x from float64 to complex32.

                                                            In-place modification
                                                            val create_ : out:('a, 'b) t -> 'a -> unit

                                                            TODO

                                                            val uniform_ : ?a:'a -> ?b:'a -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val bernoulli_ : ?p:float -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val zeros_ : out:('a, 'b) t -> unit

                                                            TODO

                                                            val ones_ : out:('a, 'b) t -> unit

                                                            TODO

                                                            val one_hot_ : out:('a, 'b) t -> int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val sort_ : ('a, 'b) t -> unit

                                                            sort_ x performs in-place quicksort of the elelments in x.

                                                            val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            copy_ ~out src copies the data from ndarray src to destination out.

                                                            val reshape_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            TODO

                                                            val transpose_ : out:('a, 'b) t -> ?axis:int array -> ('a, 'b) t -> unit

                                                            transpose_ ~out x is similar to transpose x but the output is written to out.

                                                            val sum_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val min_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val max_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            add_ x y is similar to add function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            sub_ x y is similar to sub function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            mul_ x y is similar to mul function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            div_ x y is similar to div function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val pow_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            pow_ x y is similar to pow function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val atan2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            atan2_ x y is similar to atan2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val hypot_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            hypot_ x y is similar to hypot function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val fmod_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fmod_ x y is similar to fmod function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val min2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            min2_ x y is similar to min2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val max2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            max2_ x y is similar to max2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            add_scalar_ x y is similar to add_scalar function but the output is written to x.

                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            sub_scalar_ x y is similar to sub_scalar function but the output is written to x.

                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            mul_scalar_ x y is similar to mul_scalar function but the output is written to x.

                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            div_scalar_ x y is similar to div_scalar function but the output is written to x.

                                                            val pow_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            pow_scalar_ x y is similar to pow_scalar function but the output is written to x.

                                                            val atan2_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            atan2_scalar_ x y is similar to atan2_scalar function but the output is written to x.

                                                            val fmod_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            fmod_scalar_ x y is similar to fmod_scalar function but the output is written to x.

                                                            val scalar_add_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_add_ a x is similar to scalar_add function but the output is written to x.

                                                            val scalar_sub_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_sub_ a x is similar to scalar_sub function but the output is written to x.

                                                            val scalar_mul_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_mul_ a x is similar to scalar_mul function but the output is written to x.

                                                            val scalar_div_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_div_ a x is similar to scalar_div function but the output is written to x.

                                                            val scalar_pow_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_pow_ a x is similar to scalar_pow function but the output is written to x.

                                                            val scalar_atan2_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_atan2_ a x is similar to scalar_atan2 function but the output is written to x.

                                                            val scalar_fmod_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_fmod_ a x is similar to scalar_fmod function but the output is written to x.

                                                            val fma_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fma_ ~out x y z is similar to fma x y z function but the output is written to out.

                                                            val dot_ : + (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) t

                                                            cast_d2c x casts x from float64 to complex32.

                                                            val create_ : out:('a, 'b) t -> 'a -> unit

                                                            In-place modification

                                                            create_ ~out value initializes the matrix out with the scalar value value. The operation is performed in-place.

                                                            val uniform_ : ?a:'a -> ?b:'a -> out:('a, 'b) t -> unit

                                                            uniform_ ?a ?b ~out fills the matrix out with random values drawn from a uniform distribution over the interval [a, b\). If a and b are not provided, the default interval is [0, 1\). The operation is performed in-place.

                                                            val bernoulli_ : ?p:float -> out:('a, 'b) t -> unit

                                                            bernoulli_ ?p ~out fills the matrix out with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. The operation is performed in-place.

                                                            val zeros_ : out:('a, 'b) t -> unit

                                                            zeros_ ~out fills the matrix out with zeros. The operation is performed in-place.

                                                            val ones_ : out:('a, 'b) t -> unit

                                                            ones_ ~out fills the matrix out with ones. The operation is performed in-place.

                                                            val one_hot_ : out:('a, 'b) t -> int -> ('a, 'b) t -> unit

                                                            one_hot_ ~out depth x converts the matrix x into a one-hot encoded matrix with the specified depth, and stores the result in out. The operation is performed in-place.

                                                            val sort_ : ('a, 'b) t -> unit

                                                            sort_ x performs in-place quicksort of the elements in x. The elements are sorted in ascending order.

                                                            val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            copy_ ~out src copies the data from ndarray src to destination out. The operation is performed in-place.

                                                            val reshape_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            reshape_ ~out x reshapes the matrix x and stores the result in out. The total number of elements must remain the same. The operation is performed in-place.

                                                            val transpose_ : out:('a, 'b) t -> ?axis:int array -> ('a, 'b) t -> unit

                                                            transpose_ ~out ?axis x transposes the matrix x according to the specified axes and stores the result in out. If axis is not provided, the transpose is performed with the default axes. The operation is performed in-place.

                                                            val sum_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            sum_ ~out ~axis x computes the sum of elements along the specified axis of the matrix x and stores the result in out. The operation is performed in-place.

                                                            val min_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            min_ ~out ~axis x computes the minimum value along the specified axis of the matrix x and stores the result in out. The operation is performed in-place.

                                                            val max_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            max_ ~out ~axis x computes the maximum value along the specified axis of the matrix x and stores the result in out. The operation is performed in-place.

                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            add_ x y is similar to add function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            sub_ x y is similar to sub function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            mul_ x y is similar to mul function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            div_ x y is similar to div function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val pow_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            pow_ x y is similar to pow function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val atan2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            atan2_ x y is similar to atan2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val hypot_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            hypot_ x y is similar to hypot function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val fmod_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fmod_ x y is similar to fmod function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val min2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            min2_ x y is similar to min2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val max2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            max2_ x y is similar to max2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            add_scalar_ x y is similar to add_scalar function but the output is written to x.

                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            sub_scalar_ x y is similar to sub_scalar function but the output is written to x.

                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            mul_scalar_ x y is similar to mul_scalar function but the output is written to x.

                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            div_scalar_ x y is similar to div_scalar function but the output is written to x.

                                                            val pow_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            pow_scalar_ x y is similar to pow_scalar function but the output is written to x.

                                                            val atan2_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            atan2_scalar_ x y is similar to atan2_scalar function but the output is written to x.

                                                            val fmod_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            fmod_scalar_ x y is similar to fmod_scalar function but the output is written to x.

                                                            val scalar_add_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_add_ a x is similar to scalar_add function but the output is written to x.

                                                            val scalar_sub_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_sub_ a x is similar to scalar_sub function but the output is written to x.

                                                            val scalar_mul_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_mul_ a x is similar to scalar_mul function but the output is written to x.

                                                            val scalar_div_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_div_ a x is similar to scalar_div function but the output is written to x.

                                                            val scalar_pow_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_pow_ a x is similar to scalar_pow function but the output is written to x.

                                                            val scalar_atan2_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_atan2_ a x is similar to scalar_atan2 function but the output is written to x.

                                                            val scalar_fmod_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_fmod_ a x is similar to scalar_fmod function but the output is written to x.

                                                            val fma_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fma_ ~out x y z is similar to fma x y z function but the output is written to out.

                                                            val dot_ : ?transa:bool -> ?transb:bool -> ?alpha:'a -> diff --git a/docs/owl/Owl_dense_matrix/Operator/index.html b/docs/owl/Owl_dense_matrix/Operator/index.html index 561b1a9b0..983018ee4 100644 --- a/docs/owl/Owl_dense_matrix/Operator/index.html +++ b/docs/owl/Owl_dense_matrix/Operator/index.html @@ -1,5 +1,5 @@ -Operator (owl.Owl_dense_matrix.Operator)

                                                            Module Owl_dense_matrix.Operator

                                                            include sig ... end
                                                            val (+) : +Operator (owl.Owl_dense_matrix.Operator)

                                                            Module Owl_dense_matrix.Operator

                                                            include sig ... end
                                                            val (-) : diff --git a/docs/owl/Owl_dense_matrix/S/index.html b/docs/owl/Owl_dense_matrix/S/index.html index 2ce04350e..6d4d58f63 100644 --- a/docs/owl/Owl_dense_matrix/S/index.html +++ b/docs/owl/Owl_dense_matrix/S/index.html @@ -1,5 +1,5 @@ -S (owl.Owl_dense_matrix.S)

                                                            Module Owl_dense_matrix.S

                                                            include module type of struct include Owl_dense_matrix_s end
                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : +S (owl.Owl_dense_matrix.S)

                                                            Module Owl_dense_matrix.S

                                                            include module type of struct include Owl_dense_matrix_s end
                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            Iterate elements, columns, and rows.
                                                            val iteri : (int -> elt -> unit) -> mat -> unit
                                                            val iter : (elt -> unit) -> mat -> unit
                                                            val mapi : (int -> elt -> elt) -> mat -> mat
                                                            val map : (elt -> elt) -> mat -> mat
                                                            val foldi : ?axis:int -> (int -> elt -> elt -> elt) -> elt -> mat -> mat
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> mat -> mat
                                                            val scani : ?axis:int -> (int -> elt -> elt -> elt) -> mat -> mat
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> mat -> mat
                                                            val filteri : (int -> elt -> bool) -> mat -> int array
                                                            val filter : (elt -> bool) -> mat -> int array
                                                            val iteri_2d : (int -> int -> elt -> unit) -> mat -> unit
                                                            val mapi_2d : (int -> int -> elt -> elt) -> mat -> mat
                                                            val foldi_2d : diff --git a/docs/owl/Owl_dense_matrix/Z/index.html b/docs/owl/Owl_dense_matrix/Z/index.html index 812cf1cee..147c177a7 100644 --- a/docs/owl/Owl_dense_matrix/Z/index.html +++ b/docs/owl/Owl_dense_matrix/Z/index.html @@ -1,5 +1,5 @@ -Z (owl.Owl_dense_matrix.Z)

                                                            Module Owl_dense_matrix.Z

                                                            include module type of struct include Owl_dense_matrix_z end
                                                            type elt = Stdlib.Complex.t
                                                            type mat = +Z (owl.Owl_dense_matrix.Z)

                                                            Module Owl_dense_matrix.Z

                                                            include module type of struct include Owl_dense_matrix_z end
                                                            type elt = Stdlib.Complex.t
                                                            type mat = (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) Owl_dense_matrix_generic.t
                                                            type cast_mat = (float, Stdlib.Bigarray.float64_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) diff --git a/docs/owl/Owl_dense_matrix/index.html b/docs/owl/Owl_dense_matrix/index.html index 6754a812a..ce0bbe657 100644 --- a/docs/owl/Owl_dense_matrix/index.html +++ b/docs/owl/Owl_dense_matrix/index.html @@ -1,2 +1,2 @@ -Owl_dense_matrix (owl.Owl_dense_matrix)

                                                            Module Owl_dense_matrix

                                                            Matrix: module aliases

                                                            module Operator : sig ... end
                                                            module Generic : sig ... end
                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            module C : sig ... end
                                                            module Z : sig ... end
                                                            +Owl_dense_matrix (owl.Owl_dense_matrix)

                                                            Module Owl_dense_matrix

                                                            Matrix: module aliases

                                                            module Operator : sig ... end
                                                            module Generic : sig ... end
                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            module C : sig ... end
                                                            module Z : sig ... end
                                                            diff --git a/docs/owl/Owl_dense_matrix_c/index.html b/docs/owl/Owl_dense_matrix_c/index.html index 5a4ec671a..2593d44ad 100644 --- a/docs/owl/Owl_dense_matrix_c/index.html +++ b/docs/owl/Owl_dense_matrix_c/index.html @@ -1,5 +1,5 @@ -Owl_dense_matrix_c (owl.Owl_dense_matrix_c)

                                                            Module Owl_dense_matrix_c

                                                            Complex dense matrix module: this module supports operations on dense matrices of complex numbers. The complex number has a record type of {re = float; im = float}.

                                                            This page only contains detailed explanations for the operations specific to Dense.Complex module. Most of the other operations are the same to those in Dense.Real module, therefore please refer to the documentation of Dense.Real for more information.

                                                            type elt = Stdlib.Complex.t
                                                            type mat = +Owl_dense_matrix_c (owl.Owl_dense_matrix_c)

                                                            Module Owl_dense_matrix_c

                                                            Complex dense matrix module: this module supports operations on dense matrices of complex numbers. The complex number has a record type of {re = float; im = float}.

                                                            This page only contains detailed explanations for the operations specific to Dense.Complex module. Most of the other operations are the same to those in Dense.Real module, therefore please refer to the documentation of Dense.Real for more information.

                                                            type elt = Stdlib.Complex.t
                                                            type mat = (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) Owl_dense_matrix_generic.t
                                                            type cast_mat = (float, Stdlib.Bigarray.float32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) diff --git a/docs/owl/Owl_dense_matrix_d/index.html b/docs/owl/Owl_dense_matrix_d/index.html index 88fb50e49..74a314293 100644 --- a/docs/owl/Owl_dense_matrix_d/index.html +++ b/docs/owl/Owl_dense_matrix_d/index.html @@ -1,5 +1,5 @@ -Owl_dense_matrix_d (owl.Owl_dense_matrix_d)

                                                            Module Owl_dense_matrix_d

                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float64_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : +Owl_dense_matrix_d (owl.Owl_dense_matrix_d)

                                                            Module Owl_dense_matrix_d

                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float64_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            Iterate elements, columns, and rows.
                                                            val iteri : (int -> elt -> unit) -> mat -> unit
                                                            val iter : (elt -> unit) -> mat -> unit
                                                            val mapi : (int -> elt -> elt) -> mat -> mat
                                                            val map : (elt -> elt) -> mat -> mat
                                                            val foldi : ?axis:int -> (int -> elt -> elt -> elt) -> elt -> mat -> mat
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> mat -> mat
                                                            val scani : ?axis:int -> (int -> elt -> elt -> elt) -> mat -> mat
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> mat -> mat
                                                            val filteri : (int -> elt -> bool) -> mat -> int array
                                                            val filter : (elt -> bool) -> mat -> int array
                                                            val iteri_2d : (int -> int -> elt -> unit) -> mat -> unit
                                                            val mapi_2d : (int -> int -> elt -> elt) -> mat -> mat
                                                            val foldi_2d : diff --git a/docs/owl/Owl_dense_matrix_generic/index.html b/docs/owl/Owl_dense_matrix_generic/index.html index 117382963..4e62f91e0 100644 --- a/docs/owl/Owl_dense_matrix_generic/index.html +++ b/docs/owl/Owl_dense_matrix_generic/index.html @@ -1,5 +1,5 @@ -Owl_dense_matrix_generic (owl.Owl_dense_matrix_generic)

                                                            Module Owl_dense_matrix_generic

                                                            Matrix module: including creation, manipulation, and various vectorised mathematical operations.

                                                            About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

                                                            The generic module supports operations for the following Bigarry element types: Int8_signed, Int8_unsigned, Int16_signed, Int16_unsigned, Int32, Int64, Float32, Float64, Complex32, Complex64.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            N-dimensional array type, i.e. Bigarray Genarray type.

                                                            Create matrices
                                                            val empty : ('a, 'b) Owl_dense_ndarray_generic.kind -> int -> int -> ('a, 'b) t

                                                            empty m n creates an m by n matrix without initialising the values of elements in x.

                                                            val create : +Owl_dense_matrix_generic (owl.Owl_dense_matrix_generic)

                                                            Module Owl_dense_matrix_generic

                                                            Matrix module: including creation, manipulation, and various vectorised mathematical operations.

                                                            About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of x; in case both x and y have the same magnitudes, x is less than x if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

                                                            The generic module supports operations for the following Bigarry element types: Int8_signed, Int8_unsigned, Int16_signed, Int16_unsigned, Int32, Int64, Float32, Float64, Complex32, Complex64.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            N-dimensional array type, i.e. Bigarray Genarray type.

                                                            Create matrices
                                                            val empty : ('a, 'b) Owl_dense_ndarray_generic.kind -> int -> int -> ('a, 'b) t

                                                            empty m n creates an m by n matrix without initialising the values of elements in x.

                                                            val create : ('a, 'b) Owl_dense_ndarray_generic.kind -> int -> int -> @@ -210,7 +210,7 @@ (float, Stdlib.Bigarray.float32_elt) t -> (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) t

                                                            cast_s2z x casts x from float32 to complex64.

                                                            val cast_d2c : (float, Stdlib.Bigarray.float64_elt) t -> - (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) t

                                                            cast_d2c x casts x from float64 to complex32.

                                                            In-place modification
                                                            val create_ : out:('a, 'b) t -> 'a -> unit

                                                            TODO

                                                            val uniform_ : ?a:'a -> ?b:'a -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val bernoulli_ : ?p:float -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val zeros_ : out:('a, 'b) t -> unit

                                                            TODO

                                                            val ones_ : out:('a, 'b) t -> unit

                                                            TODO

                                                            val one_hot_ : out:('a, 'b) t -> int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val sort_ : ('a, 'b) t -> unit

                                                            sort_ x performs in-place quicksort of the elelments in x.

                                                            val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            copy_ ~out src copies the data from ndarray src to destination out.

                                                            val reshape_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            TODO

                                                            val transpose_ : out:('a, 'b) t -> ?axis:int array -> ('a, 'b) t -> unit

                                                            transpose_ ~out x is similar to transpose x but the output is written to out.

                                                            val sum_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val min_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val max_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            add_ x y is similar to add function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            sub_ x y is similar to sub function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            mul_ x y is similar to mul function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            div_ x y is similar to div function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val pow_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            pow_ x y is similar to pow function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val atan2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            atan2_ x y is similar to atan2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val hypot_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            hypot_ x y is similar to hypot function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val fmod_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fmod_ x y is similar to fmod function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val min2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            min2_ x y is similar to min2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val max2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            max2_ x y is similar to max2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            add_scalar_ x y is similar to add_scalar function but the output is written to x.

                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            sub_scalar_ x y is similar to sub_scalar function but the output is written to x.

                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            mul_scalar_ x y is similar to mul_scalar function but the output is written to x.

                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            div_scalar_ x y is similar to div_scalar function but the output is written to x.

                                                            val pow_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            pow_scalar_ x y is similar to pow_scalar function but the output is written to x.

                                                            val atan2_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            atan2_scalar_ x y is similar to atan2_scalar function but the output is written to x.

                                                            val fmod_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            fmod_scalar_ x y is similar to fmod_scalar function but the output is written to x.

                                                            val scalar_add_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_add_ a x is similar to scalar_add function but the output is written to x.

                                                            val scalar_sub_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_sub_ a x is similar to scalar_sub function but the output is written to x.

                                                            val scalar_mul_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_mul_ a x is similar to scalar_mul function but the output is written to x.

                                                            val scalar_div_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_div_ a x is similar to scalar_div function but the output is written to x.

                                                            val scalar_pow_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_pow_ a x is similar to scalar_pow function but the output is written to x.

                                                            val scalar_atan2_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_atan2_ a x is similar to scalar_atan2 function but the output is written to x.

                                                            val scalar_fmod_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_fmod_ a x is similar to scalar_fmod function but the output is written to x.

                                                            val fma_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fma_ ~out x y z is similar to fma x y z function but the output is written to out.

                                                            val dot_ : + (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) t

                                                            cast_d2c x casts x from float64 to complex32.

                                                            val create_ : out:('a, 'b) t -> 'a -> unit

                                                            In-place modification

                                                            create_ ~out value initializes the matrix out with the scalar value value. The operation is performed in-place.

                                                            val uniform_ : ?a:'a -> ?b:'a -> out:('a, 'b) t -> unit

                                                            uniform_ ?a ?b ~out fills the matrix out with random values drawn from a uniform distribution over the interval [a, b\). If a and b are not provided, the default interval is [0, 1\). The operation is performed in-place.

                                                            val bernoulli_ : ?p:float -> out:('a, 'b) t -> unit

                                                            bernoulli_ ?p ~out fills the matrix out with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5. The operation is performed in-place.

                                                            val zeros_ : out:('a, 'b) t -> unit

                                                            zeros_ ~out fills the matrix out with zeros. The operation is performed in-place.

                                                            val ones_ : out:('a, 'b) t -> unit

                                                            ones_ ~out fills the matrix out with ones. The operation is performed in-place.

                                                            val one_hot_ : out:('a, 'b) t -> int -> ('a, 'b) t -> unit

                                                            one_hot_ ~out depth x converts the matrix x into a one-hot encoded matrix with the specified depth, and stores the result in out. The operation is performed in-place.

                                                            val sort_ : ('a, 'b) t -> unit

                                                            sort_ x performs in-place quicksort of the elements in x. The elements are sorted in ascending order.

                                                            val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            copy_ ~out src copies the data from ndarray src to destination out. The operation is performed in-place.

                                                            val reshape_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            reshape_ ~out x reshapes the matrix x and stores the result in out. The total number of elements must remain the same. The operation is performed in-place.

                                                            val transpose_ : out:('a, 'b) t -> ?axis:int array -> ('a, 'b) t -> unit

                                                            transpose_ ~out ?axis x transposes the matrix x according to the specified axes and stores the result in out. If axis is not provided, the transpose is performed with the default axes. The operation is performed in-place.

                                                            val sum_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            sum_ ~out ~axis x computes the sum of elements along the specified axis of the matrix x and stores the result in out. The operation is performed in-place.

                                                            val min_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            min_ ~out ~axis x computes the minimum value along the specified axis of the matrix x and stores the result in out. The operation is performed in-place.

                                                            val max_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            max_ ~out ~axis x computes the maximum value along the specified axis of the matrix x and stores the result in out. The operation is performed in-place.

                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            add_ x y is similar to add function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            sub_ x y is similar to sub function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            mul_ x y is similar to mul function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            div_ x y is similar to div function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val pow_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            pow_ x y is similar to pow function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val atan2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            atan2_ x y is similar to atan2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val hypot_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            hypot_ x y is similar to hypot function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val fmod_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fmod_ x y is similar to fmod function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val min2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            min2_ x y is similar to min2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val max2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            max2_ x y is similar to max2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            add_scalar_ x y is similar to add_scalar function but the output is written to x.

                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            sub_scalar_ x y is similar to sub_scalar function but the output is written to x.

                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            mul_scalar_ x y is similar to mul_scalar function but the output is written to x.

                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            div_scalar_ x y is similar to div_scalar function but the output is written to x.

                                                            val pow_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            pow_scalar_ x y is similar to pow_scalar function but the output is written to x.

                                                            val atan2_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            atan2_scalar_ x y is similar to atan2_scalar function but the output is written to x.

                                                            val fmod_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            fmod_scalar_ x y is similar to fmod_scalar function but the output is written to x.

                                                            val scalar_add_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_add_ a x is similar to scalar_add function but the output is written to x.

                                                            val scalar_sub_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_sub_ a x is similar to scalar_sub function but the output is written to x.

                                                            val scalar_mul_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_mul_ a x is similar to scalar_mul function but the output is written to x.

                                                            val scalar_div_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_div_ a x is similar to scalar_div function but the output is written to x.

                                                            val scalar_pow_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_pow_ a x is similar to scalar_pow function but the output is written to x.

                                                            val scalar_atan2_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_atan2_ a x is similar to scalar_atan2 function but the output is written to x.

                                                            val scalar_fmod_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_fmod_ a x is similar to scalar_fmod function but the output is written to x.

                                                            val fma_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fma_ ~out x y z is similar to fma x y z function but the output is written to out.

                                                            val dot_ : ?transa:bool -> ?transb:bool -> ?alpha:'a -> diff --git a/docs/owl/Owl_dense_matrix_intf/index.html b/docs/owl/Owl_dense_matrix_intf/index.html index f43a1370e..bf3b91466 100644 --- a/docs/owl/Owl_dense_matrix_intf/index.html +++ b/docs/owl/Owl_dense_matrix_intf/index.html @@ -1,2 +1,2 @@ -Owl_dense_matrix_intf (owl.Owl_dense_matrix_intf)

                                                            Module Owl_dense_matrix_intf

                                                            module type Common = sig ... end
                                                            module type Real = sig ... end
                                                            module type Complex = sig ... end
                                                            +Owl_dense_matrix_intf (owl.Owl_dense_matrix_intf)

                                                            Module Owl_dense_matrix_intf

                                                            module type Common = sig ... end
                                                            module type Real = sig ... end
                                                            module type Complex = sig ... end
                                                            diff --git a/docs/owl/Owl_dense_matrix_intf/module-type-Common/index.html b/docs/owl/Owl_dense_matrix_intf/module-type-Common/index.html index 80a4723a5..f575bd51e 100644 --- a/docs/owl/Owl_dense_matrix_intf/module-type-Common/index.html +++ b/docs/owl/Owl_dense_matrix_intf/module-type-Common/index.html @@ -1,5 +1,5 @@ -Common (owl.Owl_dense_matrix_intf.Common)

                                                            Module type Owl_dense_matrix_intf.Common

                                                            type elt
                                                            type mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : +Common (owl.Owl_dense_matrix_intf.Common)

                                                            Module type Owl_dense_matrix_intf.Common

                                                            type elt
                                                            type mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            Iterate elements, columns, and rows.
                                                            val iteri : (int -> elt -> unit) -> mat -> unit
                                                            val iter : (elt -> unit) -> mat -> unit
                                                            val mapi : (int -> elt -> elt) -> mat -> mat
                                                            val map : (elt -> elt) -> mat -> mat
                                                            val foldi : ?axis:int -> (int -> elt -> elt -> elt) -> elt -> mat -> mat
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> mat -> mat
                                                            val scani : ?axis:int -> (int -> elt -> elt -> elt) -> mat -> mat
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> mat -> mat
                                                            val filteri : (int -> elt -> bool) -> mat -> int array
                                                            val filter : (elt -> bool) -> mat -> int array
                                                            val iteri_2d : (int -> int -> elt -> unit) -> mat -> unit
                                                            val mapi_2d : (int -> int -> elt -> elt) -> mat -> mat
                                                            val foldi_2d : diff --git a/docs/owl/Owl_dense_matrix_intf/module-type-Complex/index.html b/docs/owl/Owl_dense_matrix_intf/module-type-Complex/index.html index ade90f2b2..a3ef65502 100644 --- a/docs/owl/Owl_dense_matrix_intf/module-type-Complex/index.html +++ b/docs/owl/Owl_dense_matrix_intf/module-type-Complex/index.html @@ -1,2 +1,2 @@ -Complex (owl.Owl_dense_matrix_intf.Complex)

                                                            Module type Owl_dense_matrix_intf.Complex

                                                            type mat
                                                            type cast_mat
                                                            Specific complex functions
                                                            val complex : cast_mat -> cast_mat -> mat
                                                            val polar : cast_mat -> cast_mat -> mat
                                                            val re : mat -> cast_mat
                                                            val im : mat -> cast_mat
                                                            +Complex (owl.Owl_dense_matrix_intf.Complex)

                                                            Module type Owl_dense_matrix_intf.Complex

                                                            type mat
                                                            type cast_mat
                                                            Specific complex functions
                                                            val complex : cast_mat -> cast_mat -> mat
                                                            val polar : cast_mat -> cast_mat -> mat
                                                            val re : mat -> cast_mat
                                                            val im : mat -> cast_mat
                                                            diff --git a/docs/owl/Owl_dense_matrix_intf/module-type-Real/index.html b/docs/owl/Owl_dense_matrix_intf/module-type-Real/index.html index d2dc44c85..c25a7e3db 100644 --- a/docs/owl/Owl_dense_matrix_intf/module-type-Real/index.html +++ b/docs/owl/Owl_dense_matrix_intf/module-type-Real/index.html @@ -1,5 +1,5 @@ -Real (owl.Owl_dense_matrix_intf.Real)

                                                            Module type Owl_dense_matrix_intf.Real

                                                            type elt
                                                            type mat
                                                            Specific real functions
                                                            val i0 : mat -> mat
                                                            val i0e : mat -> mat
                                                            val i1 : mat -> mat
                                                            val i1e : mat -> mat
                                                            val iv : v:mat -> mat -> mat
                                                            val scalar_iv : v:elt -> mat -> mat
                                                            val iv_scalar : v:mat -> elt -> mat
                                                            val j0 : mat -> mat
                                                            val j1 : mat -> mat
                                                            val jv : v:mat -> mat -> mat
                                                            val scalar_jv : v:elt -> mat -> mat
                                                            val jv_scalar : v:mat -> elt -> mat
                                                            val semidef : int -> mat
                                                            val min_rows : mat -> (elt * int * int) array
                                                            val min_cols : mat -> (elt * int * int) array
                                                            val max_rows : mat -> (elt * int * int) array
                                                            val max_cols : mat -> (elt * int * int) array
                                                            val signum : mat -> mat
                                                            val erf : mat -> mat
                                                            val erfc : mat -> mat
                                                            val logistic : mat -> mat
                                                            val relu : mat -> mat
                                                            val elu : ?alpha:elt -> mat -> mat
                                                            val leaky_relu : ?alpha:elt -> mat -> mat
                                                            val softplus : mat -> mat
                                                            val softsign : mat -> mat
                                                            val softmax : ?axis:int -> mat -> mat
                                                            val sigmoid : mat -> mat
                                                            val log_sum_exp' : mat -> elt
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> mat -> mat
                                                            val max_pool : +Real (owl.Owl_dense_matrix_intf.Real)

                                                            Module type Owl_dense_matrix_intf.Real

                                                            type elt
                                                            type mat
                                                            Specific real functions
                                                            val i0 : mat -> mat
                                                            val i0e : mat -> mat
                                                            val i1 : mat -> mat
                                                            val i1e : mat -> mat
                                                            val iv : v:mat -> mat -> mat
                                                            val scalar_iv : v:elt -> mat -> mat
                                                            val iv_scalar : v:mat -> elt -> mat
                                                            val j0 : mat -> mat
                                                            val j1 : mat -> mat
                                                            val jv : v:mat -> mat -> mat
                                                            val scalar_jv : v:elt -> mat -> mat
                                                            val jv_scalar : v:mat -> elt -> mat
                                                            val semidef : int -> mat
                                                            val min_rows : mat -> (elt * int * int) array
                                                            val min_cols : mat -> (elt * int * int) array
                                                            val max_rows : mat -> (elt * int * int) array
                                                            val max_cols : mat -> (elt * int * int) array
                                                            val signum : mat -> mat
                                                            val erf : mat -> mat
                                                            val erfc : mat -> mat
                                                            val logistic : mat -> mat
                                                            val relu : mat -> mat
                                                            val elu : ?alpha:elt -> mat -> mat
                                                            val leaky_relu : ?alpha:elt -> mat -> mat
                                                            val softplus : mat -> mat
                                                            val softsign : mat -> mat
                                                            val softmax : ?axis:int -> mat -> mat
                                                            val sigmoid : mat -> mat
                                                            val log_sum_exp' : mat -> elt
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> mat -> mat
                                                            val max_pool : ?padding:Owl_types.padding -> mat -> int array -> diff --git a/docs/owl/Owl_dense_matrix_s/index.html b/docs/owl/Owl_dense_matrix_s/index.html index d1abb7b47..434ff1ef9 100644 --- a/docs/owl/Owl_dense_matrix_s/index.html +++ b/docs/owl/Owl_dense_matrix_s/index.html @@ -1,5 +1,5 @@ -Owl_dense_matrix_s (owl.Owl_dense_matrix_s)

                                                            Module Owl_dense_matrix_s

                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : +Owl_dense_matrix_s (owl.Owl_dense_matrix_s)

                                                            Module Owl_dense_matrix_s

                                                            type elt = float
                                                            type mat = (float, Stdlib.Bigarray.float32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            Iterate elements, columns, and rows.
                                                            val iteri : (int -> elt -> unit) -> mat -> unit
                                                            val iter : (elt -> unit) -> mat -> unit
                                                            val mapi : (int -> elt -> elt) -> mat -> mat
                                                            val map : (elt -> elt) -> mat -> mat
                                                            val foldi : ?axis:int -> (int -> elt -> elt -> elt) -> elt -> mat -> mat
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> mat -> mat
                                                            val scani : ?axis:int -> (int -> elt -> elt -> elt) -> mat -> mat
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> mat -> mat
                                                            val filteri : (int -> elt -> bool) -> mat -> int array
                                                            val filter : (elt -> bool) -> mat -> int array
                                                            val iteri_2d : (int -> int -> elt -> unit) -> mat -> unit
                                                            val mapi_2d : (int -> int -> elt -> elt) -> mat -> mat
                                                            val foldi_2d : diff --git a/docs/owl/Owl_dense_matrix_z/index.html b/docs/owl/Owl_dense_matrix_z/index.html index 5714d4808..71fb199d9 100644 --- a/docs/owl/Owl_dense_matrix_z/index.html +++ b/docs/owl/Owl_dense_matrix_z/index.html @@ -1,5 +1,5 @@ -Owl_dense_matrix_z (owl.Owl_dense_matrix_z)

                                                            Module Owl_dense_matrix_z

                                                            Complex dense matrix module: this module supports operations on dense matrices of complex numbers. The complex number has a record type of {re = float; im = float}.

                                                            This page only contains detailed explanations for the operations specific to Dense.Complex module. Most of the other operations are the same to those in Dense.Real module, therefore please refer to the documentation of Dense.Real for more information.

                                                            type elt = Stdlib.Complex.t
                                                            type mat = +Owl_dense_matrix_z (owl.Owl_dense_matrix_z)

                                                            Module Owl_dense_matrix_z

                                                            Complex dense matrix module: this module supports operations on dense matrices of complex numbers. The complex number has a record type of {re = float; im = float}.

                                                            This page only contains detailed explanations for the operations specific to Dense.Complex module. Most of the other operations are the same to those in Dense.Real module, therefore please refer to the documentation of Dense.Real for more information.

                                                            type elt = Stdlib.Complex.t
                                                            type mat = (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) Owl_dense_matrix_generic.t
                                                            type cast_mat = (float, Stdlib.Bigarray.float64_elt) Owl_dense_matrix_generic.t
                                                            include Owl_dense_matrix_intf.Common with type elt := elt and type mat := mat
                                                            Create dense matrices
                                                            val empty : int -> int -> mat
                                                            val create : int -> int -> elt -> mat
                                                            val init : int -> int -> (int -> elt) -> mat
                                                            val init_2d : int -> int -> (int -> int -> elt) -> mat
                                                            val zeros : int -> int -> mat
                                                            val ones : int -> int -> mat
                                                            val eye : int -> mat
                                                            val sequential : ?a:elt -> ?step:elt -> int -> int -> mat
                                                            val uniform : ?a:elt -> ?b:elt -> int -> int -> mat
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int -> int -> mat
                                                            val bernoulli : ?p:float -> int -> int -> mat
                                                            val unit_basis : int -> int -> mat
                                                            val diagm : ?k:int -> mat -> mat
                                                            val triu : ?k:int -> mat -> mat
                                                            val tril : ?k:int -> mat -> mat
                                                            val symmetric : ?upper:bool -> mat -> mat
                                                            val bidiagonal : ?upper:bool -> mat -> mat -> mat
                                                            val toeplitz : ?c:mat -> mat -> mat
                                                            val hankel : ?r:mat -> mat -> mat
                                                            val hadamard : int -> mat
                                                            val magic : int -> mat
                                                            Dense row vectors and meshgrids
                                                            val vector : int -> mat
                                                            val vector_zeros : int -> mat
                                                            val vector_ones : int -> mat
                                                            val vector_uniform : int -> mat
                                                            val linspace : elt -> elt -> int -> mat
                                                            val logspace : ?base:float -> elt -> elt -> int -> mat
                                                            val meshgrid : elt -> elt -> elt -> elt -> int -> int -> mat * mat
                                                            val meshup : mat -> mat -> mat * mat
                                                            Obtain the basic properties of a matrix
                                                            val shape : mat -> int * int
                                                            val row_num : mat -> int
                                                            val col_num : mat -> int
                                                            val numel : mat -> int
                                                            val nnz : mat -> int
                                                            val density : mat -> float
                                                            val size_in_bytes : mat -> int
                                                            val same_shape : mat -> mat -> bool
                                                            val same_data : mat -> mat -> bool
                                                            Manipulate a matrix
                                                            val get : mat -> int -> int -> elt
                                                            val set : mat -> int -> int -> elt -> unit
                                                            val get_index : mat -> int array array -> elt array
                                                            val set_index : mat -> int array array -> elt array -> unit
                                                            val get_fancy : Owl_types.index list -> mat -> mat
                                                            val set_fancy : Owl_types.index list -> mat -> mat -> unit
                                                            val get_slice : int list list -> mat -> mat
                                                            val set_slice : int list list -> mat -> mat -> unit
                                                            val row : mat -> int -> mat
                                                            val col : mat -> int -> mat
                                                            val rows : mat -> int array -> mat
                                                            val cols : mat -> int array -> mat
                                                            val resize : ?head:bool -> mat -> int array -> mat
                                                            val reshape : mat -> int array -> mat
                                                            val flatten : mat -> mat
                                                            val reverse : mat -> mat
                                                            val flip : ?axis:int -> mat -> mat
                                                            val rotate : mat -> int -> mat
                                                            val reset : mat -> unit
                                                            val fill : mat -> elt -> unit
                                                            val copy : mat -> mat
                                                            val copy_row_to : mat -> mat -> int -> unit
                                                            val copy_col_to : mat -> mat -> int -> unit
                                                            val concat_vertical : mat -> mat -> mat
                                                            val concat_horizontal : mat -> mat -> mat
                                                            val concat_vh : mat array array -> mat
                                                            val concatenate : ?axis:int -> mat array -> mat
                                                            val split : ?axis:int -> int array -> mat -> mat array
                                                            val split_vh : (int * int) array array -> mat -> mat array array
                                                            val transpose : mat -> mat
                                                            val ctranspose : mat -> mat
                                                            val diag : ?k:int -> mat -> mat
                                                            val swap_rows : mat -> int -> int -> unit
                                                            val swap_cols : mat -> int -> int -> unit
                                                            val tile : mat -> int array -> mat
                                                            val repeat : mat -> int array -> mat
                                                            val pad : ?v:elt -> int list list -> mat -> mat
                                                            val dropout : ?rate:float -> mat -> mat
                                                            val top : mat -> int -> int array array
                                                            val bottom : mat -> int -> int array array
                                                            val sort : mat -> mat
                                                            val argsort : mat -> (int64, Stdlib.Bigarray.int64_elt, Stdlib.Bigarray.c_layout) diff --git a/docs/owl/Owl_dense_ndarray/Any/index.html b/docs/owl/Owl_dense_ndarray/Any/index.html index 23088bc7c..d0f38bc3a 100644 --- a/docs/owl/Owl_dense_ndarray/Any/index.html +++ b/docs/owl/Owl_dense_ndarray/Any/index.html @@ -1,5 +1,5 @@ -Any (owl.Owl_dense_ndarray.Any)

                                                            Module Owl_dense_ndarray.Any

                                                            include module type of struct include Owl_dense_ndarray_a end
                                                            type 'a arr = 'a Owl_dense_ndarray_a.arr = {
                                                            1. mutable shape : int array;
                                                            2. mutable stride : int array;
                                                            3. mutable data : 'a array;
                                                            }
                                                            Create N-dimensional array
                                                            val create : int array -> 'a -> 'a arr
                                                            val init : int array -> (int -> 'a) -> 'a arr
                                                            val init_nd : int array -> (int array -> 'a) -> 'a arr
                                                            val sequential : ?a:float -> ?step:float -> int array -> float arr
                                                            val zeros : int array -> float arr
                                                            val ones : int array -> float arr
                                                            Obtain basic properties
                                                            val shape : 'a arr -> int array
                                                            val num_dims : 'a arr -> int
                                                            val nth_dim : 'a arr -> int -> int
                                                            val numel : 'a arr -> int
                                                            val same_shape : 'a arr -> 'a arr -> bool
                                                            val strides : 'a arr -> int array
                                                            val slice_size : 'a arr -> int array
                                                            val index_1d_nd : int -> int array -> int array
                                                            val index_nd_1d : int array -> int array -> int
                                                            Manipulate a N-dimensional array
                                                            val get : 'a arr -> int array -> 'a
                                                            val set : 'a arr -> int array -> 'a -> unit
                                                            val get_index : 'a arr -> int array array -> 'a array
                                                            val set_index : 'a arr -> int array array -> 'a array -> unit
                                                            val get_fancy : Owl_types.index list -> 'a arr -> 'a arr
                                                            val set_fancy : Owl_types.index list -> 'a arr -> 'a arr -> unit
                                                            val get_slice : int list list -> 'a arr -> 'a arr
                                                            val set_slice : int list list -> 'a arr -> 'a arr -> unit
                                                            val fill : 'a arr -> 'a -> unit
                                                            val copy_ : out:'a arr -> 'a arr -> unit
                                                            val copy : 'a arr -> 'a arr
                                                            val reshape : 'a arr -> int array -> 'a arr
                                                            val flatten : 'a arr -> 'a arr
                                                            val sub_left : 'a arr -> int array -> 'a arr
                                                            val squeeze : ?axis:int array -> 'a arr -> 'a arr
                                                            val expand : ?hi:bool -> 'a arr -> int -> 'a arr
                                                            val reverse : 'a arr -> 'a arr
                                                            val transpose : ?axis:int array -> 'a arr -> 'a arr
                                                            val swap : int -> int -> 'a arr -> 'a arr
                                                            val repeat : 'a arr -> int array -> 'a arr
                                                            val tile : 'a arr -> int array -> 'a arr
                                                            val concatenate : ?axis:int -> 'a arr array -> 'a arr
                                                            val pad : 'a -> int list list -> 'a arr -> 'a arr
                                                            Iterate array elements
                                                            val iter : ('a -> unit) -> 'a arr -> unit
                                                            val iteri : (int -> 'a -> unit) -> 'a arr -> unit
                                                            val map : ('a -> 'b) -> 'a arr -> 'b arr
                                                            val mapi : (int -> 'a -> 'b) -> 'a arr -> 'b arr
                                                            val filter : ('a -> bool) -> 'a arr -> int array
                                                            val filteri : (int -> 'a -> bool) -> 'a arr -> int array
                                                            val fold : ('a -> 'b -> 'a) -> 'a -> 'b arr -> 'a
                                                            val foldi : (int -> 'a -> 'b -> 'a) -> 'a -> 'b arr -> 'a
                                                            val iter2 : ('a -> 'b -> unit) -> 'a arr -> 'b arr -> unit
                                                            val iter2i : (int -> 'a -> 'b -> unit) -> 'a arr -> 'b arr -> unit
                                                            val map2 : ('a -> 'b -> 'c) -> 'a arr -> 'b arr -> 'c arr
                                                            val map2i : (int -> 'a -> 'b -> 'c) -> 'a arr -> 'b arr -> 'c arr
                                                            Examine array elements or compare two arrays
                                                            val exists : ('a -> bool) -> 'a arr -> bool
                                                            val not_exists : ('a -> bool) -> 'a arr -> bool
                                                            val for_all : ('a -> bool) -> 'a arr -> bool
                                                            val is_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val not_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val greater : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val less : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val greater_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val less_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val elt_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_not_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_greater : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_less : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_greater_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_less_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_not_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_greater_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_less_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_greater_equal_scalar : +Any (owl.Owl_dense_ndarray.Any)

                                                            Module Owl_dense_ndarray.Any

                                                            include module type of struct include Owl_dense_ndarray_a end
                                                            type 'a arr = 'a Owl_dense_ndarray_a.arr = {
                                                            1. mutable shape : int array;
                                                            2. mutable stride : int array;
                                                            3. mutable data : 'a array;
                                                            }
                                                            Create N-dimensional array
                                                            val create : int array -> 'a -> 'a arr
                                                            val init : int array -> (int -> 'a) -> 'a arr
                                                            val init_nd : int array -> (int array -> 'a) -> 'a arr
                                                            val sequential : ?a:float -> ?step:float -> int array -> float arr
                                                            val zeros : int array -> float arr
                                                            val ones : int array -> float arr
                                                            Obtain basic properties
                                                            val shape : 'a arr -> int array
                                                            val num_dims : 'a arr -> int
                                                            val nth_dim : 'a arr -> int -> int
                                                            val numel : 'a arr -> int
                                                            val same_shape : 'a arr -> 'a arr -> bool
                                                            val strides : 'a arr -> int array
                                                            val slice_size : 'a arr -> int array
                                                            val index_1d_nd : int -> int array -> int array
                                                            val index_nd_1d : int array -> int array -> int
                                                            Manipulate a N-dimensional array
                                                            val get : 'a arr -> int array -> 'a
                                                            val set : 'a arr -> int array -> 'a -> unit
                                                            val get_index : 'a arr -> int array array -> 'a array
                                                            val set_index : 'a arr -> int array array -> 'a array -> unit
                                                            val get_fancy : Owl_types.index list -> 'a arr -> 'a arr
                                                            val set_fancy : Owl_types.index list -> 'a arr -> 'a arr -> unit
                                                            val get_slice : int list list -> 'a arr -> 'a arr
                                                            val set_slice : int list list -> 'a arr -> 'a arr -> unit
                                                            val fill : 'a arr -> 'a -> unit
                                                            val copy_ : out:'a arr -> 'a arr -> unit
                                                            val copy : 'a arr -> 'a arr
                                                            val reshape : 'a arr -> int array -> 'a arr
                                                            val flatten : 'a arr -> 'a arr
                                                            val sub_left : 'a arr -> int array -> 'a arr
                                                            val squeeze : ?axis:int array -> 'a arr -> 'a arr
                                                            val expand : ?hi:bool -> 'a arr -> int -> 'a arr
                                                            val reverse : 'a arr -> 'a arr
                                                            val transpose : ?axis:int array -> 'a arr -> 'a arr
                                                            val swap : int -> int -> 'a arr -> 'a arr
                                                            val repeat : 'a arr -> int array -> 'a arr
                                                            val tile : 'a arr -> int array -> 'a arr
                                                            val concatenate : ?axis:int -> 'a arr array -> 'a arr
                                                            val pad : 'a -> int list list -> 'a arr -> 'a arr
                                                            Iterate array elements
                                                            val iter : ('a -> unit) -> 'a arr -> unit
                                                            val iteri : (int -> 'a -> unit) -> 'a arr -> unit
                                                            val map : ('a -> 'b) -> 'a arr -> 'b arr
                                                            val mapi : (int -> 'a -> 'b) -> 'a arr -> 'b arr
                                                            val filter : ('a -> bool) -> 'a arr -> int array
                                                            val filteri : (int -> 'a -> bool) -> 'a arr -> int array
                                                            val fold : ('a -> 'b -> 'a) -> 'a -> 'b arr -> 'a
                                                            val foldi : (int -> 'a -> 'b -> 'a) -> 'a -> 'b arr -> 'a
                                                            val iter2 : ('a -> 'b -> unit) -> 'a arr -> 'b arr -> unit
                                                            val iter2i : (int -> 'a -> 'b -> unit) -> 'a arr -> 'b arr -> unit
                                                            val map2 : ('a -> 'b -> 'c) -> 'a arr -> 'b arr -> 'c arr
                                                            val map2i : (int -> 'a -> 'b -> 'c) -> 'a arr -> 'b arr -> 'c arr
                                                            Examine array elements or compare two arrays
                                                            val exists : ('a -> bool) -> 'a arr -> bool
                                                            val not_exists : ('a -> bool) -> 'a arr -> bool
                                                            val for_all : ('a -> bool) -> 'a arr -> bool
                                                            val is_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val not_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val greater : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val less : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val greater_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val less_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val elt_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_not_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_greater : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_less : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_greater_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_less_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_not_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_greater_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_less_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_greater_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> diff --git a/docs/owl/Owl_dense_ndarray/C/index.html b/docs/owl/Owl_dense_ndarray/C/index.html index 9e5a37e1f..7f196a8ac 100644 --- a/docs/owl/Owl_dense_ndarray/C/index.html +++ b/docs/owl/Owl_dense_ndarray/C/index.html @@ -1,5 +1,5 @@ -C (owl.Owl_dense_ndarray.C)

                                                            Module Owl_dense_ndarray.C

                                                            include module type of struct include Owl_dense_ndarray_c end
                                                            type elt = Stdlib.Complex.t
                                                            type arr = +C (owl.Owl_dense_ndarray.C)

                                                            Module Owl_dense_ndarray.C

                                                            include module type of struct include Owl_dense_ndarray_c end
                                                            type elt = Stdlib.Complex.t
                                                            type arr = (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            type cast_arr = (float, Stdlib.Bigarray.float32_elt, Stdlib.Bigarray.c_layout) diff --git a/docs/owl/Owl_dense_ndarray/D/index.html b/docs/owl/Owl_dense_ndarray/D/index.html index ca445fc9f..b7e6c870d 100644 --- a/docs/owl/Owl_dense_ndarray/D/index.html +++ b/docs/owl/Owl_dense_ndarray/D/index.html @@ -1,5 +1,5 @@ -D (owl.Owl_dense_ndarray.D)

                                                            Module Owl_dense_ndarray.D

                                                            include module type of struct include Owl_dense_ndarray_d end
                                                            type elt = float
                                                            type arr = +D (owl.Owl_dense_ndarray.D)

                                                            Module Owl_dense_ndarray.D

                                                            include module type of struct include Owl_dense_ndarray_d end
                                                            type elt = float
                                                            type arr = (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            include Owl_dense_ndarray_intf.Common with type elt := elt and type arr := arr
                                                            include Owl_base_dense_ndarray_intf.Common with type elt := elt diff --git a/docs/owl/Owl_dense_ndarray/Generic/index.html b/docs/owl/Owl_dense_ndarray/Generic/index.html index 2434d144d..7e67c0a94 100644 --- a/docs/owl/Owl_dense_ndarray/Generic/index.html +++ b/docs/owl/Owl_dense_ndarray/Generic/index.html @@ -1,5 +1,22 @@ -Generic (owl.Owl_dense_ndarray.Generic)

                                                            Module Owl_dense_ndarray.Generic

                                                            include module type of struct include Owl_dense_ndarray_generic end

                                                            About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of y; in case both x and y have the same magnitudes, x is less than y if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

                                                            The generic module supports operations for the following Bigarry element types: Int8_signed, Int8_unsigned, Int16_signed, Int16_unsigned, Int32, Int64, Float32, Float64, Complex32, Complex64.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            N-dimensional array type, i.e. Bigarray Genarray type.

                                                            type ('a, 'b) kind = ('a, 'b) Stdlib.Bigarray.kind

                                                            Type of the ndarray, e.g., Bigarray.Float32, Bigarray.Complex64, and etc.

                                                            Create Ndarrays
                                                            val empty : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            empty Bigarray.Float64 [|3;4;5|] creates a three diemensional array of Bigarray.Float64 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are not initialised, they can be any value. empty is faster than zeros to create a ndarray.

                                                            The module only supports the following four types of ndarray: Bigarray.Float32, Bigarray.Float64, Bigarray.Complex32, and Bigarray.Complex64.

                                                            val create : ('a, 'b) kind -> int array -> 'a -> ('a, 'b) t

                                                            create Bigarray.Float64 [|3;4;5|] 2. creates a three-diemensional array of Bigarray.Float64 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to 2.

                                                            val init : ('a, 'b) kind -> int array -> (int -> 'a) -> ('a, 'b) t

                                                            init Bigarray.Float64 d f creates a ndarray x of shape d, then using f to initialise the elements in x. The input of f is 1-dimensional index of the ndarray. You need to explicitly convert it if you need N-dimensional index. The function ind can help you.

                                                            val init_nd : ('a, 'b) kind -> int array -> (int array -> 'a) -> ('a, 'b) t

                                                            init_nd is almost the same as init but f receives n-dimensional index as input. It is more convenient since you don't have to convert the index by yourself, but this also means init_nd is slower than init.

                                                            val zeros : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            zeros Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of Bigarray.Complex32 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "zero". Depending on the kind, zero can be 0. or Complex.zero.

                                                            val ones : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            ones Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of Bigarray.Complex32 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "one". Depending on the kind, one can be 1. or Complex.one.

                                                            val eye : ('a, 'b) kind -> int -> ('a, 'b) t

                                                            eye m creates an m by m identity matrix.

                                                            val uniform : ('a, 'b) kind -> ?a:'a -> ?b:'a -> int array -> ('a, 'b) t

                                                            uniform Bigarray.Float64 [|3;4;5|] creates a three-diemensional array of type Bigarray.Float64. Each dimension has the following size: 3, 4, and 5. The elements in the array follow a uniform distribution 0,1.

                                                            val gaussian : ('a, 'b) kind -> ?mu:'a -> ?sigma:'a -> int array -> ('a, 'b) t

                                                            gaussian Float64 [|3;4;5|] ...

                                                            val poisson : ('a, 'b) kind -> mu:float -> int array -> ('a, 'b) t

                                                            poisson Float64 [|3;4;5|] ...

                                                            val sequential : ('a, 'b) kind -> ?a:'a -> ?step:'a -> int array -> ('a, 'b) t

                                                            sequential Bigarray.Float64 [|3;4;5|] 2. creates a three-diemensional array of type Bigarray.Float64. Each dimension has the following size: 3, 4, and 5. The elements in the array are assigned sequential values.

                                                            ?a specifies the starting value and the default value is zero; whilst ?step specifies the step size with default value one.

                                                            val linspace : ('a, 'b) kind -> 'a -> 'a -> int -> ('a, 'b) t

                                                            linspace k 0. 9. 10 ...

                                                            val logspace : ('a, 'b) kind -> ?base:float -> 'a -> 'a -> int -> ('a, 'b) t

                                                            logspace k 0. 9. 10 ...

                                                            val bernoulli : ('a, 'b) kind -> ?p:float -> int array -> ('a, 'b) t

                                                            bernoulli k ~p:0.3 [|2;3;4|]

                                                            val complex : +Generic (owl.Owl_dense_ndarray.Generic)

                                                            Module Owl_dense_ndarray.Generic

                                                            include module type of struct include Owl_dense_ndarray_generic end

                                                            About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of y; in case both x and y have the same magnitudes, x is less than y if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

                                                            The generic module supports operations for the following Bigarry element types: Int8_signed, Int8_unsigned, Int16_signed, Int16_unsigned, Int32, Int64, Float32, Float64, Complex32, Complex64.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            N-dimensional array type, i.e. Bigarray Genarray type.

                                                            type ('a, 'b) kind = ('a, 'b) Stdlib.Bigarray.kind

                                                            Type of the ndarray, e.g., Bigarray.Float32, Bigarray.Complex64, and etc.

                                                            Create Ndarrays
                                                            val empty : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            empty Bigarray.Float64 [|3;4;5|] creates a three diemensional array of Bigarray.Float64 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are not initialised, they can be any value. empty is faster than zeros to create a ndarray.

                                                            The module only supports the following four types of ndarray: Bigarray.Float32, Bigarray.Float64, Bigarray.Complex32, and Bigarray.Complex64.

                                                            val create : ('a, 'b) kind -> int array -> 'a -> ('a, 'b) t

                                                            create Bigarray.Float64 [|3;4;5|] 2. creates a three-diemensional array of Bigarray.Float64 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to 2.

                                                            val init : ('a, 'b) kind -> int array -> (int -> 'a) -> ('a, 'b) t

                                                            init Bigarray.Float64 d f creates a ndarray x of shape d, then using f to initialise the elements in x. The input of f is 1-dimensional index of the ndarray. You need to explicitly convert it if you need N-dimensional index. The function ind can help you.

                                                            val init_nd : ('a, 'b) kind -> int array -> (int array -> 'a) -> ('a, 'b) t

                                                            init_nd is almost the same as init but f receives n-dimensional index as input. It is more convenient since you don't have to convert the index by yourself, but this also means init_nd is slower than init.

                                                            val zeros : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            zeros Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of Bigarray.Complex32 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "zero". Depending on the kind, zero can be 0. or Complex.zero.

                                                            val ones : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            ones Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of Bigarray.Complex32 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "one". Depending on the kind, one can be 1. or Complex.one.

                                                            val eye : ('a, 'b) kind -> int -> ('a, 'b) t

                                                            eye m creates an m by m identity matrix.

                                                            val uniform : ('a, 'b) kind -> ?a:'a -> ?b:'a -> int array -> ('a, 'b) t

                                                            uniform Bigarray.Float64 [|3;4;5|] creates a three-diemensional array of type Bigarray.Float64. Each dimension has the following size: 3, 4, and 5. The elements in the array follow a uniform distribution 0,1.

                                                            val gaussian : ('a, 'b) kind -> ?mu:'a -> ?sigma:'a -> int array -> ('a, 'b) t

                                                            gaussian Float64 [|3;4;5|] ...

                                                            val poisson : ('a, 'b) kind -> mu:float -> int array -> ('a, 'b) t

                                                            poisson Float64 [|3;4;5|] ...

                                                            val sequential : ('a, 'b) kind -> ?a:'a -> ?step:'a -> int array -> ('a, 'b) t

                                                            sequential Bigarray.Float64 [|3;4;5|] 2. creates a three-diemensional array of type Bigarray.Float64. Each dimension has the following size: 3, 4, and 5. The elements in the array are assigned sequential values.

                                                            ?a specifies the starting value and the default value is zero; whilst ?step specifies the step size with default value one.

                                                            val linspace : ('a, 'b) kind -> 'a -> 'a -> int -> ('a, 'b) t

                                                            linspace k 0. 9. 10 ...

                                                            val logspace : ('a, 'b) kind -> ?base:float -> 'a -> 'a -> int -> ('a, 'b) t

                                                            logspace k 0. 9. 10 ...

                                                            val bernoulli : ('a, 'b) kind -> ?p:float -> int array -> ('a, 'b) t

                                                            bernoulli k ~p:0.3 [|2;3;4|]

                                                            val complex : ('a, 'b) kind -> ('c, 'd) kind -> ('a, 'b) t -> @@ -9,7 +26,13 @@ ('c, 'd) kind -> ('a, 'b) t -> ('a, 'b) t -> - ('c, 'd) t

                                                            complex rho theta constructs a complex ndarray/matrix from polar coordinates rho and theta. rho contains the magnitudes and theta contains phase angles. Note that the behaviour is undefined if rho has negative elelments or theta has infinity elelments.

                                                            val unit_basis : ('a, 'b) kind -> int -> int -> ('a, 'b) t

                                                            unit_basis k n i returns a unit basis vector with ith element set to 1.

                                                            Obtain basic properties
                                                            val shape : ('a, 'b) t -> int array

                                                            shape x returns the shape of ndarray x.

                                                            val num_dims : ('a, 'b) t -> int

                                                            num_dims x returns the number of dimensions of ndarray x.

                                                            val nth_dim : ('a, 'b) t -> int -> int

                                                            nth_dim x returns the size of the nth dimension of x.

                                                            val numel : ('a, 'b) t -> int

                                                            numel x returns the number of elements in x.

                                                            val nnz : ('a, 'b) t -> int

                                                            nnz x returns the number of non-zero elements in x.

                                                            val density : ('a, 'b) t -> float

                                                            density x returns the percentage of non-zero elements in x.

                                                            val size_in_bytes : ('a, 'b) t -> int

                                                            size_in_bytes x returns the size of x in bytes in memory.

                                                            val same_shape : ('a, 'b) t -> ('c, 'd) t -> bool

                                                            same_shape x y checks whether x and y has the same shape or not.

                                                            val same_data : ('a, 'b) t -> ('a, 'b) t -> bool

                                                            same_data x y checks whether x and y share the same underlying data in the memory. Namely, both variables point to the same memory address. This is done by checking the Data pointer in the Bigarray structure.

                                                            This function is very useful for avoiding unnecessary copying between two ndarrays especially if one has been reshaped or sliced.

                                                            val kind : ('a, 'b) t -> ('a, 'b) kind

                                                            kind x returns the type of ndarray x. It is one of the four possible values: Bigarray.Float32, Bigarray.Float64, Bigarray.Complex32, and Bigarray.Complex64.

                                                            val strides : ('a, 'b) t -> int array

                                                            strides x calculates the strides of x. E.g., if x is of shape [|3;4;5|], the returned strides will be [|20;5;1|].

                                                            val slice_size : ('a, 'b) t -> int array

                                                            slice_size calculates the slice size in each dimension, E.g., if x is of shape [|3;4;5|], the returned slice size will be [|60; 20; 5|].

                                                            val ind : ('a, 'b) t -> int -> int array

                                                            ind x i converts x's one-dimensional index i to n-dimensional one.

                                                            val i1d : ('a, 'b) t -> int array -> int

                                                            i1d x i converts x's n-dimensional index i to one-dimensional one.

                                                            Manipulate Ndarrays
                                                            val get : ('a, 'b) t -> int array -> 'a

                                                            get x i returns the value at i in x. E.g., get x [|0;2;1|] returns the value at [|0;2;1|] in x.

                                                            val set : ('a, 'b) t -> int array -> 'a -> unit

                                                            set x i a sets the value at i to a in x.

                                                            val get_index : ('a, 'b) t -> int array array -> 'a array

                                                            get_index i x returns an array of element values specified by the indices i. The length of array i equals the number of dimensions of x. The arrays in i must have the same length, and each represents the indices in that dimension.

                                                            E.g., [| [|1;2|]; [|3;4|] |] returns the value of elements at position (1,3) and (2,4) respectively.

                                                            val set_index : ('a, 'b) t -> int array array -> 'a array -> unit

                                                            set_index i x a sets the value of elements in x according to the indices specified by i. The length of array i equals the number of dimensions of x. The arrays in i must have the same length, and each represents the indices in that dimension.

                                                            If the length of a equals to the length of i, then each element will be assigned by the value in the corresponding position in x. If the length of a equals to one, then all the elements will be assigned the same value.

                                                            val get_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t

                                                            get_fancy s x returns a copy of the slice in x. The slice is defined by a which is an int option array. E.g., for a ndarray x of dimension [|2; 2; 3|], slice [0] x takes the following slices of index \(0,*,*\), i.e., [|0;0;0|], [|0;0;1|], [|0;0;2|] ... Also note that if the length of s is less than the number of dimensions of x, slice function will append slice definition to higher diemensions by assuming all the elements in missing dimensions will be taken.

                                                            Basically, slice function offers very much the same semantic as that in numpy, i.e., start:stop:step grammar, so if you how to index and slice ndarray in numpy, you should not find it difficult to use this function. Please just refer to numpy documentation or my tutorial.

                                                            There are two differences between slice_left and slice: slice_left does not make a copy but simply moving the pointer; slice_left can only make a slice from left-most axis whereas slice is much more flexible and can work on arbitrary axis which need not start from left-most side.

                                                            val set_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            set_fancy axis x y set the slice defined by axis in x according to the values in y. y must have the same shape as the one defined by axis.

                                                            About the slice definition of axis, please refer to get_fancy function.

                                                            val get_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t

                                                            This function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val set_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            This function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val get_slice : int list list -> ('a, 'b) t -> ('a, 'b) t

                                                            get_slice axis x aims to provide a simpler version of get_fancy. This function assumes that every list element in the passed in int list list represents a range, i.e., R constructor.

                                                            E.g., [[];[0;3];[0]] is equivalent to [R []; R [0;3]; R [0]].

                                                            val set_slice : int list list -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            set_slice axis x y aims to provide a simpler version of set_fancy. This function assumes that every list element in the passed in int list list represents a range, i.e., R constructor.

                                                            E.g., [[];[0;3];[0]] is equivalent to [R []; R [0;3]; R [0]].

                                                            val get_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t

                                                            get_slice_ext axis x is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            E.g., x.%{0;1;2}.

                                                            val set_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            Similar to get_slice_ext axis x, this function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val sub_left : ('a, 'b) t -> int -> int -> ('a, 'b) t

                                                            Some as Bigarray.sub_left, please refer to Bigarray documentation.

                                                            val sub_ndarray : int array -> ('a, 'b) t -> ('a, 'b) t array

                                                            sub_ndarray parts x is similar to Bigarray.sub_left. It splits the passed in ndarray x along the axis 0 according to parts. The elelments in parts do not need to be equal but they must sum up to the dimension along axis zero.

                                                            The returned sub-ndarrays share the same memory as x. Because there is no copies made, this function is much faster than using `split` function to divide the lowest dimensionality of x.

                                                            val slice_left : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Same as Bigarray.slice_left, please refer to Bigarray documentation.

                                                            val reset : ('a, 'b) t -> unit

                                                            reset x resets all the elements in x to zero.

                                                            val fill : ('a, 'b) t -> 'a -> unit

                                                            fill x a assigns the value a to the elements in x.

                                                            val copy : ('a, 'b) t -> ('a, 'b) t

                                                            copy x makes a copy of x.

                                                            val resize : ?head:bool -> ('a, 'b) t -> int array -> ('a, 'b) t

                                                            resize ~head x d resizes the ndarray x. If there are less number of elelments in the new shape than the old one, the new ndarray shares part of the memory with the old x. head indicates the alignment between the new and old data, either from head or from tail. Note the data is flattened before the operation.

                                                            If there are more elements in the new shape d. Then new memory space will be allocated and the content of x will be copied to the new memory. The rest of the allocated space will be filled with zeros. The default value of head is true.

                                                            val reshape : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            reshape x d transforms x into a new shape definted by d. Note the reshape function will not make a copy of x, the returned ndarray shares the same memory with the original x.

                                                            One shape dimension (only one) can be set to -1. In this case, the value is inferred from the length of the array and remaining dimensions.

                                                            val flatten : ('a, 'b) t -> ('a, 'b) t

                                                            flatten x transforms x into a one-dimsonal array without making a copy. Therefore the returned value shares the same memory space with original x.

                                                            val reverse : ('a, 'b) t -> ('a, 'b) t

                                                            reverse x reverse the order of all elements in the flattened x and returns the results in a new ndarray. The original x remains intact.

                                                            val flip : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            flip ~axis x flips a matrix/ndarray along axis. By default axis = 0. The result is returned in a new matrix/ndarray, so the original x remains intact.

                                                            val rotate : ('a, 'b) t -> int -> ('a, 'b) t

                                                            rotate x d rotates x clockwise d degrees. d must be multiple times of 90, otherwise the function will fail. If x is an n-dimensional array, then the function rotates the plane formed by the first and second dimensions.

                                                            val transpose : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

                                                            transpose ~axis x makes a copy of x, then transpose it according to ~axis. ~axis must be a valid permutation of x dimension indices. E.g., for a three-dimensional ndarray, it can be [2;1;0], [0;2;1], [1;2;0], and etc.

                                                            val swap : int -> int -> ('a, 'b) t -> ('a, 'b) t

                                                            swap i j x makes a copy of x, then swaps the data on axis i and j.

                                                            val tile : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            tile x a tiles the data in x according the repetition specified by a. This function provides the exact behaviour as numpy.tile, please refer to the numpy's online documentation for details.

                                                            val repeat : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            repeat x a repeats the elements of x according the repetition specified by a. The i-th element of a specifies the number of times that the individual entries of the i-th dimension of x should be repeated.

                                                            val concat_vertical : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            concat_vertical x y concatenates two ndarray x and y vertically. This is just a convenient function for concatenating two ndarrays along their lowest dimension, i.e. 0.

                                                            The associated operator is @||, please refer to :doc:`owl_operator`.

                                                            val concat_horizontal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            concat_horizontal x y concatenates two ndarrays x and y horizontally. This is just a convenient function for concatenating two ndarrays along their highest dimension.

                                                            The associated operator is @=, please refer to :doc:`owl_operator`.

                                                            val concat_vh : ('a, 'b) t array array -> ('a, 'b) t

                                                            concat_vh is used to assemble small parts of matrices into a bigger one. E.g. In [| [|a; b; c|]; [|d; e; f|]; [|g; h; i|] |], wherein `a, b, c ... i` are matrices of different shapes. They will be concatenated into a big matrix as follows.

                                                            .. math:: \beginmatrix a & b & c \\ d & e & f \\ g & h & i \endmatrix

                                                            This is achieved by first concatenating along axis:1 for each element in the array, then concatenating along axis:0. The number of elements in each array needs not to be equal as long as the aggregated dimensions match. E.g., please check the following example.

                                                            .. code-block:: ocaml

                                                            let a00 = Mat.sequential 2 3 in let a01 = Mat.sequential 2 2 in let a02 = Mat.sequential 2 1 in let a10 = Mat.sequential 3 3 in let a11 = Mat.sequential 3 3 in Mat.concat_vh | [|a00; a01; a02|]; [|a10; a11|] |;;

                                                            val concatenate : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

                                                            concatenate ~axis:2 x concatenates an array of ndarrays along the third dimension. For the ndarrays in x, they must have the same shape except the dimension specified by axis. The default value of axis is 0, i.e., the lowest dimension of a matrix/ndarray.

                                                            val stack : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

                                                            stack ~axis x stacks an array of ndarrays along the axis dimension. For example, if x contains K ndarrays of shape |2;3|, then stack ~axis:1 x will return an ndarray of dimensions |2;K;3|. The ndarrays in x, they must all have the same shape. The default value of axis is 0.

                                                            val split : ?axis:int -> int array -> ('a, 'b) t -> ('a, 'b) t array

                                                            split ~axis parts x splits an ndarray x into parts along the specified axis. This function is the inverse operation of concatenate. The elements in x must sum up to the dimension in the specified axis.

                                                            val split_vh : (int * int) array array -> ('a, 'b) t -> ('a, 'b) t array array

                                                            split_vh parts x splits a passed in ndarray x along the first two dimensions, i.e. axis 0 and axis 1. This is the inverse operation of concat_vh function, and the function is very useful in dividing a big matrix into smaller (especially heterogeneous) parts.

                                                            For example, given a matrix x of shape [|8;10|], it is possible to split in the following ways.

                                                            .. code-block:: ocaml

                                                            Mat.split_vh | [|(8,5);(8,5)|] | x;; Mat.split_vh | [|(4,5);(4,5)|]; [|(4,10)|] | x;; Mat.split_vh | [|(4,5);(4,5)|]; [|(4,5);(4,5)|] | x;;

                                                            val squeeze : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

                                                            squeeze ~axis x removes single-dimensional entries from the shape of x.

                                                            val expand : ?hi:bool -> ('a, 'b) t -> int -> ('a, 'b) t

                                                            expand x d reshapes x by increasing its rank from num_dims x to d. The opposite operation is squeeze x. The hi parameter is used to specify whether the expandsion is along high dimension (by setting true), or along the low dimension (by setting false). The default value is false.

                                                            val pad : ?v:'a -> int list list -> ('a, 'b) t -> ('a, 'b) t

                                                            pad ~v p x pads a ndarray x with a constant value v. The padding index p is a list of lists of 2 integers. These two integers denote padding width at both edges of one dimension of x.

                                                            val dropout : ?rate:float -> ('a, 'b) t -> ('a, 'b) t

                                                            dropout ~rate:0.3 x drops out 30% of the elements in x, in other words, by setting their values to zeros.

                                                            val top : ('a, 'b) t -> int -> int array array

                                                            top x n returns the indices of n greatest values of x. The indices are arranged according to the corresponding element values, from the greatest one to the smallest one.

                                                            val bottom : ('a, 'b) t -> int -> int array array

                                                            bottom x n returns the indices of n smallest values of x. The indices are arranged according to the corresponding element values, from the smallest one to the greatest one.

                                                            val sort1 : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            sort1 ~axis x performs quicksort of the elements along specific axis in x. A new copy is returned as result, the original x remains intact.

                                                            val sort : ('a, 'b) t -> ('a, 'b) t

                                                            sort x performs quicksort of the elelments in x. A new copy is returned as result, the original x remains intact. If you want to perform in-place sorting, please use `sort_` instead.

                                                            val argsort : ('a, 'b) t -> (int64, Stdlib.Bigarray.int64_elt) t

                                                            argsort x returns the indices with which the elements in x are sorted in increasing order. Note that the returned index ndarray has the same shape as that of x, and the indices are 1D indices.

                                                            val draw : ?axis:int -> ('a, 'b) t -> int -> ('a, 'b) t * int array

                                                            draw ~axis x n draws n samples from x along the specified axis, with replacement. axis is set to zero by default. The return is a tuple of both samples and the indices of the selected samples.

                                                            val mmap : + ('c, 'd) t

                                                            complex rho theta constructs a complex ndarray/matrix from polar coordinates rho and theta. rho contains the magnitudes and theta contains phase angles. Note that the behaviour is undefined if rho has negative elelments or theta has infinity elelments.

                                                            val unit_basis : ('a, 'b) kind -> int -> int -> ('a, 'b) t

                                                            unit_basis k n i returns a unit basis vector with ith element set to 1.

                                                            Obtain basic properties
                                                            val shape : ('a, 'b) t -> int array

                                                            shape x returns the shape of ndarray x.

                                                            val num_dims : ('a, 'b) t -> int

                                                            num_dims x returns the number of dimensions of ndarray x.

                                                            val nth_dim : ('a, 'b) t -> int -> int

                                                            nth_dim x returns the size of the nth dimension of x.

                                                            val numel : ('a, 'b) t -> int

                                                            numel x returns the number of elements in x.

                                                            val nnz : ('a, 'b) t -> int

                                                            nnz x returns the number of non-zero elements in x.

                                                            val density : ('a, 'b) t -> float

                                                            density x returns the percentage of non-zero elements in x.

                                                            val size_in_bytes : ('a, 'b) t -> int

                                                            size_in_bytes x returns the size of x in bytes in memory.

                                                            val same_shape : ('a, 'b) t -> ('c, 'd) t -> bool

                                                            same_shape x y checks whether x and y has the same shape or not.

                                                            val same_data : ('a, 'b) t -> ('a, 'b) t -> bool

                                                            same_data x y checks whether x and y share the same underlying data in the memory. Namely, both variables point to the same memory address. This is done by checking the Data pointer in the Bigarray structure.

                                                            This function is very useful for avoiding unnecessary copying between two ndarrays especially if one has been reshaped or sliced.

                                                            val kind : ('a, 'b) t -> ('a, 'b) kind

                                                            kind x returns the type of ndarray x. It is one of the four possible values: Bigarray.Float32, Bigarray.Float64, Bigarray.Complex32, and Bigarray.Complex64.

                                                            val strides : ('a, 'b) t -> int array

                                                            strides x calculates the strides of x. E.g., if x is of shape [|3;4;5|], the returned strides will be [|20;5;1|].

                                                            val slice_size : ('a, 'b) t -> int array

                                                            slice_size calculates the slice size in each dimension, E.g., if x is of shape [|3;4;5|], the returned slice size will be [|60; 20; 5|].

                                                            val ind : ('a, 'b) t -> int -> int array

                                                            ind x i converts x's one-dimensional index i to n-dimensional one.

                                                            val i1d : ('a, 'b) t -> int array -> int

                                                            i1d x i converts x's n-dimensional index i to one-dimensional one.

                                                            Manipulate Ndarrays
                                                            val get : ('a, 'b) t -> int array -> 'a

                                                            get x i returns the value at i in x. E.g., get x [|0;2;1|] returns the value at [|0;2;1|] in x.

                                                            val set : ('a, 'b) t -> int array -> 'a -> unit

                                                            set x i a sets the value at i to a in x.

                                                            val get_index : ('a, 'b) t -> int array array -> 'a array

                                                            get_index i x returns an array of element values specified by the indices i. The length of array i equals the number of dimensions of x. The arrays in i must have the same length, and each represents the indices in that dimension.

                                                            E.g., [| [|1;2|]; [|3;4|] |] returns the value of elements at position (1,3) and (2,4) respectively.

                                                            val set_index : ('a, 'b) t -> int array array -> 'a array -> unit

                                                            set_index i x a sets the value of elements in x according to the indices specified by i. The length of array i equals the number of dimensions of x. The arrays in i must have the same length, and each represents the indices in that dimension.

                                                            If the length of a equals to the length of i, then each element will be assigned by the value in the corresponding position in x. If the length of a equals to one, then all the elements will be assigned the same value.

                                                            val get_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t

                                                            get_fancy s x returns a copy of the slice in x. The slice is defined by a which is an int option array. E.g., for a ndarray x of dimension [|2; 2; 3|], slice [0] x takes the following slices of index \(0,*,*\), i.e., [|0;0;0|], [|0;0;1|], [|0;0;2|] ... Also note that if the length of s is less than the number of dimensions of x, slice function will append slice definition to higher diemensions by assuming all the elements in missing dimensions will be taken.

                                                            Basically, slice function offers very much the same semantic as that in numpy, i.e., start:stop:step grammar, so if you how to index and slice ndarray in numpy, you should not find it difficult to use this function. Please just refer to numpy documentation or my tutorial.

                                                            There are two differences between slice_left and slice: slice_left does not make a copy but simply moving the pointer; slice_left can only make a slice from left-most axis whereas slice is much more flexible and can work on arbitrary axis which need not start from left-most side.

                                                            val set_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            set_fancy axis x y set the slice defined by axis in x according to the values in y. y must have the same shape as the one defined by axis.

                                                            About the slice definition of axis, please refer to get_fancy function.

                                                            val get_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t

                                                            This function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val set_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            This function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val get_slice : int list list -> ('a, 'b) t -> ('a, 'b) t

                                                            get_slice axis x aims to provide a simpler version of get_fancy. This function assumes that every list element in the passed in int list list represents a range, i.e., R constructor.

                                                            E.g., [[];[0;3];[0]] is equivalent to [R []; R [0;3]; R [0]].

                                                            val set_slice : int list list -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            set_slice axis x y aims to provide a simpler version of set_fancy. This function assumes that every list element in the passed in int list list represents a range, i.e., R constructor.

                                                            E.g., [[];[0;3];[0]] is equivalent to [R []; R [0;3]; R [0]].

                                                            val get_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t

                                                            get_slice_ext axis x is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            E.g., x.%{0;1;2}.

                                                            val set_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            Similar to get_slice_ext axis x, this function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val sub_left : ('a, 'b) t -> int -> int -> ('a, 'b) t

                                                            Some as Bigarray.sub_left, please refer to Bigarray documentation.

                                                            val sub_ndarray : int array -> ('a, 'b) t -> ('a, 'b) t array

                                                            sub_ndarray parts x is similar to Bigarray.sub_left. It splits the passed in ndarray x along the axis 0 according to parts. The elelments in parts do not need to be equal but they must sum up to the dimension along axis zero.

                                                            The returned sub-ndarrays share the same memory as x. Because there is no copies made, this function is much faster than using `split` function to divide the lowest dimensionality of x.

                                                            val slice_left : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Same as Bigarray.slice_left, please refer to Bigarray documentation.

                                                            val reset : ('a, 'b) t -> unit

                                                            reset x resets all the elements in x to zero.

                                                            val fill : ('a, 'b) t -> 'a -> unit

                                                            fill x a assigns the value a to the elements in x.

                                                            val copy : ('a, 'b) t -> ('a, 'b) t

                                                            copy x makes a copy of x.

                                                            val resize : ?head:bool -> ('a, 'b) t -> int array -> ('a, 'b) t

                                                            resize ~head x d resizes the ndarray x. If there are less number of elelments in the new shape than the old one, the new ndarray shares part of the memory with the old x. head indicates the alignment between the new and old data, either from head or from tail. Note the data is flattened before the operation.

                                                            If there are more elements in the new shape d. Then new memory space will be allocated and the content of x will be copied to the new memory. The rest of the allocated space will be filled with zeros. The default value of head is true.

                                                            val reshape : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            reshape x d transforms x into a new shape definted by d. Note the reshape function will not make a copy of x, the returned ndarray shares the same memory with the original x.

                                                            One shape dimension (only one) can be set to -1. In this case, the value is inferred from the length of the array and remaining dimensions.

                                                            val flatten : ('a, 'b) t -> ('a, 'b) t

                                                            flatten x transforms x into a one-dimsonal array without making a copy. Therefore the returned value shares the same memory space with original x.

                                                            val reverse : ('a, 'b) t -> ('a, 'b) t

                                                            reverse x reverse the order of all elements in the flattened x and returns the results in a new ndarray. The original x remains intact.

                                                            val flip : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            flip ~axis x flips a matrix/ndarray along axis. By default axis = 0. The result is returned in a new matrix/ndarray, so the original x remains intact.

                                                            val rotate : ('a, 'b) t -> int -> ('a, 'b) t

                                                            rotate x d rotates x clockwise d degrees. d must be multiple times of 90, otherwise the function will fail. If x is an n-dimensional array, then the function rotates the plane formed by the first and second dimensions.

                                                            val transpose : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

                                                            transpose ~axis x makes a copy of x, then transpose it according to ~axis. ~axis must be a valid permutation of x dimension indices. E.g., for a three-dimensional ndarray, it can be [2;1;0], [0;2;1], [1;2;0], and etc.

                                                            val swap : int -> int -> ('a, 'b) t -> ('a, 'b) t

                                                            swap i j x makes a copy of x, then swaps the data on axis i and j.

                                                            val tile : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            tile x a tiles the data in x according the repetition specified by a. This function provides the exact behaviour as numpy.tile, please refer to the numpy's online documentation for details.

                                                            val repeat : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            repeat x a repeats the elements of x according the repetition specified by a. The i-th element of a specifies the number of times that the individual entries of the i-th dimension of x should be repeated.

                                                            val concat_vertical : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            concat_vertical x y concatenates two ndarray x and y vertically. This is just a convenient function for concatenating two ndarrays along their lowest dimension, i.e. 0.

                                                            The associated operator is @||, please refer to :doc:`owl_operator`.

                                                            val concat_horizontal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            concat_horizontal x y concatenates two ndarrays x and y horizontally. This is just a convenient function for concatenating two ndarrays along their highest dimension.

                                                            The associated operator is @=, please refer to :doc:`owl_operator`.

                                                            val concat_vh : ('a, 'b) t array array -> ('a, 'b) t

                                                            concat_vh is used to assemble small parts of matrices into a bigger one. E.g. In [| [|a; b; c|]; [|d; e; f|]; [|g; h; i|] |], wherein `a, b, c ... i` are matrices of different shapes. They will be concatenated into a big matrix as follows.

                                                            +  \begin{bmatrix}
                                                            +    a & b & c \\
                                                            +    d & e & f \\
                                                            +    g & h & i
                                                            +  \end{bmatrix}
                                                            +

                                                            This is achieved by first concatenating along axis:1 for each element in the array, then concatenating along axis:0. The number of elements in each array needs not to be equal as long as the aggregated dimensions match. E.g., please check the following example.

                                                            .. code-block:: ocaml

                                                            let a00 = Mat.sequential 2 3 in let a01 = Mat.sequential 2 2 in let a02 = Mat.sequential 2 1 in let a10 = Mat.sequential 3 3 in let a11 = Mat.sequential 3 3 in Mat.concat_vh | [|a00; a01; a02|]; [|a10; a11|] |;;

                                                            val concatenate : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

                                                            concatenate ~axis:2 x concatenates an array of ndarrays along the third dimension. For the ndarrays in x, they must have the same shape except the dimension specified by axis. The default value of axis is 0, i.e., the lowest dimension of a matrix/ndarray.

                                                            val stack : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

                                                            stack ~axis x stacks an array of ndarrays along the axis dimension. For example, if x contains K ndarrays of shape |2;3|, then stack ~axis:1 x will return an ndarray of dimensions |2;K;3|. The ndarrays in x, they must all have the same shape. The default value of axis is 0.

                                                            val split : ?axis:int -> int array -> ('a, 'b) t -> ('a, 'b) t array

                                                            split ~axis parts x splits an ndarray x into parts along the specified axis. This function is the inverse operation of concatenate. The elements in x must sum up to the dimension in the specified axis.

                                                            val split_vh : (int * int) array array -> ('a, 'b) t -> ('a, 'b) t array array

                                                            split_vh parts x splits a passed in ndarray x along the first two dimensions, i.e. axis 0 and axis 1. This is the inverse operation of concat_vh function, and the function is very useful in dividing a big matrix into smaller (especially heterogeneous) parts.

                                                            For example, given a matrix x of shape [|8;10|], it is possible to split in the following ways.

                                                            .. code-block:: ocaml

                                                            Mat.split_vh | [|(8,5);(8,5)|] | x;; Mat.split_vh | [|(4,5);(4,5)|]; [|(4,10)|] | x;; Mat.split_vh | [|(4,5);(4,5)|]; [|(4,5);(4,5)|] | x;;

                                                            val squeeze : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

                                                            squeeze ~axis x removes single-dimensional entries from the shape of x.

                                                            val expand : ?hi:bool -> ('a, 'b) t -> int -> ('a, 'b) t

                                                            expand x d reshapes x by increasing its rank from num_dims x to d. The opposite operation is squeeze x. The hi parameter is used to specify whether the expandsion is along high dimension (by setting true), or along the low dimension (by setting false). The default value is false.

                                                            val pad : ?v:'a -> int list list -> ('a, 'b) t -> ('a, 'b) t

                                                            pad ~v p x pads a ndarray x with a constant value v. The padding index p is a list of lists of 2 integers. These two integers denote padding width at both edges of one dimension of x.

                                                            val dropout : ?rate:float -> ('a, 'b) t -> ('a, 'b) t

                                                            dropout ~rate:0.3 x drops out 30% of the elements in x, in other words, by setting their values to zeros.

                                                            val top : ('a, 'b) t -> int -> int array array

                                                            top x n returns the indices of n greatest values of x. The indices are arranged according to the corresponding element values, from the greatest one to the smallest one.

                                                            val bottom : ('a, 'b) t -> int -> int array array

                                                            bottom x n returns the indices of n smallest values of x. The indices are arranged according to the corresponding element values, from the smallest one to the greatest one.

                                                            val sort1 : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            sort1 ~axis x performs quicksort of the elements along specific axis in x. A new copy is returned as result, the original x remains intact.

                                                            val sort : ('a, 'b) t -> ('a, 'b) t

                                                            sort x performs quicksort of the elelments in x. A new copy is returned as result, the original x remains intact. If you want to perform in-place sorting, please use `sort_` instead.

                                                            val argsort : ('a, 'b) t -> (int64, Stdlib.Bigarray.int64_elt) t

                                                            argsort x returns the indices with which the elements in x are sorted in increasing order. Note that the returned index ndarray has the same shape as that of x, and the indices are 1D indices.

                                                            val draw : ?axis:int -> ('a, 'b) t -> int -> ('a, 'b) t * int array

                                                            draw ~axis x n draws n samples from x along the specified axis, with replacement. axis is set to zero by default. The return is a tuple of both samples and the indices of the selected samples.

                                                            val mmap : Unix.file_descr -> ?pos:int64 -> ('a, 'b) kind -> @@ -98,7 +121,8 @@ ?p:float -> ?keep_dims:bool -> ('a, 'b) t -> - ('a, 'b) t

                                                            vecnorm ~axis ~p x calculates the generalised vector p-norm along the specified axis. The generalised p-norm is defined as below.

                                                            .. math:: ||v||_p = \Big \sum_{k=0}^{N-1} |v_k|^p \Big^

                                                            /p

                                                            Parameters: * axis is the axis for reduction. * p is order of norm, default value is 2. * x is the input ndarray.

                                                            Returns: * If p = infinity, then returns :math:`||v||_\infty = \max_i(|v(i)|)`. * If p = -infinity, then returns :math:`||v||_

                                                            \infty

                                                            }

                                                            = \min_i(|v(i)|)`. * Otherwise returns generalised vector p-norm defined above.

                                                            val vecnorm' : ?p:float -> ('a, 'b) t -> 'a

                                                            vecnorm' flattens the input into 1-d vector first, then calculates the generalised p-norm the same as venorm.

                                                            val cumsum : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cumsum ~axis x : performs cumulative sum of the elements along the given axis ~axis. If ~axis is None, then the cumsum is performed along the lowest dimension. The returned result however always remains the same shape.

                                                            val cumprod : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cumprod ~axis x : similar to cumsum but performs cumulative product of the elements along the given ~axis.

                                                            val cummin : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cummin ~axis x : performs cumulative min along axis dimension.

                                                            val cummax : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cummax ~axis x : performs cumulative max along axis dimension.

                                                            val diff : ?axis:int -> ?n:int -> ('a, 'b) t -> ('a, 'b) t

                                                            diff ~axis ~n x calculates the n-th difference of x along the specified axis.

                                                            Parameters: * axis: axis to calculate the difference. The default value is the highest dimension. * n: how many times to calculate the difference. The default value is 1.

                                                            Return: * The difference ndarray y. Note that the shape of y 1 less than that of x along specified axis.

                                                            val angle : (Stdlib.Complex.t, 'a) t -> (Stdlib.Complex.t, 'a) t

                                                            angle x calculates the phase angle of all complex numbers in x.

                                                            val proj : (Stdlib.Complex.t, 'a) t -> (Stdlib.Complex.t, 'a) t

                                                            proj x computes the projection on Riemann sphere of all elelments in x.

                                                            val lgamma : ('a, 'b) t -> ('a, 'b) t

                                                            lgamma x computes the loggamma of the elements in x and returns the result in a new ndarray.

                                                            val dawsn : ('a, 'b) t -> ('a, 'b) t

                                                            dawsn x computes the Dawson function of the elements in x and returns the result in a new ndarray.

                                                            val i0 : ('a, 'b) t -> ('a, 'b) t

                                                            i0 x computes the modified Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val i0e : ('a, 'b) t -> ('a, 'b) t

                                                            i0e x computes the exponentially scaled modified Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val i1 : ('a, 'b) t -> ('a, 'b) t

                                                            i1 x computes the modified Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val i1e : ('a, 'b) t -> ('a, 'b) t

                                                            i1e x computes the exponentially scaled modified Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val iv : v:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            iv v x computes modified Bessel function of x of real order v

                                                            val scalar_iv : v:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_iv v x computes the modified Bessel function of x of real order v.

                                                            val iv_scalar : v:('a, 'b) t -> 'a -> ('a, 'b) t

                                                            iv_scalar v x computes modified Bessel function of x of real order v

                                                            val j0 : ('a, 'b) t -> ('a, 'b) t

                                                            j0 x computes the Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val j1 : ('a, 'b) t -> ('a, 'b) t

                                                            j1 x computes the Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val jv : v:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            jv v x computes Bessel function the first kind of x of real order v

                                                            val scalar_jv : v:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_jv v x computes the Bessel function of the first kind of x of real order v.

                                                            val jv_scalar : v:('a, 'b) t -> 'a -> ('a, 'b) t

                                                            jv_scalar v x computes Bessel function of the first kind of x of real order v

                                                            Binary math operators
                                                            val add : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            add x y adds all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            General broadcast operation is automatically applied to add/sub/mul/div, etc. The function compares the dimension element-wise from the highest to the lowest with the following broadcast rules (same as numpy): 1. equal; 2. either is 1.

                                                            val sub : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            sub x y subtracts all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val mul : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            mul x y multiplies all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val div : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            div x y divides all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val add_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            add_scalar x a adds a scalar value a to each element in x, and returns the result in a new ndarray.

                                                            val sub_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            sub_scalar x a subtracts a scalar value a from each element in x, and returns the result in a new ndarray.

                                                            val mul_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            mul_scalar x a multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val div_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            div_scalar x a divides each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_add : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_add a x adds a scalar value a to each element in x, and returns the result in a new ndarray.

                                                            val scalar_sub : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_sub a x subtracts each element in x from a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_mul : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_mul a x multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_div : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_div a x divides a scalar value a by each element in x, and returns the result in a new ndarray.

                                                            val pow : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            pow x y computes pow(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val scalar_pow : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_pow a x computes the power value of a scalar value a using the elements in a ndarray x.

                                                            val pow_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            pow_scalar x a computes each element in x power to a.

                                                            val atan2 : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            atan2 x y computes atan2(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val scalar_atan2 : float -> (float, 'a) t -> (float, 'a) t

                                                            scalar_atan2 a x

                                                            val atan2_scalar : (float, 'a) t -> float -> (float, 'a) t

                                                            scalar_atan2 x a

                                                            val hypot : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            hypot x y computes sqrt(x*x + y*y) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val min2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            min2 x y computes the minimum of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val max2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            max2 x y computes the maximum of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val fmod : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            fmod x y performs float mod division.

                                                            val fmod_scalar : (float, 'a) t -> float -> (float, 'a) t

                                                            fmod_scalar x a performs mod division between x and scalar a.

                                                            val scalar_fmod : float -> (float, 'a) t -> (float, 'a) t

                                                            scalar_fmod x a performs mod division between scalar a and x.

                                                            val ssqr' : ('a, 'b) t -> 'a -> 'a

                                                            ssqr x a computes the sum of squared differences of all the elements in x from constant a. This function only computes the square of each element rather than the conjugate transpose as l2norm_sqr does.

                                                            val ssqr_diff' : ('a, 'b) t -> ('a, 'b) t -> 'a

                                                            ssqr_diff x y computes the sum of squared differences of every elements in x and its corresponding element in y.

                                                            val cross_entropy' : (float, 'a) t -> (float, 'a) t -> float

                                                            cross_entropy x y calculates the cross entropy between x and y using base e.

                                                            val clip_by_value : ?amin:'a -> ?amax:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            clip_by_value ~amin ~amax x clips the elements in x based on amin and amax. The elements smaller than amin will be set to amin, and the elements greater than amax will be set to amax.

                                                            val clip_by_l2norm : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            clip_by_l2norm t x clips the x according to the threshold set by t.

                                                            val fma : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            fma x y z calculates the `fused multiply add`, i.e. (x * y) + z.

                                                            Tensor Calculus
                                                            val contract1 : (int * int) array -> ('a, 'b) t -> ('a, 'b) t

                                                            contract1 index_pairs x performs indices contraction (a.k.a tensor contraction) on x. index_pairs is an array of contracted indices.

                                                            Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better performance.

                                                            val contract2 : (int * int) array -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            contract2 index_pairs x y performs indices contraction (a.k.a tensor contraction) on two ndarrays x and y. index_pairs is an array of contracted indices, the first element is the index of x, the second is that of y.

                                                            Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better performance.

                                                            Cast functions
                                                            val cast : ('a, 'b) kind -> ('c, 'd) t -> ('a, 'b) t

                                                            cast kind x casts x of type ('c, 'd) t to type ('a, 'b) t specify by the passed in kind parameter. This function is a generalisation of the other casting functions such as cast_s2d, cast_c2z, and etc.

                                                            val cast_s2d : + ('a, 'b) t

                                                            vecnorm ~axis ~p x calculates the generalised vector p-norm along the specified axis. The generalised p-norm is defined as below.

                                                            +  ||v||_p = \Big[ \sum_{k=0}^{N-1} |v_k|^p \Big]^{1/p}

                                                            Parameters: * axis is the axis for reduction. * p is order of norm, default value is 2. * x is the input ndarray.

                                                            Returns: * If p = infinity, then returns ||v||_{\infty} = \max_i(|v(i)|). * If p = -infinity, then returns ||v||_{-\infty} = \min_i(|v(i)|). * Otherwise returns generalised vector p-norm defined above.

                                                            val vecnorm' : ?p:float -> ('a, 'b) t -> 'a

                                                            vecnorm' flattens the input into 1-d vector first, then calculates the generalised p-norm the same as venorm.

                                                            val cumsum : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cumsum ~axis x : performs cumulative sum of the elements along the given axis ~axis. If ~axis is None, then the cumsum is performed along the lowest dimension. The returned result however always remains the same shape.

                                                            val cumprod : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cumprod ~axis x : similar to cumsum but performs cumulative product of the elements along the given ~axis.

                                                            val cummin : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cummin ~axis x : performs cumulative min along axis dimension.

                                                            val cummax : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cummax ~axis x : performs cumulative max along axis dimension.

                                                            val diff : ?axis:int -> ?n:int -> ('a, 'b) t -> ('a, 'b) t

                                                            diff ~axis ~n x calculates the n-th difference of x along the specified axis.

                                                            Parameters: * axis: axis to calculate the difference. The default value is the highest dimension. * n: how many times to calculate the difference. The default value is 1.

                                                            Return: * The difference ndarray y. Note that the shape of y 1 less than that of x along specified axis.

                                                            val angle : (Stdlib.Complex.t, 'a) t -> (Stdlib.Complex.t, 'a) t

                                                            angle x calculates the phase angle of all complex numbers in x.

                                                            val proj : (Stdlib.Complex.t, 'a) t -> (Stdlib.Complex.t, 'a) t

                                                            proj x computes the projection on Riemann sphere of all elelments in x.

                                                            val lgamma : ('a, 'b) t -> ('a, 'b) t

                                                            lgamma x computes the loggamma of the elements in x and returns the result in a new ndarray.

                                                            val dawsn : ('a, 'b) t -> ('a, 'b) t

                                                            dawsn x computes the Dawson function of the elements in x and returns the result in a new ndarray.

                                                            val i0 : ('a, 'b) t -> ('a, 'b) t

                                                            i0 x computes the modified Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val i0e : ('a, 'b) t -> ('a, 'b) t

                                                            i0e x computes the exponentially scaled modified Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val i1 : ('a, 'b) t -> ('a, 'b) t

                                                            i1 x computes the modified Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val i1e : ('a, 'b) t -> ('a, 'b) t

                                                            i1e x computes the exponentially scaled modified Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val iv : v:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            iv v x computes modified Bessel function of x of real order v

                                                            val scalar_iv : v:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_iv v x computes the modified Bessel function of x of real order v.

                                                            val iv_scalar : v:('a, 'b) t -> 'a -> ('a, 'b) t

                                                            iv_scalar v x computes modified Bessel function of x of real order v

                                                            val j0 : ('a, 'b) t -> ('a, 'b) t

                                                            j0 x computes the Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val j1 : ('a, 'b) t -> ('a, 'b) t

                                                            j1 x computes the Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val jv : v:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            jv v x computes Bessel function the first kind of x of real order v

                                                            val scalar_jv : v:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_jv v x computes the Bessel function of the first kind of x of real order v.

                                                            val jv_scalar : v:('a, 'b) t -> 'a -> ('a, 'b) t

                                                            jv_scalar v x computes Bessel function of the first kind of x of real order v

                                                            Binary math operators
                                                            val add : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            add x y adds all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            General broadcast operation is automatically applied to add/sub/mul/div, etc. The function compares the dimension element-wise from the highest to the lowest with the following broadcast rules (same as numpy): 1. equal; 2. either is 1.

                                                            val sub : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            sub x y subtracts all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val mul : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            mul x y multiplies all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val div : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            div x y divides all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val add_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            add_scalar x a adds a scalar value a to each element in x, and returns the result in a new ndarray.

                                                            val sub_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            sub_scalar x a subtracts a scalar value a from each element in x, and returns the result in a new ndarray.

                                                            val mul_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            mul_scalar x a multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val div_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            div_scalar x a divides each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_add : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_add a x adds a scalar value a to each element in x, and returns the result in a new ndarray.

                                                            val scalar_sub : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_sub a x subtracts each element in x from a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_mul : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_mul a x multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_div : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_div a x divides a scalar value a by each element in x, and returns the result in a new ndarray.

                                                            val pow : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            pow x y computes pow(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val scalar_pow : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_pow a x computes the power value of a scalar value a using the elements in a ndarray x.

                                                            val pow_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            pow_scalar x a computes each element in x power to a.

                                                            val atan2 : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            atan2 x y computes atan2(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val scalar_atan2 : float -> (float, 'a) t -> (float, 'a) t

                                                            scalar_atan2 a x

                                                            val atan2_scalar : (float, 'a) t -> float -> (float, 'a) t

                                                            scalar_atan2 x a

                                                            val hypot : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            hypot x y computes sqrt(x*x + y*y) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val min2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            min2 x y computes the minimum of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val max2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            max2 x y computes the maximum of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val fmod : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            fmod x y performs float mod division.

                                                            val fmod_scalar : (float, 'a) t -> float -> (float, 'a) t

                                                            fmod_scalar x a performs mod division between x and scalar a.

                                                            val scalar_fmod : float -> (float, 'a) t -> (float, 'a) t

                                                            scalar_fmod x a performs mod division between scalar a and x.

                                                            val ssqr' : ('a, 'b) t -> 'a -> 'a

                                                            ssqr x a computes the sum of squared differences of all the elements in x from constant a. This function only computes the square of each element rather than the conjugate transpose as l2norm_sqr does.

                                                            val ssqr_diff' : ('a, 'b) t -> ('a, 'b) t -> 'a

                                                            ssqr_diff x y computes the sum of squared differences of every elements in x and its corresponding element in y.

                                                            val cross_entropy' : (float, 'a) t -> (float, 'a) t -> float

                                                            cross_entropy x y calculates the cross entropy between x and y using base e.

                                                            val clip_by_value : ?amin:'a -> ?amax:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            clip_by_value ~amin ~amax x clips the elements in x based on amin and amax. The elements smaller than amin will be set to amin, and the elements greater than amax will be set to amax.

                                                            val clip_by_l2norm : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            clip_by_l2norm t x clips the x according to the threshold set by t.

                                                            val fma : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            fma x y z calculates the `fused multiply add`, i.e. (x * y) + z.

                                                            Tensor Calculus
                                                            val contract1 : (int * int) array -> ('a, 'b) t -> ('a, 'b) t

                                                            contract1 index_pairs x performs indices contraction (a.k.a tensor contraction) on x. index_pairs is an array of contracted indices.

                                                            Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better performance.

                                                            val contract2 : (int * int) array -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            contract2 index_pairs x y performs indices contraction (a.k.a tensor contraction) on two ndarrays x and y. index_pairs is an array of contracted indices, the first element is the index of x, the second is that of y.

                                                            Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better performance.

                                                            Cast functions
                                                            val cast : ('a, 'b) kind -> ('c, 'd) t -> ('a, 'b) t

                                                            cast kind x casts x of type ('c, 'd) t to type ('a, 'b) t specify by the passed in kind parameter. This function is a generalisation of the other casting functions such as cast_s2d, cast_c2z, and etc.

                                                            val cast_s2d : (float, Stdlib.Bigarray.float32_elt) t -> (float, Stdlib.Bigarray.float64_elt) t

                                                            cast_s2d x casts x from float32 to float64.

                                                            val cast_d2s : (float, Stdlib.Bigarray.float64_elt) t -> @@ -119,238 +143,238 @@ ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val conv2d : + ('a, 'b) t

                                                            conv1d ?padding input kernel strides applies a 1-dimensional convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the convolution.
                                                            val conv2d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val conv3d : + ('a, 'b) t

                                                            conv2d ?padding input kernel strides applies a 2-dimensional convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the convolution.
                                                            val conv3d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv1d : + ('a, 'b) t

                                                            conv3d ?padding input kernel strides applies a 3-dimensional convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the convolution.
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv2d : + ('a, 'b) t

                                                            dilated_conv1d ?padding input kernel strides dilations applies a 1-dimensional dilated convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. Returns the result of the dilated convolution.
                                                            val dilated_conv2d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv3d : + ('a, 'b) t

                                                            dilated_conv2d ?padding input kernel strides dilations applies a 2-dimensional dilated convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. Returns the result of the dilated convolution.
                                                            val dilated_conv3d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv1d : + ('a, 'b) t

                                                            dilated_conv3d ?padding input kernel strides dilations applies a 3-dimensional dilated convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. Returns the result of the dilated convolution.
                                                            val transpose_conv1d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv2d : + ('a, 'b) t

                                                            transpose_conv1d ?padding input kernel strides applies a 1-dimensional transposed convolution (deconvolution) over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the transposed convolution.
                                                            val transpose_conv2d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv3d : + ('a, 'b) t

                                                            transpose_conv2d ?padding input kernel strides applies a 2-dimensional transposed convolution (deconvolution) over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the transposed convolution.
                                                            val transpose_conv3d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool1d : + ('a, 'b) t

                                                            transpose_conv3d ?padding input kernel strides applies a 3-dimensional transposed convolution (deconvolution) over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the transposed convolution.
                                                            val max_pool1d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool2d : + ('a, 'b) t

                                                            max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the max pooling operation.
                                                            val max_pool2d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool3d : + ('a, 'b) t

                                                            max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the max pooling operation.
                                                            val max_pool3d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool1d : + ('a, 'b) t

                                                            max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the max pooling operation.
                                                            val avg_pool1d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool2d : + ('a, 'b) t

                                                            avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the average pooling operation.
                                                            val avg_pool2d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool3d : + ('a, 'b) t

                                                            avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the average pooling operation.
                                                            val avg_pool3d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool2d_argmax : + ('a, 'b) t

                                                            avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the average pooling operation.
                                                            val max_pool2d_argmax : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t * (int64, Stdlib.Bigarray.int64_elt) t

                                                            TODO

                                                            val upsampling2d : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            TODO

                                                            val conv1d_backward_input : + ('a, 'b) t * (int64, Stdlib.Bigarray.int64_elt) t

                                                            max_pool2d_argmax ?padding input pool_size strides applies a 2-dimensional max pooling operation over an input tensor, returning both the pooled output and the indices of the maximum values.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns a tuple containing the pooled output and the indices of the maximum values.
                                                            val upsampling2d : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            upsampling2d input size performs a 2-dimensional upsampling on the input tensor input, scaling it according to the specified size. Returns the upsampled tensor.

                                                            val conv1d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv1d_backward_kernel : + ('a, 'b) t

                                                            conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val conv1d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv2d_backward_input : + ('a, 'b) t

                                                            conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val conv2d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv2d_backward_kernel : + ('a, 'b) t

                                                            conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val conv2d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv3d_backward_input : + ('a, 'b) t

                                                            conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val conv3d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv3d_backward_kernel : + ('a, 'b) t

                                                            conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val conv3d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv1d_backward_input : + ('a, 'b) t

                                                            conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val dilated_conv1d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv1d_backward_kernel : + ('a, 'b) t

                                                            dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val dilated_conv1d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv2d_backward_input : + ('a, 'b) t

                                                            dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val dilated_conv2d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv2d_backward_kernel : + ('a, 'b) t

                                                            dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val dilated_conv2d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv3d_backward_input : + ('a, 'b) t

                                                            dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val dilated_conv3d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv3d_backward_kernel : + ('a, 'b) t

                                                            dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val dilated_conv3d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv1d_backward_input : + ('a, 'b) t

                                                            dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val transpose_conv1d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv1d_backward_kernel : + ('a, 'b) t

                                                            transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val transpose_conv1d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv2d_backward_input : + ('a, 'b) t

                                                            transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val transpose_conv2d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv2d_backward_kernel : + ('a, 'b) t

                                                            transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val transpose_conv2d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv3d_backward_input : + ('a, 'b) t

                                                            transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val transpose_conv3d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv3d_backward_kernel : + ('a, 'b) t

                                                            transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val transpose_conv3d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool1d_backward : + ('a, 'b) t

                                                            transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val max_pool1d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool2d_backward : + ('a, 'b) t

                                                            max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional max pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val max_pool2d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool3d_backward : + ('a, 'b) t

                                                            max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional max pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val max_pool3d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool1d_backward : + ('a, 'b) t

                                                            max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional max pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val avg_pool1d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool2d_backward : + ('a, 'b) t

                                                            avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional average pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val avg_pool2d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool3d_backward : + ('a, 'b) t

                                                            avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional average pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val avg_pool3d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val upsampling2d_backward : ('a, 'b) t -> int array -> ('a, 'b) t -> ('a, 'b) t

                                                            TODO

                                                            Helper functions

                                                            The following functions are helper functions for some other functions in both Ndarray and Ndview modules. In general, you are not supposed to use these functions directly.

                                                            val print_element : ('a, 'b) kind -> 'a -> unit

                                                            print_element kind a prints the value of a single element.

                                                            val print_index : int array -> unit

                                                            print_index i prints out the index of an element.

                                                            val _check_transpose_axis : int array -> int -> unit

                                                            _check_transpose_axis a d checks whether a is a legiti('a, 'b) te transpose index.

                                                            val one_hot : int -> ('a, 'b) t -> ('a, 'b) t

                                                            one_hot idx depth creates one-hot vectors according to the indices ndarray and the specified depth. If idx is rank N, then the return is rank N+1. More specifically, if idx is of shape [|a;b;c|], the return is of shape [|a;b;c;depth|].

                                                            val sum_slices : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            sum_slices ~axis:2 x for x of [|2;3;4;5|], it returns an ndarray of shape [|4;5|]. Currently, the operation is done using gemm, it is fast but consumes more memory.

                                                            val slide : + ('a, 'b) t

                                                            avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional average pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val upsampling2d_backward : ('a, 'b) t -> int array -> ('a, 'b) t -> ('a, 'b) t

                                                            upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional upsampling layer.

                                                            • input is the original input tensor.
                                                            • size specifies the upsampling factors for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            Helper functions

                                                            The following functions are helper functions for some other functions in both Ndarray and Ndview modules. In general, you are not supposed to use these functions directly.

                                                            val print_element : ('a, 'b) kind -> 'a -> unit

                                                            print_element kind a prints the value of a single element.

                                                            val print_index : int array -> unit

                                                            print_index i prints out the index of an element.

                                                            val _check_transpose_axis : int array -> int -> unit

                                                            _check_transpose_axis a d checks whether a is a legiti('a, 'b) te transpose index.

                                                            val one_hot : int -> ('a, 'b) t -> ('a, 'b) t

                                                            one_hot idx depth creates one-hot vectors according to the indices ndarray and the specified depth. If idx is rank N, then the return is rank N+1. More specifically, if idx is of shape [|a;b;c|], the return is of shape [|a;b;c;depth|].

                                                            val sum_slices : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            sum_slices ~axis:2 x for x of [|2;3;4;5|], it returns an ndarray of shape [|4;5|]. Currently, the operation is done using gemm, it is fast but consumes more memory.

                                                            val slide : ?axis:int -> ?ofs:int -> ?step:int -> window:int -> ('a, 'b) t -> - ('a, 'b) t

                                                            slide ~axis ~window x generates a new ndarray by sliding a window along specified axis in x. E.g., if x has shape [|a;b;c|] and axis = 1, then [|a; number of windows; window; c|] is the shape of the returned ndarray.

                                                            Parameters: * axis is the axis for sliding, the default is -1, i.e. highest dimension. * ofs is the starting position of the sliding window. The default is 0. * step is the step size, the default is 1. * window is the size of the sliding window.

                                                            In-place modification
                                                            val create_ : out:('a, 'b) t -> 'a -> unit

                                                            TODO

                                                            val uniform_ : ?a:'a -> ?b:'a -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val gaussian_ : ?mu:'a -> ?sigma:'a -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val poisson_ : mu:float -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val sequential_ : ?a:'a -> ?step:'a -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val bernoulli_ : ?p:float -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val zeros_ : out:('a, 'b) t -> unit

                                                            TODO

                                                            val ones_ : out:('a, 'b) t -> unit

                                                            TODO

                                                            val one_hot_ : out:('a, 'b) t -> int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val sort_ : ('a, 'b) t -> unit

                                                            sort_ x performs in-place quicksort of the elelments in x.

                                                            val get_fancy_ : out:('a, 'b) t -> Owl_types.index list -> ('a, 'b) t -> unit

                                                            TODO

                                                            val set_fancy_ : + ('a, 'b) t

                                                            slide ~axis ~window x generates a new ndarray by sliding a window along specified axis in x. E.g., if x has shape [|a;b;c|] and axis = 1, then [|a; number of windows; window; c|] is the shape of the returned ndarray.

                                                            Parameters: * axis is the axis for sliding, the default is -1, i.e. highest dimension. * ofs is the starting position of the sliding window. The default is 0. * step is the step size, the default is 1. * window is the size of the sliding window.

                                                            In-place modification
                                                            val create_ : out:('a, 'b) t -> 'a -> unit

                                                            create_ ~out value initializes the matrix out in-place with the scalar value value. This operation modifies the contents of out.

                                                            val uniform_ : ?a:'a -> ?b:'a -> out:('a, 'b) t -> unit

                                                            uniform_ ?a ?b ~out fills the matrix out in-place with random values drawn from a uniform distribution over the interval [a, b\). If a and b are not provided, the default interval is [0, 1\).

                                                            val gaussian_ : ?mu:'a -> ?sigma:'a -> out:('a, 'b) t -> unit

                                                            gaussian_ ?mu ?sigma ~out fills the matrix out in-place with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1.

                                                            val poisson_ : mu:float -> out:('a, 'b) t -> unit

                                                            poisson_ ~mu ~out fills the matrix out in-place with random values drawn from a Poisson distribution with mean mu.

                                                            val sequential_ : ?a:'a -> ?step:'a -> out:('a, 'b) t -> unit

                                                            sequential_ ?a ?step ~out fills the matrix out in-place with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1.

                                                            val bernoulli_ : ?p:float -> out:('a, 'b) t -> unit

                                                            bernoulli_ ?p ~out fills the matrix out in-place with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5.

                                                            val zeros_ : out:('a, 'b) t -> unit

                                                            zeros_ ~out fills the matrix out in-place with zeros.

                                                            val ones_ : out:('a, 'b) t -> unit

                                                            ones_ ~out fills the matrix out in-place with ones.

                                                            val one_hot_ : out:('a, 'b) t -> int -> ('a, 'b) t -> unit

                                                            one_hot_ ~out depth indices fills the matrix out in-place with one-hot encoded vectors according to the specified depth and the indices.

                                                            val sort_ : ('a, 'b) t -> unit

                                                            sort_ x performs in-place quicksort on the elements in x, sorting them in ascending order.

                                                            val get_fancy_ : out:('a, 'b) t -> Owl_types.index list -> ('a, 'b) t -> unit

                                                            get_fancy_ ~out indices src extracts elements from the source matrix src according to the list of indices and stores them in out. This operation is performed in-place on out.

                                                            val set_fancy_ : out:('a, 'b) t -> Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val get_slice_ : out:('a, 'b) t -> int list list -> ('a, 'b) t -> unit

                                                            TODO

                                                            val set_slice_ : + unit

                                                            set_fancy_ ~out indices src sets the elements in out at the positions specified by indices with the values from the source matrix src. This operation is performed in-place on out.

                                                            val get_slice_ : out:('a, 'b) t -> int list list -> ('a, 'b) t -> unit

                                                            get_slice_ ~out slices src extracts a slice from the source matrix src according to the list of slices and stores it in out. This operation is performed in-place on out.

                                                            val set_slice_ : out:('a, 'b) t -> int list list -> ('a, 'b) t -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            copy_ ~out src copies the data from ndarray src to destination out.

                                                            val reshape_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            TODO

                                                            val reverse_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            TODO

                                                            val transpose_ : out:('a, 'b) t -> ?axis:int array -> ('a, 'b) t -> unit

                                                            transpose_ ~out x is similar to transpose x but the output is written to out.

                                                            val repeat_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            repeat_ ~out x reps is similar to repeat x reps but the output is written to out.

                                                            val tile_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            tile_ ~out x reps is similar to tile x reps but the output is written to out.

                                                            val pad_ : out:('a, 'b) t -> ?v:'a -> int list list -> ('a, 'b) t -> unit

                                                            pad_ ~out ?v p x is similar to pad ?v p x but the output is written to out.

                                                            val sum_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val min_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val max_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            add_ x y is similar to add function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            sub_ x y is similar to sub function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            mul_ x y is similar to mul function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            div_ x y is similar to div function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val pow_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            pow_ x y is similar to pow function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val atan2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            atan2_ x y is similar to atan2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val hypot_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            hypot_ x y is similar to hypot function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val fmod_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fmod_ x y is similar to fmod function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val min2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            min2_ x y is similar to min2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val max2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            max2_ x y is similar to max2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            add_scalar_ x y is similar to add_scalar function but the output is written to x.

                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            sub_scalar_ x y is similar to sub_scalar function but the output is written to x.

                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            mul_scalar_ x y is similar to mul_scalar function but the output is written to x.

                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            div_scalar_ x y is similar to div_scalar function but the output is written to x.

                                                            val pow_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            pow_scalar_ x y is similar to pow_scalar function but the output is written to x.

                                                            val atan2_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            atan2_scalar_ x y is similar to atan2_scalar function but the output is written to x.

                                                            val fmod_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            fmod_scalar_ x y is similar to fmod_scalar function but the output is written to x.

                                                            val scalar_add_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_add_ a x is similar to scalar_add function but the output is written to x.

                                                            val scalar_sub_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_sub_ a x is similar to scalar_sub function but the output is written to x.

                                                            val scalar_mul_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_mul_ a x is similar to scalar_mul function but the output is written to x.

                                                            val scalar_div_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_div_ a x is similar to scalar_div function but the output is written to x.

                                                            val scalar_pow_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_pow_ a x is similar to scalar_pow function but the output is written to x.

                                                            val scalar_atan2_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_atan2_ a x is similar to scalar_atan2 function but the output is written to x.

                                                            val scalar_fmod_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_fmod_ a x is similar to scalar_fmod function but the output is written to x.

                                                            val clip_by_value_ : + unit

                                                            set_slice_ ~out slices src sets the slice in out defined by slices with the values from the source matrix src. This operation is performed in-place on out.

                                                            val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            copy_ ~out src copies the data from the source matrix src to the destination matrix out. This operation is performed in-place on out.

                                                            val reshape_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            reshape_ ~out src reshapes the source matrix src and stores the result in out. The total number of elements must remain the same. This operation is performed in-place on out.

                                                            val reverse_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            reverse_ ~out src reverses the elements of the source matrix src along each dimension and stores the result in out. This operation is performed in-place on out.

                                                            val transpose_ : out:('a, 'b) t -> ?axis:int array -> ('a, 'b) t -> unit

                                                            transpose_ ~out x is similar to transpose x but the output is written to out.

                                                            val repeat_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            repeat_ ~out x reps is similar to repeat x reps but the output is written to out.

                                                            val tile_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            tile_ ~out x reps is similar to tile x reps but the output is written to out.

                                                            val pad_ : out:('a, 'b) t -> ?v:'a -> int list list -> ('a, 'b) t -> unit

                                                            pad_ ~out ?v p x is similar to pad ?v p x but the output is written to out.

                                                            val sum_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            sum_ ~out ~axis x computes the sum of elements along the specified axis of the array x and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • axis specifies the axis along which to compute the sum. This operation is performed in-place on out.
                                                            val min_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            min_ ~out ~axis x computes the minimum value along the specified axis of the array x and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • axis specifies the axis along which to compute the minimum value. This operation is performed in-place on out.
                                                            val max_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            max_ ~out ~axis x computes the maximum value along the specified axis of the array x and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • axis specifies the axis along which to compute the maximum value. This operation is performed in-place on out.
                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            add_ x y is similar to add function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            sub_ x y is similar to sub function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            mul_ x y is similar to mul function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            div_ x y is similar to div function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val pow_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            pow_ x y is similar to pow function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val atan2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            atan2_ x y is similar to atan2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val hypot_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            hypot_ x y is similar to hypot function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val fmod_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fmod_ x y is similar to fmod function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val min2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            min2_ x y is similar to min2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val max2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            max2_ x y is similar to max2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            add_scalar_ x y is similar to add_scalar function but the output is written to x.

                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            sub_scalar_ x y is similar to sub_scalar function but the output is written to x.

                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            mul_scalar_ x y is similar to mul_scalar function but the output is written to x.

                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            div_scalar_ x y is similar to div_scalar function but the output is written to x.

                                                            val pow_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            pow_scalar_ x y is similar to pow_scalar function but the output is written to x.

                                                            val atan2_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            atan2_scalar_ x y is similar to atan2_scalar function but the output is written to x.

                                                            val fmod_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            fmod_scalar_ x y is similar to fmod_scalar function but the output is written to x.

                                                            val scalar_add_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_add_ a x is similar to scalar_add function but the output is written to x.

                                                            val scalar_sub_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_sub_ a x is similar to scalar_sub function but the output is written to x.

                                                            val scalar_mul_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_mul_ a x is similar to scalar_mul function but the output is written to x.

                                                            val scalar_div_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_div_ a x is similar to scalar_div function but the output is written to x.

                                                            val scalar_pow_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_pow_ a x is similar to scalar_pow function but the output is written to x.

                                                            val scalar_atan2_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_atan2_ a x is similar to scalar_atan2 function but the output is written to x.

                                                            val scalar_fmod_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_fmod_ a x is similar to scalar_fmod function but the output is written to x.

                                                            val clip_by_value_ : ?out:('a, 'b) t -> ?amin:'a -> ?amax:'a -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val clip_by_l2norm_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            TODO

                                                            val fma_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fma_ ~out x y z is similar to fma x y z function but the output is written to out.

                                                            val dot_ : + unit

                                                            clip_by_value_ ?out ?amin ?amax x clips the values of the array x to lie within the range amin, amax and stores the result in out.

                                                            • out is the optional output array where the result will be stored. If not provided, x is modified in-place.
                                                            • amin is the optional minimum value to clip to. If not provided, no minimum clipping is applied.
                                                            • amax is the optional maximum value to clip to. If not provided, no maximum clipping is applied. This operation is performed in-place.
                                                            val clip_by_l2norm_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            clip_by_l2norm_ ?out l2norm x clips the L2 norm of the array x to the specified value l2norm and stores the result in out.

                                                            • out is the optional output array where the result will be stored. If not provided, x is modified in-place.
                                                            • l2norm specifies the maximum L2 norm. This operation is performed in-place.
                                                            val fma_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fma_ ~out x y z is similar to fma x y z function but the output is written to out.

                                                            val dot_ : ?transa:bool -> ?transb:bool -> ?alpha:'a -> @@ -364,255 +388,255 @@ ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val conv2d_ : + unit

                                                            conv1d_ ~out ?padding input kernel strides applies a 1-dimensional convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val conv2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val conv3d_ : + unit

                                                            conv2d_ ~out ?padding input kernel strides applies a 2-dimensional convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val conv3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val dilated_conv1d_ : + unit

                                                            conv3d_ ~out ?padding input kernel strides applies a 3-dimensional convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val dilated_conv1d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val dilated_conv2d_ : + unit

                                                            dilated_conv1d_ ~out ?padding input kernel strides dilations applies a 1-dimensional dilated convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. This operation is performed in-place on out.
                                                            val dilated_conv2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val dilated_conv3d_ : + unit

                                                            dilated_conv2d_ ~out ?padding input kernel strides dilations applies a 2-dimensional dilated convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. This operation is performed in-place on out.
                                                            val dilated_conv3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val transpose_conv1d_ : + unit

                                                            dilated_conv3d_ ~out ?padding input kernel strides dilations applies a 3-dimensional dilated convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. This operation is performed in-place on out.
                                                            val transpose_conv1d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val transpose_conv2d_ : + unit

                                                            transpose_conv1d_ ~out ?padding input kernel strides applies a 1-dimensional transposed convolution (deconvolution) over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the transposed convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val transpose_conv2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val transpose_conv3d_ : + unit

                                                            transpose_conv2d_ ~out ?padding input kernel strides applies a 2-dimensional transposed convolution (deconvolution) over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the transposed convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val transpose_conv3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val max_pool1d_ : + unit

                                                            transpose_conv3d_ ~out ?padding input kernel strides applies a 3-dimensional transposed convolution (deconvolution) over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the transposed convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val max_pool1d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val max_pool2d_ : + unit

                                                            max_pool1d_ ~out ?padding input pool_size strides applies a 1-dimensional max pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val max_pool2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val max_pool3d_ : + unit

                                                            max_pool2d_ ~out ?padding input pool_size strides applies a 2-dimensional max pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val max_pool3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val avg_pool1d_ : + unit

                                                            max_pool3d_ ~out ?padding input pool_size strides applies a 3-dimensional max pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val avg_pool1d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val avg_pool2d_ : + unit

                                                            avg_pool1d_ ~out ?padding input pool_size strides applies a 1-dimensional average pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val avg_pool2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val avg_pool3d_ : + unit

                                                            avg_pool2d_ ~out ?padding input pool_size strides applies a 2-dimensional average pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val avg_pool3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val upsampling2d_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            TODO

                                                            val conv1d_backward_input_ : + unit

                                                            avg_pool3d_ ~out ?padding input pool_size strides applies a 3-dimensional average pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val upsampling2d_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            upsampling2d_ ~out input size performs a 2-dimensional upsampling on the input tensor input, scaling it according to the specified size, and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • input is the input tensor to be upsampled.
                                                            • size specifies the upsampling factors for each dimension. This operation is performed in-place on out.
                                                            val conv1d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv1d_backward_kernel_ : + unit

                                                            conv1d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv1d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv2d_backward_input_ : + unit

                                                            conv1d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv2d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv2d_backward_kernel_ : + unit

                                                            conv2d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv2d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv3d_backward_input_ : + unit

                                                            conv2d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv3d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv3d_backward_kernel_ : + unit

                                                            conv3d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv3d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv1d_backward_input_ : + unit

                                                            conv3d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv1d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv1d_backward_kernel_ : + unit

                                                            dilated_conv1d_backward_input_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv1d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv2d_backward_input_ : + unit

                                                            dilated_conv1d_backward_kernel_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv2d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv2d_backward_kernel_ : + unit

                                                            dilated_conv2d_backward_input_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv2d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv3d_backward_input_ : + unit

                                                            dilated_conv2d_backward_kernel_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv3d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv3d_backward_kernel_ : + unit

                                                            dilated_conv3d_backward_input_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv3d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv1d_backward_input_ : + unit

                                                            dilated_conv3d_backward_kernel_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv1d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv1d_backward_kernel_ : + unit

                                                            transpose_conv1d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv1d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv2d_backward_input_ : + unit

                                                            transpose_conv1d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv2d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv2d_backward_kernel_ : + unit

                                                            transpose_conv2d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv2d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv3d_backward_input_ : + unit

                                                            transpose_conv2d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv3d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv3d_backward_kernel_ : + unit

                                                            transpose_conv3d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv3d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val max_pool1d_backward_ : + unit

                                                            transpose_conv3d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val max_pool1d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val max_pool2d_backward_ : + unit

                                                            max_pool1d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional max pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. This operation is performed in-place on out.
                                                            val max_pool2d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val max_pool3d_backward_ : + unit

                                                            max_pool2d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional max pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. This operation is performed in-place on out.
                                                            val max_pool3d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val avg_pool1d_backward_ : + unit

                                                            max_pool3d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional max pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. This operation is performed in-place on out.
                                                            val avg_pool1d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val avg_pool2d_backward_ : + unit

                                                            avg_pool1d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional average pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. This operation is performed in-place on out.
                                                            val avg_pool2d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val avg_pool3d_backward_ : + unit

                                                            avg_pool2d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional average pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. This operation is performed in-place on out.
                                                            val avg_pool3d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val upsampling2d_backward_ : + unit

                                                            avg_pool3d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional average pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. This operation is performed in-place on out.
                                                            val upsampling2d_backward_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val fused_adagrad_ : ?out:('a, 'b) t -> rate:'a -> eps:'a -> ('a, 'b) t -> unit

                                                            TODO

                                                            Matrix functions
                                                            type area = Owl_dense_ndarray_generic.area = {
                                                            1. a : int;
                                                            2. b : int;
                                                            3. c : int;
                                                            4. d : int;
                                                            }

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val area : int -> int -> int -> int -> area

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_area_to : ('a, 'b) t -> area -> ('a, 'b) t -> area -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val row_num : ('a, 'b) t -> int

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val col_num : ('a, 'b) t -> int

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val row : ('a, 'b) t -> int -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val col : ('a, 'b) t -> int -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val rows : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val cols : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_row_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_col_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val dot : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val diag : ?k:int -> ('a, 'b) t -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val trace : ('a, 'b) t -> 'a

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_rows : ('a, 'b) t -> ('a, 'b) t array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_rows : ('a, 'b) t array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_cols : ('a, 'b) t -> ('a, 'b) t array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_cols : ('a, 'b) t array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_arrays : ('a, 'b) t -> 'a array array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_arrays : ('a, 'b) kind -> 'a array array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val draw_rows : + unit

                                                            upsampling2d_backward_ ~out input size grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional upsampling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • size specifies the upsampling factors for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the upsampling layer. This operation is performed in-place on out.
                                                            val fused_adagrad_ : ?out:('a, 'b) t -> rate:'a -> eps:'a -> ('a, 'b) t -> unit

                                                            fused_adagrad_ ?out ~rate ~eps grad applies the Adagrad optimization algorithm to the gradients grad with a given learning rate and epsilon eps for numerical stability, storing the result in out.

                                                            • out is the optional output array where the updated parameters will be stored. If not provided, grad is modified in-place.
                                                            • rate specifies the learning rate.
                                                            • eps specifies the epsilon value for numerical stability. This operation is performed in-place.
                                                            Matrix functions
                                                            type area = Owl_dense_ndarray_generic.area = {
                                                            1. a : int;
                                                            2. b : int;
                                                            3. c : int;
                                                            4. d : int;
                                                            }

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val area : int -> int -> int -> int -> area

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_area_to : ('a, 'b) t -> area -> ('a, 'b) t -> area -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val row_num : ('a, 'b) t -> int

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val col_num : ('a, 'b) t -> int

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val row : ('a, 'b) t -> int -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val col : ('a, 'b) t -> int -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val rows : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val cols : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_row_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_col_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val dot : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val diag : ?k:int -> ('a, 'b) t -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val trace : ('a, 'b) t -> 'a

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_rows : ('a, 'b) t -> ('a, 'b) t array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_rows : ('a, 'b) t array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_cols : ('a, 'b) t -> ('a, 'b) t array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_cols : ('a, 'b) t array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_arrays : ('a, 'b) t -> 'a array array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_arrays : ('a, 'b) kind -> 'a array array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val draw_rows : ?replacement:bool -> ('a, 'b) t -> int -> diff --git a/docs/owl/Owl_dense_ndarray/Operator/index.html b/docs/owl/Owl_dense_ndarray/Operator/index.html index c88261795..26979ea94 100644 --- a/docs/owl/Owl_dense_ndarray/Operator/index.html +++ b/docs/owl/Owl_dense_ndarray/Operator/index.html @@ -1,5 +1,5 @@ -Operator (owl.Owl_dense_ndarray.Operator)

                                                            Module Owl_dense_ndarray.Operator

                                                            include sig ... end
                                                            val (+) : +Operator (owl.Owl_dense_ndarray.Operator)

                                                            Module Owl_dense_ndarray.Operator

                                                            include sig ... end
                                                            val (-) : diff --git a/docs/owl/Owl_dense_ndarray/S/index.html b/docs/owl/Owl_dense_ndarray/S/index.html index b1149875d..532903ece 100644 --- a/docs/owl/Owl_dense_ndarray/S/index.html +++ b/docs/owl/Owl_dense_ndarray/S/index.html @@ -1,5 +1,5 @@ -S (owl.Owl_dense_ndarray.S)

                                                            Module Owl_dense_ndarray.S

                                                            include module type of struct include Owl_dense_ndarray_s end
                                                            type elt = float
                                                            type arr = +S (owl.Owl_dense_ndarray.S)

                                                            Module Owl_dense_ndarray.S

                                                            include module type of struct include Owl_dense_ndarray_s end
                                                            type elt = float
                                                            type arr = (float, Stdlib.Bigarray.float32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            include Owl_dense_ndarray_intf.Common with type elt := elt and type arr := arr
                                                            include Owl_base_dense_ndarray_intf.Common with type elt := elt diff --git a/docs/owl/Owl_dense_ndarray/Z/index.html b/docs/owl/Owl_dense_ndarray/Z/index.html index be31721f9..238c8c75b 100644 --- a/docs/owl/Owl_dense_ndarray/Z/index.html +++ b/docs/owl/Owl_dense_ndarray/Z/index.html @@ -1,5 +1,5 @@ -Z (owl.Owl_dense_ndarray.Z)

                                                            Module Owl_dense_ndarray.Z

                                                            include module type of struct include Owl_dense_ndarray_z end
                                                            type elt = Stdlib.Complex.t
                                                            type arr = +Z (owl.Owl_dense_ndarray.Z)

                                                            Module Owl_dense_ndarray.Z

                                                            include module type of struct include Owl_dense_ndarray_z end
                                                            type elt = Stdlib.Complex.t
                                                            type arr = (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            type cast_arr = (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout) diff --git a/docs/owl/Owl_dense_ndarray/index.html b/docs/owl/Owl_dense_ndarray/index.html index 51a8a1b4c..71733fc05 100644 --- a/docs/owl/Owl_dense_ndarray/index.html +++ b/docs/owl/Owl_dense_ndarray/index.html @@ -1,2 +1,19 @@ -Owl_dense_ndarray (owl.Owl_dense_ndarray)

                                                            Module Owl_dense_ndarray

                                                            Ndarray: module aliases

                                                            module Operator : sig ... end
                                                            module Generic : sig ... end
                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            module C : sig ... end
                                                            module Z : sig ... end
                                                            module Any : sig ... end
                                                            +Owl_dense_ndarray (owl.Owl_dense_ndarray)

                                                            Module Owl_dense_ndarray

                                                            Ndarray: module aliases

                                                            module Operator : sig ... end
                                                            module Generic : sig ... end
                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            module C : sig ... end
                                                            module Z : sig ... end
                                                            module Any : sig ... end
                                                            diff --git a/docs/owl/Owl_dense_ndarray_a/index.html b/docs/owl/Owl_dense_ndarray_a/index.html index 996f770ec..5cc5735e6 100644 --- a/docs/owl/Owl_dense_ndarray_a/index.html +++ b/docs/owl/Owl_dense_ndarray_a/index.html @@ -1,5 +1,5 @@ -Owl_dense_ndarray_a (owl.Owl_dense_ndarray_a)

                                                            Module Owl_dense_ndarray_a

                                                            type 'a arr = {
                                                            1. mutable shape : int array;
                                                            2. mutable stride : int array;
                                                            3. mutable data : 'a array;
                                                            }
                                                            Create N-dimensional array
                                                            val create : int array -> 'a -> 'a arr
                                                            val init : int array -> (int -> 'a) -> 'a arr
                                                            val init_nd : int array -> (int array -> 'a) -> 'a arr
                                                            val sequential : ?a:float -> ?step:float -> int array -> float arr
                                                            val zeros : int array -> float arr
                                                            val ones : int array -> float arr
                                                            Obtain basic properties
                                                            val shape : 'a arr -> int array
                                                            val num_dims : 'a arr -> int
                                                            val nth_dim : 'a arr -> int -> int
                                                            val numel : 'a arr -> int
                                                            val same_shape : 'a arr -> 'a arr -> bool
                                                            val strides : 'a arr -> int array
                                                            val slice_size : 'a arr -> int array
                                                            val index_1d_nd : int -> int array -> int array
                                                            val index_nd_1d : int array -> int array -> int
                                                            Manipulate a N-dimensional array
                                                            val get : 'a arr -> int array -> 'a
                                                            val set : 'a arr -> int array -> 'a -> unit
                                                            val get_index : 'a arr -> int array array -> 'a array
                                                            val set_index : 'a arr -> int array array -> 'a array -> unit
                                                            val get_fancy : Owl_types.index list -> 'a arr -> 'a arr
                                                            val set_fancy : Owl_types.index list -> 'a arr -> 'a arr -> unit
                                                            val get_slice : int list list -> 'a arr -> 'a arr
                                                            val set_slice : int list list -> 'a arr -> 'a arr -> unit
                                                            val fill : 'a arr -> 'a -> unit
                                                            val copy_ : out:'a arr -> 'a arr -> unit
                                                            val copy : 'a arr -> 'a arr
                                                            val reshape : 'a arr -> int array -> 'a arr
                                                            val flatten : 'a arr -> 'a arr
                                                            val sub_left : 'a arr -> int array -> 'a arr
                                                            val squeeze : ?axis:int array -> 'a arr -> 'a arr
                                                            val expand : ?hi:bool -> 'a arr -> int -> 'a arr
                                                            val reverse : 'a arr -> 'a arr
                                                            val transpose : ?axis:int array -> 'a arr -> 'a arr
                                                            val swap : int -> int -> 'a arr -> 'a arr
                                                            val repeat : 'a arr -> int array -> 'a arr
                                                            val tile : 'a arr -> int array -> 'a arr
                                                            val concatenate : ?axis:int -> 'a arr array -> 'a arr
                                                            val pad : 'a -> int list list -> 'a arr -> 'a arr
                                                            Iterate array elements
                                                            val iter : ('a -> unit) -> 'a arr -> unit
                                                            val iteri : (int -> 'a -> unit) -> 'a arr -> unit
                                                            val map : ('a -> 'b) -> 'a arr -> 'b arr
                                                            val mapi : (int -> 'a -> 'b) -> 'a arr -> 'b arr
                                                            val filter : ('a -> bool) -> 'a arr -> int array
                                                            val filteri : (int -> 'a -> bool) -> 'a arr -> int array
                                                            val fold : ('a -> 'b -> 'a) -> 'a -> 'b arr -> 'a
                                                            val foldi : (int -> 'a -> 'b -> 'a) -> 'a -> 'b arr -> 'a
                                                            val iter2 : ('a -> 'b -> unit) -> 'a arr -> 'b arr -> unit
                                                            val iter2i : (int -> 'a -> 'b -> unit) -> 'a arr -> 'b arr -> unit
                                                            val map2 : ('a -> 'b -> 'c) -> 'a arr -> 'b arr -> 'c arr
                                                            val map2i : (int -> 'a -> 'b -> 'c) -> 'a arr -> 'b arr -> 'c arr
                                                            Examine array elements or compare two arrays
                                                            val exists : ('a -> bool) -> 'a arr -> bool
                                                            val not_exists : ('a -> bool) -> 'a arr -> bool
                                                            val for_all : ('a -> bool) -> 'a arr -> bool
                                                            val is_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val not_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val greater : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val less : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val greater_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val less_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val elt_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_not_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_greater : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_less : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_greater_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_less_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_not_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_greater_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_less_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_greater_equal_scalar : +Owl_dense_ndarray_a (owl.Owl_dense_ndarray_a)

                                                            Module Owl_dense_ndarray_a

                                                            type 'a arr = {
                                                            1. mutable shape : int array;
                                                            2. mutable stride : int array;
                                                            3. mutable data : 'a array;
                                                            }
                                                            Create N-dimensional array
                                                            val create : int array -> 'a -> 'a arr
                                                            val init : int array -> (int -> 'a) -> 'a arr
                                                            val init_nd : int array -> (int array -> 'a) -> 'a arr
                                                            val sequential : ?a:float -> ?step:float -> int array -> float arr
                                                            val zeros : int array -> float arr
                                                            val ones : int array -> float arr
                                                            Obtain basic properties
                                                            val shape : 'a arr -> int array
                                                            val num_dims : 'a arr -> int
                                                            val nth_dim : 'a arr -> int -> int
                                                            val numel : 'a arr -> int
                                                            val same_shape : 'a arr -> 'a arr -> bool
                                                            val strides : 'a arr -> int array
                                                            val slice_size : 'a arr -> int array
                                                            val index_1d_nd : int -> int array -> int array
                                                            val index_nd_1d : int array -> int array -> int
                                                            Manipulate a N-dimensional array
                                                            val get : 'a arr -> int array -> 'a
                                                            val set : 'a arr -> int array -> 'a -> unit
                                                            val get_index : 'a arr -> int array array -> 'a array
                                                            val set_index : 'a arr -> int array array -> 'a array -> unit
                                                            val get_fancy : Owl_types.index list -> 'a arr -> 'a arr
                                                            val set_fancy : Owl_types.index list -> 'a arr -> 'a arr -> unit
                                                            val get_slice : int list list -> 'a arr -> 'a arr
                                                            val set_slice : int list list -> 'a arr -> 'a arr -> unit
                                                            val fill : 'a arr -> 'a -> unit
                                                            val copy_ : out:'a arr -> 'a arr -> unit
                                                            val copy : 'a arr -> 'a arr
                                                            val reshape : 'a arr -> int array -> 'a arr
                                                            val flatten : 'a arr -> 'a arr
                                                            val sub_left : 'a arr -> int array -> 'a arr
                                                            val squeeze : ?axis:int array -> 'a arr -> 'a arr
                                                            val expand : ?hi:bool -> 'a arr -> int -> 'a arr
                                                            val reverse : 'a arr -> 'a arr
                                                            val transpose : ?axis:int array -> 'a arr -> 'a arr
                                                            val swap : int -> int -> 'a arr -> 'a arr
                                                            val repeat : 'a arr -> int array -> 'a arr
                                                            val tile : 'a arr -> int array -> 'a arr
                                                            val concatenate : ?axis:int -> 'a arr array -> 'a arr
                                                            val pad : 'a -> int list list -> 'a arr -> 'a arr
                                                            Iterate array elements
                                                            val iter : ('a -> unit) -> 'a arr -> unit
                                                            val iteri : (int -> 'a -> unit) -> 'a arr -> unit
                                                            val map : ('a -> 'b) -> 'a arr -> 'b arr
                                                            val mapi : (int -> 'a -> 'b) -> 'a arr -> 'b arr
                                                            val filter : ('a -> bool) -> 'a arr -> int array
                                                            val filteri : (int -> 'a -> bool) -> 'a arr -> int array
                                                            val fold : ('a -> 'b -> 'a) -> 'a -> 'b arr -> 'a
                                                            val foldi : (int -> 'a -> 'b -> 'a) -> 'a -> 'b arr -> 'a
                                                            val iter2 : ('a -> 'b -> unit) -> 'a arr -> 'b arr -> unit
                                                            val iter2i : (int -> 'a -> 'b -> unit) -> 'a arr -> 'b arr -> unit
                                                            val map2 : ('a -> 'b -> 'c) -> 'a arr -> 'b arr -> 'c arr
                                                            val map2i : (int -> 'a -> 'b -> 'c) -> 'a arr -> 'b arr -> 'c arr
                                                            Examine array elements or compare two arrays
                                                            val exists : ('a -> bool) -> 'a arr -> bool
                                                            val not_exists : ('a -> bool) -> 'a arr -> bool
                                                            val for_all : ('a -> bool) -> 'a arr -> bool
                                                            val is_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val not_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val greater : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val less : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val greater_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val less_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool
                                                            val elt_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_not_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_greater : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_less : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_greater_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_less_equal : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a arr -> bool arr
                                                            val elt_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_not_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_greater_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_less_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> bool arr
                                                            val elt_greater_equal_scalar : ?cmp:('a -> 'a -> int) -> 'a arr -> 'a -> diff --git a/docs/owl/Owl_dense_ndarray_c/index.html b/docs/owl/Owl_dense_ndarray_c/index.html index 770150033..ac416d352 100644 --- a/docs/owl/Owl_dense_ndarray_c/index.html +++ b/docs/owl/Owl_dense_ndarray_c/index.html @@ -1,5 +1,5 @@ -Owl_dense_ndarray_c (owl.Owl_dense_ndarray_c)

                                                            Module Owl_dense_ndarray_c

                                                            type elt = Stdlib.Complex.t
                                                            type arr = +Owl_dense_ndarray_c (owl.Owl_dense_ndarray_c)

                                                            Module Owl_dense_ndarray_c

                                                            type elt = Stdlib.Complex.t
                                                            type arr = (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            type cast_arr = (float, Stdlib.Bigarray.float32_elt, Stdlib.Bigarray.c_layout) diff --git a/docs/owl/Owl_dense_ndarray_d/index.html b/docs/owl/Owl_dense_ndarray_d/index.html index af1cb2bb9..e4584c7a7 100644 --- a/docs/owl/Owl_dense_ndarray_d/index.html +++ b/docs/owl/Owl_dense_ndarray_d/index.html @@ -1,5 +1,5 @@ -Owl_dense_ndarray_d (owl.Owl_dense_ndarray_d)

                                                            Module Owl_dense_ndarray_d

                                                            type elt = float
                                                            type arr = +Owl_dense_ndarray_d (owl.Owl_dense_ndarray_d)

                                                            Module Owl_dense_ndarray_d

                                                            type elt = float
                                                            type arr = (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            include Owl_dense_ndarray_intf.Common with type elt := elt and type arr := arr
                                                            include Owl_base_dense_ndarray_intf.Common with type elt := elt diff --git a/docs/owl/Owl_dense_ndarray_generic/index.html b/docs/owl/Owl_dense_ndarray_generic/index.html index 43f8fbf4d..ffd23ede0 100644 --- a/docs/owl/Owl_dense_ndarray_generic/index.html +++ b/docs/owl/Owl_dense_ndarray_generic/index.html @@ -1,5 +1,22 @@ -Owl_dense_ndarray_generic (owl.Owl_dense_ndarray_generic)

                                                            Module Owl_dense_ndarray_generic

                                                            N-dimensional array module: including creation, manipulation, and various vectorised mathematical operations.

                                                            About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of y; in case both x and y have the same magnitudes, x is less than y if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

                                                            The generic module supports operations for the following Bigarry element types: Int8_signed, Int8_unsigned, Int16_signed, Int16_unsigned, Int32, Int64, Float32, Float64, Complex32, Complex64.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            N-dimensional array type, i.e. Bigarray Genarray type.

                                                            type ('a, 'b) kind = ('a, 'b) Stdlib.Bigarray.kind

                                                            Type of the ndarray, e.g., Bigarray.Float32, Bigarray.Complex64, and etc.

                                                            Create Ndarrays
                                                            val empty : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            empty Bigarray.Float64 [|3;4;5|] creates a three diemensional array of Bigarray.Float64 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are not initialised, they can be any value. empty is faster than zeros to create a ndarray.

                                                            The module only supports the following four types of ndarray: Bigarray.Float32, Bigarray.Float64, Bigarray.Complex32, and Bigarray.Complex64.

                                                            val create : ('a, 'b) kind -> int array -> 'a -> ('a, 'b) t

                                                            create Bigarray.Float64 [|3;4;5|] 2. creates a three-diemensional array of Bigarray.Float64 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to 2.

                                                            val init : ('a, 'b) kind -> int array -> (int -> 'a) -> ('a, 'b) t

                                                            init Bigarray.Float64 d f creates a ndarray x of shape d, then using f to initialise the elements in x. The input of f is 1-dimensional index of the ndarray. You need to explicitly convert it if you need N-dimensional index. The function ind can help you.

                                                            val init_nd : ('a, 'b) kind -> int array -> (int array -> 'a) -> ('a, 'b) t

                                                            init_nd is almost the same as init but f receives n-dimensional index as input. It is more convenient since you don't have to convert the index by yourself, but this also means init_nd is slower than init.

                                                            val zeros : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            zeros Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of Bigarray.Complex32 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "zero". Depending on the kind, zero can be 0. or Complex.zero.

                                                            val ones : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            ones Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of Bigarray.Complex32 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "one". Depending on the kind, one can be 1. or Complex.one.

                                                            val eye : ('a, 'b) kind -> int -> ('a, 'b) t

                                                            eye m creates an m by m identity matrix.

                                                            val uniform : ('a, 'b) kind -> ?a:'a -> ?b:'a -> int array -> ('a, 'b) t

                                                            uniform Bigarray.Float64 [|3;4;5|] creates a three-diemensional array of type Bigarray.Float64. Each dimension has the following size: 3, 4, and 5. The elements in the array follow a uniform distribution 0,1.

                                                            val gaussian : ('a, 'b) kind -> ?mu:'a -> ?sigma:'a -> int array -> ('a, 'b) t

                                                            gaussian Float64 [|3;4;5|] ...

                                                            val poisson : ('a, 'b) kind -> mu:float -> int array -> ('a, 'b) t

                                                            poisson Float64 [|3;4;5|] ...

                                                            val sequential : ('a, 'b) kind -> ?a:'a -> ?step:'a -> int array -> ('a, 'b) t

                                                            sequential Bigarray.Float64 [|3;4;5|] 2. creates a three-diemensional array of type Bigarray.Float64. Each dimension has the following size: 3, 4, and 5. The elements in the array are assigned sequential values.

                                                            ?a specifies the starting value and the default value is zero; whilst ?step specifies the step size with default value one.

                                                            val linspace : ('a, 'b) kind -> 'a -> 'a -> int -> ('a, 'b) t

                                                            linspace k 0. 9. 10 ...

                                                            val logspace : ('a, 'b) kind -> ?base:float -> 'a -> 'a -> int -> ('a, 'b) t

                                                            logspace k 0. 9. 10 ...

                                                            val bernoulli : ('a, 'b) kind -> ?p:float -> int array -> ('a, 'b) t

                                                            bernoulli k ~p:0.3 [|2;3;4|]

                                                            val complex : +Owl_dense_ndarray_generic (owl.Owl_dense_ndarray_generic)

                                                            Module Owl_dense_ndarray_generic

                                                            N-dimensional array module: including creation, manipulation, and various vectorised mathematical operations.

                                                            About the comparison of two complex numbers x and y, Owl uses the following conventions: 1) x and y are equal iff both real and imaginary parts are equal; 2) x is less than y if the magnitude of x is less than the magnitude of y; in case both x and y have the same magnitudes, x is less than y if the phase of x is less than the phase of y; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.

                                                            The generic module supports operations for the following Bigarry element types: Int8_signed, Int8_unsigned, Int16_signed, Int16_unsigned, Int32, Int64, Float32, Float64, Complex32, Complex64.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            N-dimensional array type, i.e. Bigarray Genarray type.

                                                            type ('a, 'b) kind = ('a, 'b) Stdlib.Bigarray.kind

                                                            Type of the ndarray, e.g., Bigarray.Float32, Bigarray.Complex64, and etc.

                                                            Create Ndarrays
                                                            val empty : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            empty Bigarray.Float64 [|3;4;5|] creates a three diemensional array of Bigarray.Float64 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are not initialised, they can be any value. empty is faster than zeros to create a ndarray.

                                                            The module only supports the following four types of ndarray: Bigarray.Float32, Bigarray.Float64, Bigarray.Complex32, and Bigarray.Complex64.

                                                            val create : ('a, 'b) kind -> int array -> 'a -> ('a, 'b) t

                                                            create Bigarray.Float64 [|3;4;5|] 2. creates a three-diemensional array of Bigarray.Float64 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to 2.

                                                            val init : ('a, 'b) kind -> int array -> (int -> 'a) -> ('a, 'b) t

                                                            init Bigarray.Float64 d f creates a ndarray x of shape d, then using f to initialise the elements in x. The input of f is 1-dimensional index of the ndarray. You need to explicitly convert it if you need N-dimensional index. The function ind can help you.

                                                            val init_nd : ('a, 'b) kind -> int array -> (int array -> 'a) -> ('a, 'b) t

                                                            init_nd is almost the same as init but f receives n-dimensional index as input. It is more convenient since you don't have to convert the index by yourself, but this also means init_nd is slower than init.

                                                            val zeros : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            zeros Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of Bigarray.Complex32 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "zero". Depending on the kind, zero can be 0. or Complex.zero.

                                                            val ones : ('a, 'b) kind -> int array -> ('a, 'b) t

                                                            ones Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of Bigarray.Complex32 type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "one". Depending on the kind, one can be 1. or Complex.one.

                                                            val eye : ('a, 'b) kind -> int -> ('a, 'b) t

                                                            eye m creates an m by m identity matrix.

                                                            val uniform : ('a, 'b) kind -> ?a:'a -> ?b:'a -> int array -> ('a, 'b) t

                                                            uniform Bigarray.Float64 [|3;4;5|] creates a three-diemensional array of type Bigarray.Float64. Each dimension has the following size: 3, 4, and 5. The elements in the array follow a uniform distribution 0,1.

                                                            val gaussian : ('a, 'b) kind -> ?mu:'a -> ?sigma:'a -> int array -> ('a, 'b) t

                                                            gaussian Float64 [|3;4;5|] ...

                                                            val poisson : ('a, 'b) kind -> mu:float -> int array -> ('a, 'b) t

                                                            poisson Float64 [|3;4;5|] ...

                                                            val sequential : ('a, 'b) kind -> ?a:'a -> ?step:'a -> int array -> ('a, 'b) t

                                                            sequential Bigarray.Float64 [|3;4;5|] 2. creates a three-diemensional array of type Bigarray.Float64. Each dimension has the following size: 3, 4, and 5. The elements in the array are assigned sequential values.

                                                            ?a specifies the starting value and the default value is zero; whilst ?step specifies the step size with default value one.

                                                            val linspace : ('a, 'b) kind -> 'a -> 'a -> int -> ('a, 'b) t

                                                            linspace k 0. 9. 10 ...

                                                            val logspace : ('a, 'b) kind -> ?base:float -> 'a -> 'a -> int -> ('a, 'b) t

                                                            logspace k 0. 9. 10 ...

                                                            val bernoulli : ('a, 'b) kind -> ?p:float -> int array -> ('a, 'b) t

                                                            bernoulli k ~p:0.3 [|2;3;4|]

                                                            val complex : ('a, 'b) kind -> ('c, 'd) kind -> ('a, 'b) t -> @@ -9,7 +26,13 @@ ('c, 'd) kind -> ('a, 'b) t -> ('a, 'b) t -> - ('c, 'd) t

                                                            complex rho theta constructs a complex ndarray/matrix from polar coordinates rho and theta. rho contains the magnitudes and theta contains phase angles. Note that the behaviour is undefined if rho has negative elelments or theta has infinity elelments.

                                                            val unit_basis : ('a, 'b) kind -> int -> int -> ('a, 'b) t

                                                            unit_basis k n i returns a unit basis vector with ith element set to 1.

                                                            Obtain basic properties
                                                            val shape : ('a, 'b) t -> int array

                                                            shape x returns the shape of ndarray x.

                                                            val num_dims : ('a, 'b) t -> int

                                                            num_dims x returns the number of dimensions of ndarray x.

                                                            val nth_dim : ('a, 'b) t -> int -> int

                                                            nth_dim x returns the size of the nth dimension of x.

                                                            val numel : ('a, 'b) t -> int

                                                            numel x returns the number of elements in x.

                                                            val nnz : ('a, 'b) t -> int

                                                            nnz x returns the number of non-zero elements in x.

                                                            val density : ('a, 'b) t -> float

                                                            density x returns the percentage of non-zero elements in x.

                                                            val size_in_bytes : ('a, 'b) t -> int

                                                            size_in_bytes x returns the size of x in bytes in memory.

                                                            val same_shape : ('a, 'b) t -> ('c, 'd) t -> bool

                                                            same_shape x y checks whether x and y has the same shape or not.

                                                            val same_data : ('a, 'b) t -> ('a, 'b) t -> bool

                                                            same_data x y checks whether x and y share the same underlying data in the memory. Namely, both variables point to the same memory address. This is done by checking the Data pointer in the Bigarray structure.

                                                            This function is very useful for avoiding unnecessary copying between two ndarrays especially if one has been reshaped or sliced.

                                                            val kind : ('a, 'b) t -> ('a, 'b) kind

                                                            kind x returns the type of ndarray x. It is one of the four possible values: Bigarray.Float32, Bigarray.Float64, Bigarray.Complex32, and Bigarray.Complex64.

                                                            val strides : ('a, 'b) t -> int array

                                                            strides x calculates the strides of x. E.g., if x is of shape [|3;4;5|], the returned strides will be [|20;5;1|].

                                                            val slice_size : ('a, 'b) t -> int array

                                                            slice_size calculates the slice size in each dimension, E.g., if x is of shape [|3;4;5|], the returned slice size will be [|60; 20; 5|].

                                                            val ind : ('a, 'b) t -> int -> int array

                                                            ind x i converts x's one-dimensional index i to n-dimensional one.

                                                            val i1d : ('a, 'b) t -> int array -> int

                                                            i1d x i converts x's n-dimensional index i to one-dimensional one.

                                                            Manipulate Ndarrays
                                                            val get : ('a, 'b) t -> int array -> 'a

                                                            get x i returns the value at i in x. E.g., get x [|0;2;1|] returns the value at [|0;2;1|] in x.

                                                            val set : ('a, 'b) t -> int array -> 'a -> unit

                                                            set x i a sets the value at i to a in x.

                                                            val get_index : ('a, 'b) t -> int array array -> 'a array

                                                            get_index i x returns an array of element values specified by the indices i. The length of array i equals the number of dimensions of x. The arrays in i must have the same length, and each represents the indices in that dimension.

                                                            E.g., [| [|1;2|]; [|3;4|] |] returns the value of elements at position (1,3) and (2,4) respectively.

                                                            val set_index : ('a, 'b) t -> int array array -> 'a array -> unit

                                                            set_index i x a sets the value of elements in x according to the indices specified by i. The length of array i equals the number of dimensions of x. The arrays in i must have the same length, and each represents the indices in that dimension.

                                                            If the length of a equals to the length of i, then each element will be assigned by the value in the corresponding position in x. If the length of a equals to one, then all the elements will be assigned the same value.

                                                            val get_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t

                                                            get_fancy s x returns a copy of the slice in x. The slice is defined by a which is an int option array. E.g., for a ndarray x of dimension [|2; 2; 3|], slice [0] x takes the following slices of index \(0,*,*\), i.e., [|0;0;0|], [|0;0;1|], [|0;0;2|] ... Also note that if the length of s is less than the number of dimensions of x, slice function will append slice definition to higher diemensions by assuming all the elements in missing dimensions will be taken.

                                                            Basically, slice function offers very much the same semantic as that in numpy, i.e., start:stop:step grammar, so if you how to index and slice ndarray in numpy, you should not find it difficult to use this function. Please just refer to numpy documentation or my tutorial.

                                                            There are two differences between slice_left and slice: slice_left does not make a copy but simply moving the pointer; slice_left can only make a slice from left-most axis whereas slice is much more flexible and can work on arbitrary axis which need not start from left-most side.

                                                            val set_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            set_fancy axis x y set the slice defined by axis in x according to the values in y. y must have the same shape as the one defined by axis.

                                                            About the slice definition of axis, please refer to get_fancy function.

                                                            val get_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t

                                                            This function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val set_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            This function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val get_slice : int list list -> ('a, 'b) t -> ('a, 'b) t

                                                            get_slice axis x aims to provide a simpler version of get_fancy. This function assumes that every list element in the passed in int list list represents a range, i.e., R constructor.

                                                            E.g., [[];[0;3];[0]] is equivalent to [R []; R [0;3]; R [0]].

                                                            val set_slice : int list list -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            set_slice axis x y aims to provide a simpler version of set_fancy. This function assumes that every list element in the passed in int list list represents a range, i.e., R constructor.

                                                            E.g., [[];[0;3];[0]] is equivalent to [R []; R [0;3]; R [0]].

                                                            val get_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t

                                                            get_slice_ext axis x is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            E.g., x.%{0;1;2}.

                                                            val set_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            Similar to get_slice_ext axis x, this function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val sub_left : ('a, 'b) t -> int -> int -> ('a, 'b) t

                                                            Some as Bigarray.sub_left, please refer to Bigarray documentation.

                                                            val sub_ndarray : int array -> ('a, 'b) t -> ('a, 'b) t array

                                                            sub_ndarray parts x is similar to Bigarray.sub_left. It splits the passed in ndarray x along the axis 0 according to parts. The elelments in parts do not need to be equal but they must sum up to the dimension along axis zero.

                                                            The returned sub-ndarrays share the same memory as x. Because there is no copies made, this function is much faster than using `split` function to divide the lowest dimensionality of x.

                                                            val slice_left : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Same as Bigarray.slice_left, please refer to Bigarray documentation.

                                                            val reset : ('a, 'b) t -> unit

                                                            reset x resets all the elements in x to zero.

                                                            val fill : ('a, 'b) t -> 'a -> unit

                                                            fill x a assigns the value a to the elements in x.

                                                            val copy : ('a, 'b) t -> ('a, 'b) t

                                                            copy x makes a copy of x.

                                                            val resize : ?head:bool -> ('a, 'b) t -> int array -> ('a, 'b) t

                                                            resize ~head x d resizes the ndarray x. If there are less number of elelments in the new shape than the old one, the new ndarray shares part of the memory with the old x. head indicates the alignment between the new and old data, either from head or from tail. Note the data is flattened before the operation.

                                                            If there are more elements in the new shape d. Then new memory space will be allocated and the content of x will be copied to the new memory. The rest of the allocated space will be filled with zeros. The default value of head is true.

                                                            val reshape : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            reshape x d transforms x into a new shape definted by d. Note the reshape function will not make a copy of x, the returned ndarray shares the same memory with the original x.

                                                            One shape dimension (only one) can be set to -1. In this case, the value is inferred from the length of the array and remaining dimensions.

                                                            val flatten : ('a, 'b) t -> ('a, 'b) t

                                                            flatten x transforms x into a one-dimsonal array without making a copy. Therefore the returned value shares the same memory space with original x.

                                                            val reverse : ('a, 'b) t -> ('a, 'b) t

                                                            reverse x reverse the order of all elements in the flattened x and returns the results in a new ndarray. The original x remains intact.

                                                            val flip : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            flip ~axis x flips a matrix/ndarray along axis. By default axis = 0. The result is returned in a new matrix/ndarray, so the original x remains intact.

                                                            val rotate : ('a, 'b) t -> int -> ('a, 'b) t

                                                            rotate x d rotates x clockwise d degrees. d must be multiple times of 90, otherwise the function will fail. If x is an n-dimensional array, then the function rotates the plane formed by the first and second dimensions.

                                                            val transpose : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

                                                            transpose ~axis x makes a copy of x, then transpose it according to ~axis. ~axis must be a valid permutation of x dimension indices. E.g., for a three-dimensional ndarray, it can be [2;1;0], [0;2;1], [1;2;0], and etc.

                                                            val swap : int -> int -> ('a, 'b) t -> ('a, 'b) t

                                                            swap i j x makes a copy of x, then swaps the data on axis i and j.

                                                            val tile : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            tile x a tiles the data in x according the repetition specified by a. This function provides the exact behaviour as numpy.tile, please refer to the numpy's online documentation for details.

                                                            val repeat : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            repeat x a repeats the elements of x according the repetition specified by a. The i-th element of a specifies the number of times that the individual entries of the i-th dimension of x should be repeated.

                                                            val concat_vertical : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            concat_vertical x y concatenates two ndarray x and y vertically. This is just a convenient function for concatenating two ndarrays along their lowest dimension, i.e. 0.

                                                            The associated operator is @||, please refer to :doc:`owl_operator`.

                                                            val concat_horizontal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            concat_horizontal x y concatenates two ndarrays x and y horizontally. This is just a convenient function for concatenating two ndarrays along their highest dimension.

                                                            The associated operator is @=, please refer to :doc:`owl_operator`.

                                                            val concat_vh : ('a, 'b) t array array -> ('a, 'b) t

                                                            concat_vh is used to assemble small parts of matrices into a bigger one. E.g. In [| [|a; b; c|]; [|d; e; f|]; [|g; h; i|] |], wherein `a, b, c ... i` are matrices of different shapes. They will be concatenated into a big matrix as follows.

                                                            .. math:: \beginmatrix a & b & c \\ d & e & f \\ g & h & i \endmatrix

                                                            This is achieved by first concatenating along axis:1 for each element in the array, then concatenating along axis:0. The number of elements in each array needs not to be equal as long as the aggregated dimensions match. E.g., please check the following example.

                                                            .. code-block:: ocaml

                                                            let a00 = Mat.sequential 2 3 in let a01 = Mat.sequential 2 2 in let a02 = Mat.sequential 2 1 in let a10 = Mat.sequential 3 3 in let a11 = Mat.sequential 3 3 in Mat.concat_vh | [|a00; a01; a02|]; [|a10; a11|] |;;

                                                            val concatenate : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

                                                            concatenate ~axis:2 x concatenates an array of ndarrays along the third dimension. For the ndarrays in x, they must have the same shape except the dimension specified by axis. The default value of axis is 0, i.e., the lowest dimension of a matrix/ndarray.

                                                            val stack : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

                                                            stack ~axis x stacks an array of ndarrays along the axis dimension. For example, if x contains K ndarrays of shape |2;3|, then stack ~axis:1 x will return an ndarray of dimensions |2;K;3|. The ndarrays in x, they must all have the same shape. The default value of axis is 0.

                                                            val split : ?axis:int -> int array -> ('a, 'b) t -> ('a, 'b) t array

                                                            split ~axis parts x splits an ndarray x into parts along the specified axis. This function is the inverse operation of concatenate. The elements in x must sum up to the dimension in the specified axis.

                                                            val split_vh : (int * int) array array -> ('a, 'b) t -> ('a, 'b) t array array

                                                            split_vh parts x splits a passed in ndarray x along the first two dimensions, i.e. axis 0 and axis 1. This is the inverse operation of concat_vh function, and the function is very useful in dividing a big matrix into smaller (especially heterogeneous) parts.

                                                            For example, given a matrix x of shape [|8;10|], it is possible to split in the following ways.

                                                            .. code-block:: ocaml

                                                            Mat.split_vh | [|(8,5);(8,5)|] | x;; Mat.split_vh | [|(4,5);(4,5)|]; [|(4,10)|] | x;; Mat.split_vh | [|(4,5);(4,5)|]; [|(4,5);(4,5)|] | x;;

                                                            val squeeze : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

                                                            squeeze ~axis x removes single-dimensional entries from the shape of x.

                                                            val expand : ?hi:bool -> ('a, 'b) t -> int -> ('a, 'b) t

                                                            expand x d reshapes x by increasing its rank from num_dims x to d. The opposite operation is squeeze x. The hi parameter is used to specify whether the expandsion is along high dimension (by setting true), or along the low dimension (by setting false). The default value is false.

                                                            val pad : ?v:'a -> int list list -> ('a, 'b) t -> ('a, 'b) t

                                                            pad ~v p x pads a ndarray x with a constant value v. The padding index p is a list of lists of 2 integers. These two integers denote padding width at both edges of one dimension of x.

                                                            val dropout : ?rate:float -> ('a, 'b) t -> ('a, 'b) t

                                                            dropout ~rate:0.3 x drops out 30% of the elements in x, in other words, by setting their values to zeros.

                                                            val top : ('a, 'b) t -> int -> int array array

                                                            top x n returns the indices of n greatest values of x. The indices are arranged according to the corresponding element values, from the greatest one to the smallest one.

                                                            val bottom : ('a, 'b) t -> int -> int array array

                                                            bottom x n returns the indices of n smallest values of x. The indices are arranged according to the corresponding element values, from the smallest one to the greatest one.

                                                            val sort1 : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            sort1 ~axis x performs quicksort of the elements along specific axis in x. A new copy is returned as result, the original x remains intact.

                                                            val sort : ('a, 'b) t -> ('a, 'b) t

                                                            sort x performs quicksort of the elelments in x. A new copy is returned as result, the original x remains intact. If you want to perform in-place sorting, please use `sort_` instead.

                                                            val argsort : ('a, 'b) t -> (int64, Stdlib.Bigarray.int64_elt) t

                                                            argsort x returns the indices with which the elements in x are sorted in increasing order. Note that the returned index ndarray has the same shape as that of x, and the indices are 1D indices.

                                                            val draw : ?axis:int -> ('a, 'b) t -> int -> ('a, 'b) t * int array

                                                            draw ~axis x n draws n samples from x along the specified axis, with replacement. axis is set to zero by default. The return is a tuple of both samples and the indices of the selected samples.

                                                            val mmap : + ('c, 'd) t

                                                            complex rho theta constructs a complex ndarray/matrix from polar coordinates rho and theta. rho contains the magnitudes and theta contains phase angles. Note that the behaviour is undefined if rho has negative elelments or theta has infinity elelments.

                                                            val unit_basis : ('a, 'b) kind -> int -> int -> ('a, 'b) t

                                                            unit_basis k n i returns a unit basis vector with ith element set to 1.

                                                            Obtain basic properties
                                                            val shape : ('a, 'b) t -> int array

                                                            shape x returns the shape of ndarray x.

                                                            val num_dims : ('a, 'b) t -> int

                                                            num_dims x returns the number of dimensions of ndarray x.

                                                            val nth_dim : ('a, 'b) t -> int -> int

                                                            nth_dim x returns the size of the nth dimension of x.

                                                            val numel : ('a, 'b) t -> int

                                                            numel x returns the number of elements in x.

                                                            val nnz : ('a, 'b) t -> int

                                                            nnz x returns the number of non-zero elements in x.

                                                            val density : ('a, 'b) t -> float

                                                            density x returns the percentage of non-zero elements in x.

                                                            val size_in_bytes : ('a, 'b) t -> int

                                                            size_in_bytes x returns the size of x in bytes in memory.

                                                            val same_shape : ('a, 'b) t -> ('c, 'd) t -> bool

                                                            same_shape x y checks whether x and y has the same shape or not.

                                                            val same_data : ('a, 'b) t -> ('a, 'b) t -> bool

                                                            same_data x y checks whether x and y share the same underlying data in the memory. Namely, both variables point to the same memory address. This is done by checking the Data pointer in the Bigarray structure.

                                                            This function is very useful for avoiding unnecessary copying between two ndarrays especially if one has been reshaped or sliced.

                                                            val kind : ('a, 'b) t -> ('a, 'b) kind

                                                            kind x returns the type of ndarray x. It is one of the four possible values: Bigarray.Float32, Bigarray.Float64, Bigarray.Complex32, and Bigarray.Complex64.

                                                            val strides : ('a, 'b) t -> int array

                                                            strides x calculates the strides of x. E.g., if x is of shape [|3;4;5|], the returned strides will be [|20;5;1|].

                                                            val slice_size : ('a, 'b) t -> int array

                                                            slice_size calculates the slice size in each dimension, E.g., if x is of shape [|3;4;5|], the returned slice size will be [|60; 20; 5|].

                                                            val ind : ('a, 'b) t -> int -> int array

                                                            ind x i converts x's one-dimensional index i to n-dimensional one.

                                                            val i1d : ('a, 'b) t -> int array -> int

                                                            i1d x i converts x's n-dimensional index i to one-dimensional one.

                                                            Manipulate Ndarrays
                                                            val get : ('a, 'b) t -> int array -> 'a

                                                            get x i returns the value at i in x. E.g., get x [|0;2;1|] returns the value at [|0;2;1|] in x.

                                                            val set : ('a, 'b) t -> int array -> 'a -> unit

                                                            set x i a sets the value at i to a in x.

                                                            val get_index : ('a, 'b) t -> int array array -> 'a array

                                                            get_index i x returns an array of element values specified by the indices i. The length of array i equals the number of dimensions of x. The arrays in i must have the same length, and each represents the indices in that dimension.

                                                            E.g., [| [|1;2|]; [|3;4|] |] returns the value of elements at position (1,3) and (2,4) respectively.

                                                            val set_index : ('a, 'b) t -> int array array -> 'a array -> unit

                                                            set_index i x a sets the value of elements in x according to the indices specified by i. The length of array i equals the number of dimensions of x. The arrays in i must have the same length, and each represents the indices in that dimension.

                                                            If the length of a equals to the length of i, then each element will be assigned by the value in the corresponding position in x. If the length of a equals to one, then all the elements will be assigned the same value.

                                                            val get_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t

                                                            get_fancy s x returns a copy of the slice in x. The slice is defined by a which is an int option array. E.g., for a ndarray x of dimension [|2; 2; 3|], slice [0] x takes the following slices of index \(0,*,*\), i.e., [|0;0;0|], [|0;0;1|], [|0;0;2|] ... Also note that if the length of s is less than the number of dimensions of x, slice function will append slice definition to higher diemensions by assuming all the elements in missing dimensions will be taken.

                                                            Basically, slice function offers very much the same semantic as that in numpy, i.e., start:stop:step grammar, so if you how to index and slice ndarray in numpy, you should not find it difficult to use this function. Please just refer to numpy documentation or my tutorial.

                                                            There are two differences between slice_left and slice: slice_left does not make a copy but simply moving the pointer; slice_left can only make a slice from left-most axis whereas slice is much more flexible and can work on arbitrary axis which need not start from left-most side.

                                                            val set_fancy : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            set_fancy axis x y set the slice defined by axis in x according to the values in y. y must have the same shape as the one defined by axis.

                                                            About the slice definition of axis, please refer to get_fancy function.

                                                            val get_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t

                                                            This function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val set_fancy_ext : Owl_types.index array -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            This function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val get_slice : int list list -> ('a, 'b) t -> ('a, 'b) t

                                                            get_slice axis x aims to provide a simpler version of get_fancy. This function assumes that every list element in the passed in int list list represents a range, i.e., R constructor.

                                                            E.g., [[];[0;3];[0]] is equivalent to [R []; R [0;3]; R [0]].

                                                            val set_slice : int list list -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            set_slice axis x y aims to provide a simpler version of set_fancy. This function assumes that every list element in the passed in int list list represents a range, i.e., R constructor.

                                                            E.g., [[];[0;3];[0]] is equivalent to [R []; R [0;3]; R [0]].

                                                            val get_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t

                                                            get_slice_ext axis x is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            E.g., x.%{0;1;2}.

                                                            val set_slice_ext : int list array -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            Similar to get_slice_ext axis x, this function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.

                                                            val sub_left : ('a, 'b) t -> int -> int -> ('a, 'b) t

                                                            Some as Bigarray.sub_left, please refer to Bigarray documentation.

                                                            val sub_ndarray : int array -> ('a, 'b) t -> ('a, 'b) t array

                                                            sub_ndarray parts x is similar to Bigarray.sub_left. It splits the passed in ndarray x along the axis 0 according to parts. The elelments in parts do not need to be equal but they must sum up to the dimension along axis zero.

                                                            The returned sub-ndarrays share the same memory as x. Because there is no copies made, this function is much faster than using `split` function to divide the lowest dimensionality of x.

                                                            val slice_left : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Same as Bigarray.slice_left, please refer to Bigarray documentation.

                                                            val reset : ('a, 'b) t -> unit

                                                            reset x resets all the elements in x to zero.

                                                            val fill : ('a, 'b) t -> 'a -> unit

                                                            fill x a assigns the value a to the elements in x.

                                                            val copy : ('a, 'b) t -> ('a, 'b) t

                                                            copy x makes a copy of x.

                                                            val resize : ?head:bool -> ('a, 'b) t -> int array -> ('a, 'b) t

                                                            resize ~head x d resizes the ndarray x. If there are less number of elelments in the new shape than the old one, the new ndarray shares part of the memory with the old x. head indicates the alignment between the new and old data, either from head or from tail. Note the data is flattened before the operation.

                                                            If there are more elements in the new shape d. Then new memory space will be allocated and the content of x will be copied to the new memory. The rest of the allocated space will be filled with zeros. The default value of head is true.

                                                            val reshape : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            reshape x d transforms x into a new shape definted by d. Note the reshape function will not make a copy of x, the returned ndarray shares the same memory with the original x.

                                                            One shape dimension (only one) can be set to -1. In this case, the value is inferred from the length of the array and remaining dimensions.

                                                            val flatten : ('a, 'b) t -> ('a, 'b) t

                                                            flatten x transforms x into a one-dimsonal array without making a copy. Therefore the returned value shares the same memory space with original x.

                                                            val reverse : ('a, 'b) t -> ('a, 'b) t

                                                            reverse x reverse the order of all elements in the flattened x and returns the results in a new ndarray. The original x remains intact.

                                                            val flip : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            flip ~axis x flips a matrix/ndarray along axis. By default axis = 0. The result is returned in a new matrix/ndarray, so the original x remains intact.

                                                            val rotate : ('a, 'b) t -> int -> ('a, 'b) t

                                                            rotate x d rotates x clockwise d degrees. d must be multiple times of 90, otherwise the function will fail. If x is an n-dimensional array, then the function rotates the plane formed by the first and second dimensions.

                                                            val transpose : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

                                                            transpose ~axis x makes a copy of x, then transpose it according to ~axis. ~axis must be a valid permutation of x dimension indices. E.g., for a three-dimensional ndarray, it can be [2;1;0], [0;2;1], [1;2;0], and etc.

                                                            val swap : int -> int -> ('a, 'b) t -> ('a, 'b) t

                                                            swap i j x makes a copy of x, then swaps the data on axis i and j.

                                                            val tile : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            tile x a tiles the data in x according the repetition specified by a. This function provides the exact behaviour as numpy.tile, please refer to the numpy's online documentation for details.

                                                            val repeat : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            repeat x a repeats the elements of x according the repetition specified by a. The i-th element of a specifies the number of times that the individual entries of the i-th dimension of x should be repeated.

                                                            val concat_vertical : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            concat_vertical x y concatenates two ndarray x and y vertically. This is just a convenient function for concatenating two ndarrays along their lowest dimension, i.e. 0.

                                                            The associated operator is @||, please refer to :doc:`owl_operator`.

                                                            val concat_horizontal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            concat_horizontal x y concatenates two ndarrays x and y horizontally. This is just a convenient function for concatenating two ndarrays along their highest dimension.

                                                            The associated operator is @=, please refer to :doc:`owl_operator`.

                                                            val concat_vh : ('a, 'b) t array array -> ('a, 'b) t

                                                            concat_vh is used to assemble small parts of matrices into a bigger one. E.g. In [| [|a; b; c|]; [|d; e; f|]; [|g; h; i|] |], wherein `a, b, c ... i` are matrices of different shapes. They will be concatenated into a big matrix as follows.

                                                            +  \begin{bmatrix}
                                                            +    a & b & c \\
                                                            +    d & e & f \\
                                                            +    g & h & i
                                                            +  \end{bmatrix}
                                                            +

                                                            This is achieved by first concatenating along axis:1 for each element in the array, then concatenating along axis:0. The number of elements in each array needs not to be equal as long as the aggregated dimensions match. E.g., please check the following example.

                                                            .. code-block:: ocaml

                                                            let a00 = Mat.sequential 2 3 in let a01 = Mat.sequential 2 2 in let a02 = Mat.sequential 2 1 in let a10 = Mat.sequential 3 3 in let a11 = Mat.sequential 3 3 in Mat.concat_vh | [|a00; a01; a02|]; [|a10; a11|] |;;

                                                            val concatenate : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

                                                            concatenate ~axis:2 x concatenates an array of ndarrays along the third dimension. For the ndarrays in x, they must have the same shape except the dimension specified by axis. The default value of axis is 0, i.e., the lowest dimension of a matrix/ndarray.

                                                            val stack : ?axis:int -> ('a, 'b) t array -> ('a, 'b) t

                                                            stack ~axis x stacks an array of ndarrays along the axis dimension. For example, if x contains K ndarrays of shape |2;3|, then stack ~axis:1 x will return an ndarray of dimensions |2;K;3|. The ndarrays in x, they must all have the same shape. The default value of axis is 0.

                                                            val split : ?axis:int -> int array -> ('a, 'b) t -> ('a, 'b) t array

                                                            split ~axis parts x splits an ndarray x into parts along the specified axis. This function is the inverse operation of concatenate. The elements in x must sum up to the dimension in the specified axis.

                                                            val split_vh : (int * int) array array -> ('a, 'b) t -> ('a, 'b) t array array

                                                            split_vh parts x splits a passed in ndarray x along the first two dimensions, i.e. axis 0 and axis 1. This is the inverse operation of concat_vh function, and the function is very useful in dividing a big matrix into smaller (especially heterogeneous) parts.

                                                            For example, given a matrix x of shape [|8;10|], it is possible to split in the following ways.

                                                            .. code-block:: ocaml

                                                            Mat.split_vh | [|(8,5);(8,5)|] | x;; Mat.split_vh | [|(4,5);(4,5)|]; [|(4,10)|] | x;; Mat.split_vh | [|(4,5);(4,5)|]; [|(4,5);(4,5)|] | x;;

                                                            val squeeze : ?axis:int array -> ('a, 'b) t -> ('a, 'b) t

                                                            squeeze ~axis x removes single-dimensional entries from the shape of x.

                                                            val expand : ?hi:bool -> ('a, 'b) t -> int -> ('a, 'b) t

                                                            expand x d reshapes x by increasing its rank from num_dims x to d. The opposite operation is squeeze x. The hi parameter is used to specify whether the expandsion is along high dimension (by setting true), or along the low dimension (by setting false). The default value is false.

                                                            val pad : ?v:'a -> int list list -> ('a, 'b) t -> ('a, 'b) t

                                                            pad ~v p x pads a ndarray x with a constant value v. The padding index p is a list of lists of 2 integers. These two integers denote padding width at both edges of one dimension of x.

                                                            val dropout : ?rate:float -> ('a, 'b) t -> ('a, 'b) t

                                                            dropout ~rate:0.3 x drops out 30% of the elements in x, in other words, by setting their values to zeros.

                                                            val top : ('a, 'b) t -> int -> int array array

                                                            top x n returns the indices of n greatest values of x. The indices are arranged according to the corresponding element values, from the greatest one to the smallest one.

                                                            val bottom : ('a, 'b) t -> int -> int array array

                                                            bottom x n returns the indices of n smallest values of x. The indices are arranged according to the corresponding element values, from the smallest one to the greatest one.

                                                            val sort1 : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            sort1 ~axis x performs quicksort of the elements along specific axis in x. A new copy is returned as result, the original x remains intact.

                                                            val sort : ('a, 'b) t -> ('a, 'b) t

                                                            sort x performs quicksort of the elelments in x. A new copy is returned as result, the original x remains intact. If you want to perform in-place sorting, please use `sort_` instead.

                                                            val argsort : ('a, 'b) t -> (int64, Stdlib.Bigarray.int64_elt) t

                                                            argsort x returns the indices with which the elements in x are sorted in increasing order. Note that the returned index ndarray has the same shape as that of x, and the indices are 1D indices.

                                                            val draw : ?axis:int -> ('a, 'b) t -> int -> ('a, 'b) t * int array

                                                            draw ~axis x n draws n samples from x along the specified axis, with replacement. axis is set to zero by default. The return is a tuple of both samples and the indices of the selected samples.

                                                            val mmap : Unix.file_descr -> ?pos:int64 -> ('a, 'b) kind -> @@ -98,7 +121,8 @@ ?p:float -> ?keep_dims:bool -> ('a, 'b) t -> - ('a, 'b) t

                                                            vecnorm ~axis ~p x calculates the generalised vector p-norm along the specified axis. The generalised p-norm is defined as below.

                                                            .. math:: ||v||_p = \Big \sum_{k=0}^{N-1} |v_k|^p \Big^

                                                            /p

                                                            Parameters: * axis is the axis for reduction. * p is order of norm, default value is 2. * x is the input ndarray.

                                                            Returns: * If p = infinity, then returns :math:`||v||_\infty = \max_i(|v(i)|)`. * If p = -infinity, then returns :math:`||v||_

                                                            \infty

                                                            }

                                                            = \min_i(|v(i)|)`. * Otherwise returns generalised vector p-norm defined above.

                                                            val vecnorm' : ?p:float -> ('a, 'b) t -> 'a

                                                            vecnorm' flattens the input into 1-d vector first, then calculates the generalised p-norm the same as venorm.

                                                            val cumsum : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cumsum ~axis x : performs cumulative sum of the elements along the given axis ~axis. If ~axis is None, then the cumsum is performed along the lowest dimension. The returned result however always remains the same shape.

                                                            val cumprod : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cumprod ~axis x : similar to cumsum but performs cumulative product of the elements along the given ~axis.

                                                            val cummin : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cummin ~axis x : performs cumulative min along axis dimension.

                                                            val cummax : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cummax ~axis x : performs cumulative max along axis dimension.

                                                            val diff : ?axis:int -> ?n:int -> ('a, 'b) t -> ('a, 'b) t

                                                            diff ~axis ~n x calculates the n-th difference of x along the specified axis.

                                                            Parameters: * axis: axis to calculate the difference. The default value is the highest dimension. * n: how many times to calculate the difference. The default value is 1.

                                                            Return: * The difference ndarray y. Note that the shape of y 1 less than that of x along specified axis.

                                                            val angle : (Stdlib.Complex.t, 'a) t -> (Stdlib.Complex.t, 'a) t

                                                            angle x calculates the phase angle of all complex numbers in x.

                                                            val proj : (Stdlib.Complex.t, 'a) t -> (Stdlib.Complex.t, 'a) t

                                                            proj x computes the projection on Riemann sphere of all elelments in x.

                                                            val lgamma : ('a, 'b) t -> ('a, 'b) t

                                                            lgamma x computes the loggamma of the elements in x and returns the result in a new ndarray.

                                                            val dawsn : ('a, 'b) t -> ('a, 'b) t

                                                            dawsn x computes the Dawson function of the elements in x and returns the result in a new ndarray.

                                                            val i0 : ('a, 'b) t -> ('a, 'b) t

                                                            i0 x computes the modified Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val i0e : ('a, 'b) t -> ('a, 'b) t

                                                            i0e x computes the exponentially scaled modified Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val i1 : ('a, 'b) t -> ('a, 'b) t

                                                            i1 x computes the modified Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val i1e : ('a, 'b) t -> ('a, 'b) t

                                                            i1e x computes the exponentially scaled modified Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val iv : v:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            iv v x computes modified Bessel function of x of real order v

                                                            val scalar_iv : v:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_iv v x computes the modified Bessel function of x of real order v.

                                                            val iv_scalar : v:('a, 'b) t -> 'a -> ('a, 'b) t

                                                            iv_scalar v x computes modified Bessel function of x of real order v

                                                            val j0 : ('a, 'b) t -> ('a, 'b) t

                                                            j0 x computes the Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val j1 : ('a, 'b) t -> ('a, 'b) t

                                                            j1 x computes the Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val jv : v:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            jv v x computes Bessel function the first kind of x of real order v

                                                            val scalar_jv : v:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_jv v x computes the Bessel function of the first kind of x of real order v.

                                                            val jv_scalar : v:('a, 'b) t -> 'a -> ('a, 'b) t

                                                            jv_scalar v x computes Bessel function of the first kind of x of real order v

                                                            Binary math operators
                                                            val add : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            add x y adds all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            General broadcast operation is automatically applied to add/sub/mul/div, etc. The function compares the dimension element-wise from the highest to the lowest with the following broadcast rules (same as numpy): 1. equal; 2. either is 1.

                                                            val sub : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            sub x y subtracts all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val mul : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            mul x y multiplies all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val div : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            div x y divides all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val add_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            add_scalar x a adds a scalar value a to each element in x, and returns the result in a new ndarray.

                                                            val sub_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            sub_scalar x a subtracts a scalar value a from each element in x, and returns the result in a new ndarray.

                                                            val mul_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            mul_scalar x a multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val div_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            div_scalar x a divides each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_add : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_add a x adds a scalar value a to each element in x, and returns the result in a new ndarray.

                                                            val scalar_sub : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_sub a x subtracts each element in x from a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_mul : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_mul a x multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_div : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_div a x divides a scalar value a by each element in x, and returns the result in a new ndarray.

                                                            val pow : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            pow x y computes pow(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val scalar_pow : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_pow a x computes the power value of a scalar value a using the elements in a ndarray x.

                                                            val pow_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            pow_scalar x a computes each element in x power to a.

                                                            val atan2 : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            atan2 x y computes atan2(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val scalar_atan2 : float -> (float, 'a) t -> (float, 'a) t

                                                            scalar_atan2 a x

                                                            val atan2_scalar : (float, 'a) t -> float -> (float, 'a) t

                                                            scalar_atan2 x a

                                                            val hypot : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            hypot x y computes sqrt(x*x + y*y) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val min2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            min2 x y computes the minimum of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val max2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            max2 x y computes the maximum of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val fmod : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            fmod x y performs float mod division.

                                                            val fmod_scalar : (float, 'a) t -> float -> (float, 'a) t

                                                            fmod_scalar x a performs mod division between x and scalar a.

                                                            val scalar_fmod : float -> (float, 'a) t -> (float, 'a) t

                                                            scalar_fmod x a performs mod division between scalar a and x.

                                                            val ssqr' : ('a, 'b) t -> 'a -> 'a

                                                            ssqr x a computes the sum of squared differences of all the elements in x from constant a. This function only computes the square of each element rather than the conjugate transpose as l2norm_sqr does.

                                                            val ssqr_diff' : ('a, 'b) t -> ('a, 'b) t -> 'a

                                                            ssqr_diff x y computes the sum of squared differences of every elements in x and its corresponding element in y.

                                                            val cross_entropy' : (float, 'a) t -> (float, 'a) t -> float

                                                            cross_entropy x y calculates the cross entropy between x and y using base e.

                                                            val clip_by_value : ?amin:'a -> ?amax:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            clip_by_value ~amin ~amax x clips the elements in x based on amin and amax. The elements smaller than amin will be set to amin, and the elements greater than amax will be set to amax.

                                                            val clip_by_l2norm : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            clip_by_l2norm t x clips the x according to the threshold set by t.

                                                            val fma : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            fma x y z calculates the `fused multiply add`, i.e. (x * y) + z.

                                                            Tensor Calculus
                                                            val contract1 : (int * int) array -> ('a, 'b) t -> ('a, 'b) t

                                                            contract1 index_pairs x performs indices contraction (a.k.a tensor contraction) on x. index_pairs is an array of contracted indices.

                                                            Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better performance.

                                                            val contract2 : (int * int) array -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            contract2 index_pairs x y performs indices contraction (a.k.a tensor contraction) on two ndarrays x and y. index_pairs is an array of contracted indices, the first element is the index of x, the second is that of y.

                                                            Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better performance.

                                                            Cast functions
                                                            val cast : ('a, 'b) kind -> ('c, 'd) t -> ('a, 'b) t

                                                            cast kind x casts x of type ('c, 'd) t to type ('a, 'b) t specify by the passed in kind parameter. This function is a generalisation of the other casting functions such as cast_s2d, cast_c2z, and etc.

                                                            val cast_s2d : + ('a, 'b) t

                                                            vecnorm ~axis ~p x calculates the generalised vector p-norm along the specified axis. The generalised p-norm is defined as below.

                                                            +  ||v||_p = \Big[ \sum_{k=0}^{N-1} |v_k|^p \Big]^{1/p}

                                                            Parameters: * axis is the axis for reduction. * p is order of norm, default value is 2. * x is the input ndarray.

                                                            Returns: * If p = infinity, then returns ||v||_{\infty} = \max_i(|v(i)|). * If p = -infinity, then returns ||v||_{-\infty} = \min_i(|v(i)|). * Otherwise returns generalised vector p-norm defined above.

                                                            val vecnorm' : ?p:float -> ('a, 'b) t -> 'a

                                                            vecnorm' flattens the input into 1-d vector first, then calculates the generalised p-norm the same as venorm.

                                                            val cumsum : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cumsum ~axis x : performs cumulative sum of the elements along the given axis ~axis. If ~axis is None, then the cumsum is performed along the lowest dimension. The returned result however always remains the same shape.

                                                            val cumprod : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cumprod ~axis x : similar to cumsum but performs cumulative product of the elements along the given ~axis.

                                                            val cummin : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cummin ~axis x : performs cumulative min along axis dimension.

                                                            val cummax : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            cummax ~axis x : performs cumulative max along axis dimension.

                                                            val diff : ?axis:int -> ?n:int -> ('a, 'b) t -> ('a, 'b) t

                                                            diff ~axis ~n x calculates the n-th difference of x along the specified axis.

                                                            Parameters: * axis: axis to calculate the difference. The default value is the highest dimension. * n: how many times to calculate the difference. The default value is 1.

                                                            Return: * The difference ndarray y. Note that the shape of y 1 less than that of x along specified axis.

                                                            val angle : (Stdlib.Complex.t, 'a) t -> (Stdlib.Complex.t, 'a) t

                                                            angle x calculates the phase angle of all complex numbers in x.

                                                            val proj : (Stdlib.Complex.t, 'a) t -> (Stdlib.Complex.t, 'a) t

                                                            proj x computes the projection on Riemann sphere of all elelments in x.

                                                            val lgamma : ('a, 'b) t -> ('a, 'b) t

                                                            lgamma x computes the loggamma of the elements in x and returns the result in a new ndarray.

                                                            val dawsn : ('a, 'b) t -> ('a, 'b) t

                                                            dawsn x computes the Dawson function of the elements in x and returns the result in a new ndarray.

                                                            val i0 : ('a, 'b) t -> ('a, 'b) t

                                                            i0 x computes the modified Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val i0e : ('a, 'b) t -> ('a, 'b) t

                                                            i0e x computes the exponentially scaled modified Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val i1 : ('a, 'b) t -> ('a, 'b) t

                                                            i1 x computes the modified Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val i1e : ('a, 'b) t -> ('a, 'b) t

                                                            i1e x computes the exponentially scaled modified Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val iv : v:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            iv v x computes modified Bessel function of x of real order v

                                                            val scalar_iv : v:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_iv v x computes the modified Bessel function of x of real order v.

                                                            val iv_scalar : v:('a, 'b) t -> 'a -> ('a, 'b) t

                                                            iv_scalar v x computes modified Bessel function of x of real order v

                                                            val j0 : ('a, 'b) t -> ('a, 'b) t

                                                            j0 x computes the Bessel function of order 0 of the elements in x and returns the result in a new ndarray.

                                                            val j1 : ('a, 'b) t -> ('a, 'b) t

                                                            j1 x computes the Bessel function of order 1 of the elements in x and returns the result in a new ndarray.

                                                            val jv : v:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            jv v x computes Bessel function the first kind of x of real order v

                                                            val scalar_jv : v:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_jv v x computes the Bessel function of the first kind of x of real order v.

                                                            val jv_scalar : v:('a, 'b) t -> 'a -> ('a, 'b) t

                                                            jv_scalar v x computes Bessel function of the first kind of x of real order v

                                                            Binary math operators
                                                            val add : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            add x y adds all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            General broadcast operation is automatically applied to add/sub/mul/div, etc. The function compares the dimension element-wise from the highest to the lowest with the following broadcast rules (same as numpy): 1. equal; 2. either is 1.

                                                            val sub : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            sub x y subtracts all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val mul : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            mul x y multiplies all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val div : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            div x y divides all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val add_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            add_scalar x a adds a scalar value a to each element in x, and returns the result in a new ndarray.

                                                            val sub_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            sub_scalar x a subtracts a scalar value a from each element in x, and returns the result in a new ndarray.

                                                            val mul_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            mul_scalar x a multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val div_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            div_scalar x a divides each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_add : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_add a x adds a scalar value a to each element in x, and returns the result in a new ndarray.

                                                            val scalar_sub : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_sub a x subtracts each element in x from a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_mul : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_mul a x multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

                                                            val scalar_div : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_div a x divides a scalar value a by each element in x, and returns the result in a new ndarray.

                                                            val pow : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            pow x y computes pow(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val scalar_pow : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            scalar_pow a x computes the power value of a scalar value a using the elements in a ndarray x.

                                                            val pow_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

                                                            pow_scalar x a computes each element in x power to a.

                                                            val atan2 : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            atan2 x y computes atan2(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val scalar_atan2 : float -> (float, 'a) t -> (float, 'a) t

                                                            scalar_atan2 a x

                                                            val atan2_scalar : (float, 'a) t -> float -> (float, 'a) t

                                                            scalar_atan2 x a

                                                            val hypot : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            hypot x y computes sqrt(x*x + y*y) of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val min2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            min2 x y computes the minimum of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val max2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            max2 x y computes the maximum of all the elements in x and y elementwise, and returns the result in a new ndarray.

                                                            val fmod : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

                                                            fmod x y performs float mod division.

                                                            val fmod_scalar : (float, 'a) t -> float -> (float, 'a) t

                                                            fmod_scalar x a performs mod division between x and scalar a.

                                                            val scalar_fmod : float -> (float, 'a) t -> (float, 'a) t

                                                            scalar_fmod x a performs mod division between scalar a and x.

                                                            val ssqr' : ('a, 'b) t -> 'a -> 'a

                                                            ssqr x a computes the sum of squared differences of all the elements in x from constant a. This function only computes the square of each element rather than the conjugate transpose as l2norm_sqr does.

                                                            val ssqr_diff' : ('a, 'b) t -> ('a, 'b) t -> 'a

                                                            ssqr_diff x y computes the sum of squared differences of every elements in x and its corresponding element in y.

                                                            val cross_entropy' : (float, 'a) t -> (float, 'a) t -> float

                                                            cross_entropy x y calculates the cross entropy between x and y using base e.

                                                            val clip_by_value : ?amin:'a -> ?amax:'a -> ('a, 'b) t -> ('a, 'b) t

                                                            clip_by_value ~amin ~amax x clips the elements in x based on amin and amax. The elements smaller than amin will be set to amin, and the elements greater than amax will be set to amax.

                                                            val clip_by_l2norm : 'a -> ('a, 'b) t -> ('a, 'b) t

                                                            clip_by_l2norm t x clips the x according to the threshold set by t.

                                                            val fma : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            fma x y z calculates the `fused multiply add`, i.e. (x * y) + z.

                                                            Tensor Calculus
                                                            val contract1 : (int * int) array -> ('a, 'b) t -> ('a, 'b) t

                                                            contract1 index_pairs x performs indices contraction (a.k.a tensor contraction) on x. index_pairs is an array of contracted indices.

                                                            Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better performance.

                                                            val contract2 : (int * int) array -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            contract2 index_pairs x y performs indices contraction (a.k.a tensor contraction) on two ndarrays x and y. index_pairs is an array of contracted indices, the first element is the index of x, the second is that of y.

                                                            Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better performance.

                                                            Cast functions
                                                            val cast : ('a, 'b) kind -> ('c, 'd) t -> ('a, 'b) t

                                                            cast kind x casts x of type ('c, 'd) t to type ('a, 'b) t specify by the passed in kind parameter. This function is a generalisation of the other casting functions such as cast_s2d, cast_c2z, and etc.

                                                            val cast_s2d : (float, Stdlib.Bigarray.float32_elt) t -> (float, Stdlib.Bigarray.float64_elt) t

                                                            cast_s2d x casts x from float32 to float64.

                                                            val cast_d2s : (float, Stdlib.Bigarray.float64_elt) t -> @@ -119,238 +143,238 @@ ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val conv2d : + ('a, 'b) t

                                                            conv1d ?padding input kernel strides applies a 1-dimensional convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the convolution.
                                                            val conv2d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val conv3d : + ('a, 'b) t

                                                            conv2d ?padding input kernel strides applies a 2-dimensional convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the convolution.
                                                            val conv3d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv1d : + ('a, 'b) t

                                                            conv3d ?padding input kernel strides applies a 3-dimensional convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the convolution.
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv2d : + ('a, 'b) t

                                                            dilated_conv1d ?padding input kernel strides dilations applies a 1-dimensional dilated convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. Returns the result of the dilated convolution.
                                                            val dilated_conv2d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv3d : + ('a, 'b) t

                                                            dilated_conv2d ?padding input kernel strides dilations applies a 2-dimensional dilated convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. Returns the result of the dilated convolution.
                                                            val dilated_conv3d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv1d : + ('a, 'b) t

                                                            dilated_conv3d ?padding input kernel strides dilations applies a 3-dimensional dilated convolution over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. Returns the result of the dilated convolution.
                                                            val transpose_conv1d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv2d : + ('a, 'b) t

                                                            transpose_conv1d ?padding input kernel strides applies a 1-dimensional transposed convolution (deconvolution) over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the transposed convolution.
                                                            val transpose_conv2d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv3d : + ('a, 'b) t

                                                            transpose_conv2d ?padding input kernel strides applies a 2-dimensional transposed convolution (deconvolution) over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the transposed convolution.
                                                            val transpose_conv3d : ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool1d : + ('a, 'b) t

                                                            transpose_conv3d ?padding input kernel strides applies a 3-dimensional transposed convolution (deconvolution) over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. Returns the result of the transposed convolution.
                                                            val max_pool1d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool2d : + ('a, 'b) t

                                                            max_pool1d ?padding input pool_size strides applies a 1-dimensional max pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the max pooling operation.
                                                            val max_pool2d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool3d : + ('a, 'b) t

                                                            max_pool2d ?padding input pool_size strides applies a 2-dimensional max pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the max pooling operation.
                                                            val max_pool3d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool1d : + ('a, 'b) t

                                                            max_pool3d ?padding input pool_size strides applies a 3-dimensional max pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the max pooling operation.
                                                            val avg_pool1d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool2d : + ('a, 'b) t

                                                            avg_pool1d ?padding input pool_size strides applies a 1-dimensional average pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the average pooling operation.
                                                            val avg_pool2d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool3d : + ('a, 'b) t

                                                            avg_pool2d ?padding input pool_size strides applies a 2-dimensional average pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the average pooling operation.
                                                            val avg_pool3d : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool2d_argmax : + ('a, 'b) t

                                                            avg_pool3d ?padding input pool_size strides applies a 3-dimensional average pooling operation over an input tensor.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns the result of the average pooling operation.
                                                            val max_pool2d_argmax : ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - ('a, 'b) t * (int64, Stdlib.Bigarray.int64_elt) t

                                                            TODO

                                                            val upsampling2d : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            TODO

                                                            val conv1d_backward_input : + ('a, 'b) t * (int64, Stdlib.Bigarray.int64_elt) t

                                                            max_pool2d_argmax ?padding input pool_size strides applies a 2-dimensional max pooling operation over an input tensor, returning both the pooled output and the indices of the maximum values.

                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. Returns a tuple containing the pooled output and the indices of the maximum values.
                                                            val upsampling2d : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            upsampling2d input size performs a 2-dimensional upsampling on the input tensor input, scaling it according to the specified size. Returns the upsampled tensor.

                                                            val conv1d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv1d_backward_kernel : + ('a, 'b) t

                                                            conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val conv1d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv2d_backward_input : + ('a, 'b) t

                                                            conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val conv2d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv2d_backward_kernel : + ('a, 'b) t

                                                            conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val conv2d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv3d_backward_input : + ('a, 'b) t

                                                            conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val conv3d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val conv3d_backward_kernel : + ('a, 'b) t

                                                            conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val conv3d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv1d_backward_input : + ('a, 'b) t

                                                            conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val dilated_conv1d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv1d_backward_kernel : + ('a, 'b) t

                                                            dilated_conv1d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val dilated_conv1d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv2d_backward_input : + ('a, 'b) t

                                                            dilated_conv1d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val dilated_conv2d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv2d_backward_kernel : + ('a, 'b) t

                                                            dilated_conv2d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val dilated_conv2d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv3d_backward_input : + ('a, 'b) t

                                                            dilated_conv2d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val dilated_conv3d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val dilated_conv3d_backward_kernel : + ('a, 'b) t

                                                            dilated_conv3d_backward_input input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val dilated_conv3d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv1d_backward_input : + ('a, 'b) t

                                                            dilated_conv3d_backward_kernel input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional dilated convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val transpose_conv1d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv1d_backward_kernel : + ('a, 'b) t

                                                            transpose_conv1d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val transpose_conv1d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv2d_backward_input : + ('a, 'b) t

                                                            transpose_conv1d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val transpose_conv2d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv2d_backward_kernel : + ('a, 'b) t

                                                            transpose_conv2d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val transpose_conv2d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv3d_backward_input : + ('a, 'b) t

                                                            transpose_conv2d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val transpose_conv3d_backward_input : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val transpose_conv3d_backward_kernel : + ('a, 'b) t

                                                            transpose_conv3d_backward_input input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val transpose_conv3d_backward_kernel : ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool1d_backward : + ('a, 'b) t

                                                            transpose_conv3d_backward_kernel input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional transposed convolutional layer.

                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. Returns the gradient of the loss with respect to the kernel.
                                                            val max_pool1d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool2d_backward : + ('a, 'b) t

                                                            max_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional max pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val max_pool2d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val max_pool3d_backward : + ('a, 'b) t

                                                            max_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional max pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val max_pool3d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool1d_backward : + ('a, 'b) t

                                                            max_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional max pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val avg_pool1d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool2d_backward : + ('a, 'b) t

                                                            avg_pool1d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional average pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val avg_pool2d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val avg_pool3d_backward : + ('a, 'b) t

                                                            avg_pool2d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional average pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val avg_pool3d_backward : Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - ('a, 'b) t

                                                            TODO

                                                            val upsampling2d_backward : ('a, 'b) t -> int array -> ('a, 'b) t -> ('a, 'b) t

                                                            TODO

                                                            Helper functions

                                                            The following functions are helper functions for some other functions in both Ndarray and Ndview modules. In general, you are not supposed to use these functions directly.

                                                            val print_element : ('a, 'b) kind -> 'a -> unit

                                                            print_element kind a prints the value of a single element.

                                                            val print_index : int array -> unit

                                                            print_index i prints out the index of an element.

                                                            val _check_transpose_axis : int array -> int -> unit

                                                            _check_transpose_axis a d checks whether a is a legiti('a, 'b) te transpose index.

                                                            val one_hot : int -> ('a, 'b) t -> ('a, 'b) t

                                                            one_hot idx depth creates one-hot vectors according to the indices ndarray and the specified depth. If idx is rank N, then the return is rank N+1. More specifically, if idx is of shape [|a;b;c|], the return is of shape [|a;b;c;depth|].

                                                            val sum_slices : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            sum_slices ~axis:2 x for x of [|2;3;4;5|], it returns an ndarray of shape [|4;5|]. Currently, the operation is done using gemm, it is fast but consumes more memory.

                                                            val slide : + ('a, 'b) t

                                                            avg_pool3d_backward padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional average pooling layer.

                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            val upsampling2d_backward : ('a, 'b) t -> int array -> ('a, 'b) t -> ('a, 'b) t

                                                            upsampling2d_backward input size grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional upsampling layer.

                                                            • input is the original input tensor.
                                                            • size specifies the upsampling factors for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the upsampling layer. Returns the gradient of the loss with respect to the input tensor.
                                                            Helper functions

                                                            The following functions are helper functions for some other functions in both Ndarray and Ndview modules. In general, you are not supposed to use these functions directly.

                                                            val print_element : ('a, 'b) kind -> 'a -> unit

                                                            print_element kind a prints the value of a single element.

                                                            val print_index : int array -> unit

                                                            print_index i prints out the index of an element.

                                                            val _check_transpose_axis : int array -> int -> unit

                                                            _check_transpose_axis a d checks whether a is a legiti('a, 'b) te transpose index.

                                                            val one_hot : int -> ('a, 'b) t -> ('a, 'b) t

                                                            one_hot idx depth creates one-hot vectors according to the indices ndarray and the specified depth. If idx is rank N, then the return is rank N+1. More specifically, if idx is of shape [|a;b;c|], the return is of shape [|a;b;c;depth|].

                                                            val sum_slices : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

                                                            sum_slices ~axis:2 x for x of [|2;3;4;5|], it returns an ndarray of shape [|4;5|]. Currently, the operation is done using gemm, it is fast but consumes more memory.

                                                            val slide : ?axis:int -> ?ofs:int -> ?step:int -> window:int -> ('a, 'b) t -> - ('a, 'b) t

                                                            slide ~axis ~window x generates a new ndarray by sliding a window along specified axis in x. E.g., if x has shape [|a;b;c|] and axis = 1, then [|a; number of windows; window; c|] is the shape of the returned ndarray.

                                                            Parameters: * axis is the axis for sliding, the default is -1, i.e. highest dimension. * ofs is the starting position of the sliding window. The default is 0. * step is the step size, the default is 1. * window is the size of the sliding window.

                                                            In-place modification
                                                            val create_ : out:('a, 'b) t -> 'a -> unit

                                                            TODO

                                                            val uniform_ : ?a:'a -> ?b:'a -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val gaussian_ : ?mu:'a -> ?sigma:'a -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val poisson_ : mu:float -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val sequential_ : ?a:'a -> ?step:'a -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val bernoulli_ : ?p:float -> out:('a, 'b) t -> unit

                                                            TODO

                                                            val zeros_ : out:('a, 'b) t -> unit

                                                            TODO

                                                            val ones_ : out:('a, 'b) t -> unit

                                                            TODO

                                                            val one_hot_ : out:('a, 'b) t -> int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val sort_ : ('a, 'b) t -> unit

                                                            sort_ x performs in-place quicksort of the elelments in x.

                                                            val get_fancy_ : out:('a, 'b) t -> Owl_types.index list -> ('a, 'b) t -> unit

                                                            TODO

                                                            val set_fancy_ : + ('a, 'b) t

                                                            slide ~axis ~window x generates a new ndarray by sliding a window along specified axis in x. E.g., if x has shape [|a;b;c|] and axis = 1, then [|a; number of windows; window; c|] is the shape of the returned ndarray.

                                                            Parameters: * axis is the axis for sliding, the default is -1, i.e. highest dimension. * ofs is the starting position of the sliding window. The default is 0. * step is the step size, the default is 1. * window is the size of the sliding window.

                                                            In-place modification
                                                            val create_ : out:('a, 'b) t -> 'a -> unit

                                                            create_ ~out value initializes the matrix out in-place with the scalar value value. This operation modifies the contents of out.

                                                            val uniform_ : ?a:'a -> ?b:'a -> out:('a, 'b) t -> unit

                                                            uniform_ ?a ?b ~out fills the matrix out in-place with random values drawn from a uniform distribution over the interval [a, b\). If a and b are not provided, the default interval is [0, 1\).

                                                            val gaussian_ : ?mu:'a -> ?sigma:'a -> out:('a, 'b) t -> unit

                                                            gaussian_ ?mu ?sigma ~out fills the matrix out in-place with random values drawn from a Gaussian distribution with mean mu and standard deviation sigma. If mu is not provided, the default mean is 0. If sigma is not provided, the default standard deviation is 1.

                                                            val poisson_ : mu:float -> out:('a, 'b) t -> unit

                                                            poisson_ ~mu ~out fills the matrix out in-place with random values drawn from a Poisson distribution with mean mu.

                                                            val sequential_ : ?a:'a -> ?step:'a -> out:('a, 'b) t -> unit

                                                            sequential_ ?a ?step ~out fills the matrix out in-place with a sequence of values starting from a with a step of step. If a is not provided, the sequence starts from 0. If step is not provided, the step size is 1.

                                                            val bernoulli_ : ?p:float -> out:('a, 'b) t -> unit

                                                            bernoulli_ ?p ~out fills the matrix out in-place with random values drawn from a Bernoulli distribution with probability p of being 1. If p is not provided, the default probability is 0.5.

                                                            val zeros_ : out:('a, 'b) t -> unit

                                                            zeros_ ~out fills the matrix out in-place with zeros.

                                                            val ones_ : out:('a, 'b) t -> unit

                                                            ones_ ~out fills the matrix out in-place with ones.

                                                            val one_hot_ : out:('a, 'b) t -> int -> ('a, 'b) t -> unit

                                                            one_hot_ ~out depth indices fills the matrix out in-place with one-hot encoded vectors according to the specified depth and the indices.

                                                            val sort_ : ('a, 'b) t -> unit

                                                            sort_ x performs in-place quicksort on the elements in x, sorting them in ascending order.

                                                            val get_fancy_ : out:('a, 'b) t -> Owl_types.index list -> ('a, 'b) t -> unit

                                                            get_fancy_ ~out indices src extracts elements from the source matrix src according to the list of indices and stores them in out. This operation is performed in-place on out.

                                                            val set_fancy_ : out:('a, 'b) t -> Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val get_slice_ : out:('a, 'b) t -> int list list -> ('a, 'b) t -> unit

                                                            TODO

                                                            val set_slice_ : + unit

                                                            set_fancy_ ~out indices src sets the elements in out at the positions specified by indices with the values from the source matrix src. This operation is performed in-place on out.

                                                            val get_slice_ : out:('a, 'b) t -> int list list -> ('a, 'b) t -> unit

                                                            get_slice_ ~out slices src extracts a slice from the source matrix src according to the list of slices and stores it in out. This operation is performed in-place on out.

                                                            val set_slice_ : out:('a, 'b) t -> int list list -> ('a, 'b) t -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            copy_ ~out src copies the data from ndarray src to destination out.

                                                            val reshape_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            TODO

                                                            val reverse_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            TODO

                                                            val transpose_ : out:('a, 'b) t -> ?axis:int array -> ('a, 'b) t -> unit

                                                            transpose_ ~out x is similar to transpose x but the output is written to out.

                                                            val repeat_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            repeat_ ~out x reps is similar to repeat x reps but the output is written to out.

                                                            val tile_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            tile_ ~out x reps is similar to tile x reps but the output is written to out.

                                                            val pad_ : out:('a, 'b) t -> ?v:'a -> int list list -> ('a, 'b) t -> unit

                                                            pad_ ~out ?v p x is similar to pad ?v p x but the output is written to out.

                                                            val sum_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val min_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val max_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            TODO

                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            add_ x y is similar to add function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            sub_ x y is similar to sub function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            mul_ x y is similar to mul function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            div_ x y is similar to div function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val pow_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            pow_ x y is similar to pow function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val atan2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            atan2_ x y is similar to atan2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val hypot_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            hypot_ x y is similar to hypot function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val fmod_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fmod_ x y is similar to fmod function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val min2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            min2_ x y is similar to min2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val max2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            max2_ x y is similar to max2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            add_scalar_ x y is similar to add_scalar function but the output is written to x.

                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            sub_scalar_ x y is similar to sub_scalar function but the output is written to x.

                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            mul_scalar_ x y is similar to mul_scalar function but the output is written to x.

                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            div_scalar_ x y is similar to div_scalar function but the output is written to x.

                                                            val pow_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            pow_scalar_ x y is similar to pow_scalar function but the output is written to x.

                                                            val atan2_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            atan2_scalar_ x y is similar to atan2_scalar function but the output is written to x.

                                                            val fmod_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            fmod_scalar_ x y is similar to fmod_scalar function but the output is written to x.

                                                            val scalar_add_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_add_ a x is similar to scalar_add function but the output is written to x.

                                                            val scalar_sub_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_sub_ a x is similar to scalar_sub function but the output is written to x.

                                                            val scalar_mul_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_mul_ a x is similar to scalar_mul function but the output is written to x.

                                                            val scalar_div_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_div_ a x is similar to scalar_div function but the output is written to x.

                                                            val scalar_pow_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_pow_ a x is similar to scalar_pow function but the output is written to x.

                                                            val scalar_atan2_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_atan2_ a x is similar to scalar_atan2 function but the output is written to x.

                                                            val scalar_fmod_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_fmod_ a x is similar to scalar_fmod function but the output is written to x.

                                                            val clip_by_value_ : + unit

                                                            set_slice_ ~out slices src sets the slice in out defined by slices with the values from the source matrix src. This operation is performed in-place on out.

                                                            val copy_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            copy_ ~out src copies the data from the source matrix src to the destination matrix out. This operation is performed in-place on out.

                                                            val reshape_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            reshape_ ~out src reshapes the source matrix src and stores the result in out. The total number of elements must remain the same. This operation is performed in-place on out.

                                                            val reverse_ : out:('a, 'b) t -> ('a, 'b) t -> unit

                                                            reverse_ ~out src reverses the elements of the source matrix src along each dimension and stores the result in out. This operation is performed in-place on out.

                                                            val transpose_ : out:('a, 'b) t -> ?axis:int array -> ('a, 'b) t -> unit

                                                            transpose_ ~out x is similar to transpose x but the output is written to out.

                                                            val repeat_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            repeat_ ~out x reps is similar to repeat x reps but the output is written to out.

                                                            val tile_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            tile_ ~out x reps is similar to tile x reps but the output is written to out.

                                                            val pad_ : out:('a, 'b) t -> ?v:'a -> int list list -> ('a, 'b) t -> unit

                                                            pad_ ~out ?v p x is similar to pad ?v p x but the output is written to out.

                                                            val sum_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            sum_ ~out ~axis x computes the sum of elements along the specified axis of the array x and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • axis specifies the axis along which to compute the sum. This operation is performed in-place on out.
                                                            val min_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            min_ ~out ~axis x computes the minimum value along the specified axis of the array x and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • axis specifies the axis along which to compute the minimum value. This operation is performed in-place on out.
                                                            val max_ : out:('a, 'b) t -> axis:int -> ('a, 'b) t -> unit

                                                            max_ ~out ~axis x computes the maximum value along the specified axis of the array x and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • axis specifies the axis along which to compute the maximum value. This operation is performed in-place on out.
                                                            val add_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            add_ x y is similar to add function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val sub_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            sub_ x y is similar to sub function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val mul_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            mul_ x y is similar to mul function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val div_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            div_ x y is similar to div function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val pow_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            pow_ x y is similar to pow function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val atan2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            atan2_ x y is similar to atan2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val hypot_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            hypot_ x y is similar to hypot function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val fmod_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fmod_ x y is similar to fmod function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val min2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            min2_ x y is similar to min2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val max2_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            max2_ x y is similar to max2 function but the output is written to out. You need to make sure out is big enough to hold the output result.

                                                            val add_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            add_scalar_ x y is similar to add_scalar function but the output is written to x.

                                                            val sub_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            sub_scalar_ x y is similar to sub_scalar function but the output is written to x.

                                                            val mul_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            mul_scalar_ x y is similar to mul_scalar function but the output is written to x.

                                                            val div_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            div_scalar_ x y is similar to div_scalar function but the output is written to x.

                                                            val pow_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            pow_scalar_ x y is similar to pow_scalar function but the output is written to x.

                                                            val atan2_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            atan2_scalar_ x y is similar to atan2_scalar function but the output is written to x.

                                                            val fmod_scalar_ : ?out:('a, 'b) t -> ('a, 'b) t -> 'a -> unit

                                                            fmod_scalar_ x y is similar to fmod_scalar function but the output is written to x.

                                                            val scalar_add_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_add_ a x is similar to scalar_add function but the output is written to x.

                                                            val scalar_sub_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_sub_ a x is similar to scalar_sub function but the output is written to x.

                                                            val scalar_mul_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_mul_ a x is similar to scalar_mul function but the output is written to x.

                                                            val scalar_div_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_div_ a x is similar to scalar_div function but the output is written to x.

                                                            val scalar_pow_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_pow_ a x is similar to scalar_pow function but the output is written to x.

                                                            val scalar_atan2_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_atan2_ a x is similar to scalar_atan2 function but the output is written to x.

                                                            val scalar_fmod_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            scalar_fmod_ a x is similar to scalar_fmod function but the output is written to x.

                                                            val clip_by_value_ : ?out:('a, 'b) t -> ?amin:'a -> ?amax:'a -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val clip_by_l2norm_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            TODO

                                                            val fma_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fma_ ~out x y z is similar to fma x y z function but the output is written to out.

                                                            val dot_ : + unit

                                                            clip_by_value_ ?out ?amin ?amax x clips the values of the array x to lie within the range amin, amax and stores the result in out.

                                                            • out is the optional output array where the result will be stored. If not provided, x is modified in-place.
                                                            • amin is the optional minimum value to clip to. If not provided, no minimum clipping is applied.
                                                            • amax is the optional maximum value to clip to. If not provided, no maximum clipping is applied. This operation is performed in-place.
                                                            val clip_by_l2norm_ : ?out:('a, 'b) t -> 'a -> ('a, 'b) t -> unit

                                                            clip_by_l2norm_ ?out l2norm x clips the L2 norm of the array x to the specified value l2norm and stores the result in out.

                                                            • out is the optional output array where the result will be stored. If not provided, x is modified in-place.
                                                            • l2norm specifies the maximum L2 norm. This operation is performed in-place.
                                                            val fma_ : ?out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> unit

                                                            fma_ ~out x y z is similar to fma x y z function but the output is written to out.

                                                            val dot_ : ?transa:bool -> ?transb:bool -> ?alpha:'a -> @@ -364,255 +388,255 @@ ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val conv2d_ : + unit

                                                            conv1d_ ~out ?padding input kernel strides applies a 1-dimensional convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val conv2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val conv3d_ : + unit

                                                            conv2d_ ~out ?padding input kernel strides applies a 2-dimensional convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val conv3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val dilated_conv1d_ : + unit

                                                            conv3d_ ~out ?padding input kernel strides applies a 3-dimensional convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val dilated_conv1d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val dilated_conv2d_ : + unit

                                                            dilated_conv1d_ ~out ?padding input kernel strides dilations applies a 1-dimensional dilated convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. This operation is performed in-place on out.
                                                            val dilated_conv2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val dilated_conv3d_ : + unit

                                                            dilated_conv2d_ ~out ?padding input kernel strides dilations applies a 2-dimensional dilated convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. This operation is performed in-place on out.
                                                            val dilated_conv3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val transpose_conv1d_ : + unit

                                                            dilated_conv3d_ ~out ?padding input kernel strides dilations applies a 3-dimensional dilated convolution over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the convolutional kernel.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension. This operation is performed in-place on out.
                                                            val transpose_conv1d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val transpose_conv2d_ : + unit

                                                            transpose_conv1d_ ~out ?padding input kernel strides applies a 1-dimensional transposed convolution (deconvolution) over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the transposed convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val transpose_conv2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val transpose_conv3d_ : + unit

                                                            transpose_conv2d_ ~out ?padding input kernel strides applies a 2-dimensional transposed convolution (deconvolution) over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the transposed convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val transpose_conv3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> ('a, 'b) t -> int array -> - unit

                                                            TODO

                                                            val max_pool1d_ : + unit

                                                            transpose_conv3d_ ~out ?padding input kernel strides applies a 3-dimensional transposed convolution (deconvolution) over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • kernel is the transposed convolutional kernel.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val max_pool1d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val max_pool2d_ : + unit

                                                            max_pool1d_ ~out ?padding input pool_size strides applies a 1-dimensional max pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val max_pool2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val max_pool3d_ : + unit

                                                            max_pool2d_ ~out ?padding input pool_size strides applies a 2-dimensional max pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val max_pool3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val avg_pool1d_ : + unit

                                                            max_pool3d_ ~out ?padding input pool_size strides applies a 3-dimensional max pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val avg_pool1d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val avg_pool2d_ : + unit

                                                            avg_pool1d_ ~out ?padding input pool_size strides applies a 1-dimensional average pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val avg_pool2d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val avg_pool3d_ : + unit

                                                            avg_pool2d_ ~out ?padding input pool_size strides applies a 2-dimensional average pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val avg_pool3d_ : out:('a, 'b) t -> ?padding:Owl_types.padding -> ('a, 'b) t -> int array -> int array -> - unit

                                                            TODO

                                                            val upsampling2d_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            TODO

                                                            val conv1d_backward_input_ : + unit

                                                            avg_pool3d_ ~out ?padding input pool_size strides applies a 3-dimensional average pooling operation over an input tensor and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • padding specifies the padding strategy to use ('valid' or 'same').
                                                            • input is the input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension. This operation is performed in-place on out.
                                                            val upsampling2d_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> unit

                                                            upsampling2d_ ~out input size performs a 2-dimensional upsampling on the input tensor input, scaling it according to the specified size, and stores the result in out.

                                                            • out is the output array where the result will be stored.
                                                            • input is the input tensor to be upsampled.
                                                            • size specifies the upsampling factors for each dimension. This operation is performed in-place on out.
                                                            val conv1d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv1d_backward_kernel_ : + unit

                                                            conv1d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv1d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv2d_backward_input_ : + unit

                                                            conv1d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv2d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv2d_backward_kernel_ : + unit

                                                            conv2d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv2d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv3d_backward_input_ : + unit

                                                            conv2d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv3d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val conv3d_backward_kernel_ : + unit

                                                            conv3d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val conv3d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv1d_backward_input_ : + unit

                                                            conv3d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv1d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv1d_backward_kernel_ : + unit

                                                            dilated_conv1d_backward_input_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv1d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv2d_backward_input_ : + unit

                                                            dilated_conv1d_backward_kernel_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv2d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv2d_backward_kernel_ : + unit

                                                            dilated_conv2d_backward_input_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv2d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv3d_backward_input_ : + unit

                                                            dilated_conv2d_backward_kernel_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv3d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val dilated_conv3d_backward_kernel_ : + unit

                                                            dilated_conv3d_backward_input_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val dilated_conv3d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv1d_backward_input_ : + unit

                                                            dilated_conv3d_backward_kernel_ ~out input kernel strides dilations grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional dilated convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the dilated convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • dilations specify the dilation factor for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the dilated convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv1d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv1d_backward_kernel_ : + unit

                                                            transpose_conv1d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv1d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv2d_backward_input_ : + unit

                                                            transpose_conv1d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 1-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv2d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv2d_backward_kernel_ : + unit

                                                            transpose_conv2d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv2d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv3d_backward_input_ : + unit

                                                            transpose_conv2d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 2-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv3d_backward_input_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val transpose_conv3d_backward_kernel_ : + unit

                                                            transpose_conv3d_backward_input_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val transpose_conv3d_backward_kernel_ : out:('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val max_pool1d_backward_ : + unit

                                                            transpose_conv3d_backward_kernel_ ~out input kernel strides grad_output computes the gradient of the loss with respect to the kernel of a 3-dimensional transposed convolutional layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • kernel is the transposed convolutional kernel used during the forward pass.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the transposed convolutional layer. This operation is performed in-place on out.
                                                            val max_pool1d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val max_pool2d_backward_ : + unit

                                                            max_pool1d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional max pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. This operation is performed in-place on out.
                                                            val max_pool2d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val max_pool3d_backward_ : + unit

                                                            max_pool2d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional max pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. This operation is performed in-place on out.
                                                            val max_pool3d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val avg_pool1d_backward_ : + unit

                                                            max_pool3d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional max pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the max pooling layer. This operation is performed in-place on out.
                                                            val avg_pool1d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val avg_pool2d_backward_ : + unit

                                                            avg_pool1d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 1-dimensional average pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. This operation is performed in-place on out.
                                                            val avg_pool2d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val avg_pool3d_backward_ : + unit

                                                            avg_pool2d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional average pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. This operation is performed in-place on out.
                                                            val avg_pool3d_backward_ : out:('a, 'b) t -> Owl_types.padding -> ('a, 'b) t -> int array -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val upsampling2d_backward_ : + unit

                                                            avg_pool3d_backward_ ~out padding input pool_size strides grad_output computes the gradient of the loss with respect to the input tensor of a 3-dimensional average pooling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • padding specifies the padding strategy used during the forward pass.
                                                            • input is the original input tensor.
                                                            • pool_size specifies the size of the pooling window.
                                                            • strides specify the stride length for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the average pooling layer. This operation is performed in-place on out.
                                                            val upsampling2d_backward_ : out:('a, 'b) t -> ('a, 'b) t -> int array -> ('a, 'b) t -> - unit

                                                            TODO

                                                            val fused_adagrad_ : ?out:('a, 'b) t -> rate:'a -> eps:'a -> ('a, 'b) t -> unit

                                                            TODO

                                                            Matrix functions
                                                            type area = {
                                                            1. a : int;
                                                            2. b : int;
                                                            3. c : int;
                                                            4. d : int;
                                                            }

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val area : int -> int -> int -> int -> area

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_area_to : ('a, 'b) t -> area -> ('a, 'b) t -> area -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val row_num : ('a, 'b) t -> int

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val col_num : ('a, 'b) t -> int

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val row : ('a, 'b) t -> int -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val col : ('a, 'b) t -> int -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val rows : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val cols : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_row_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_col_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val dot : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val diag : ?k:int -> ('a, 'b) t -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val trace : ('a, 'b) t -> 'a

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_rows : ('a, 'b) t -> ('a, 'b) t array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_rows : ('a, 'b) t array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_cols : ('a, 'b) t -> ('a, 'b) t array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_cols : ('a, 'b) t array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_arrays : ('a, 'b) t -> 'a array array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_arrays : ('a, 'b) kind -> 'a array array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val draw_rows : + unit

                                                            upsampling2d_backward_ ~out input size grad_output computes the gradient of the loss with respect to the input tensor of a 2-dimensional upsampling layer and stores it in out.

                                                            • out is the output array where the gradient will be stored.
                                                            • input is the original input tensor.
                                                            • size specifies the upsampling factors for each dimension.
                                                            • grad_output is the gradient of the loss with respect to the output of the upsampling layer. This operation is performed in-place on out.
                                                            val fused_adagrad_ : ?out:('a, 'b) t -> rate:'a -> eps:'a -> ('a, 'b) t -> unit

                                                            fused_adagrad_ ?out ~rate ~eps grad applies the Adagrad optimization algorithm to the gradients grad with a given learning rate and epsilon eps for numerical stability, storing the result in out.

                                                            • out is the optional output array where the updated parameters will be stored. If not provided, grad is modified in-place.
                                                            • rate specifies the learning rate.
                                                            • eps specifies the epsilon value for numerical stability. This operation is performed in-place.
                                                            Matrix functions
                                                            type area = {
                                                            1. a : int;
                                                            2. b : int;
                                                            3. c : int;
                                                            4. d : int;
                                                            }

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val area : int -> int -> int -> int -> area

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_area_to : ('a, 'b) t -> area -> ('a, 'b) t -> area -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val row_num : ('a, 'b) t -> int

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val col_num : ('a, 'b) t -> int

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val row : ('a, 'b) t -> int -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val col : ('a, 'b) t -> int -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val rows : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val cols : ('a, 'b) t -> int array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_row_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val copy_col_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val dot : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val diag : ?k:int -> ('a, 'b) t -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val trace : ('a, 'b) t -> 'a

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_rows : ('a, 'b) t -> ('a, 'b) t array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_rows : ('a, 'b) t array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_cols : ('a, 'b) t -> ('a, 'b) t array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_cols : ('a, 'b) t array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val to_arrays : ('a, 'b) t -> 'a array array

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val of_arrays : ('a, 'b) kind -> 'a array array -> ('a, 'b) t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val draw_rows : ?replacement:bool -> ('a, 'b) t -> int -> diff --git a/docs/owl/Owl_dense_ndarray_intf/index.html b/docs/owl/Owl_dense_ndarray_intf/index.html index a22c28f78..75eb281fb 100644 --- a/docs/owl/Owl_dense_ndarray_intf/index.html +++ b/docs/owl/Owl_dense_ndarray_intf/index.html @@ -1,2 +1,2 @@ -Owl_dense_ndarray_intf (owl.Owl_dense_ndarray_intf)

                                                            Module Owl_dense_ndarray_intf

                                                            module type Common = sig ... end
                                                            module type Real = sig ... end
                                                            module type Complex = sig ... end
                                                            module type Distribution = sig ... end
                                                            module type NN = sig ... end
                                                            +Owl_dense_ndarray_intf (owl.Owl_dense_ndarray_intf)

                                                            Module Owl_dense_ndarray_intf

                                                            module type Common = sig ... end
                                                            module type Real = sig ... end
                                                            module type Complex = sig ... end
                                                            module type Distribution = sig ... end
                                                            module type NN = sig ... end
                                                            diff --git a/docs/owl/Owl_dense_ndarray_intf/module-type-Common/index.html b/docs/owl/Owl_dense_ndarray_intf/module-type-Common/index.html index 9251c16a9..c3edfba5b 100644 --- a/docs/owl/Owl_dense_ndarray_intf/module-type-Common/index.html +++ b/docs/owl/Owl_dense_ndarray_intf/module-type-Common/index.html @@ -1,5 +1,5 @@ -Common (owl.Owl_dense_ndarray_intf.Common)

                                                            Module type Owl_dense_ndarray_intf.Common

                                                            include Owl_base_dense_ndarray_intf.Common
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:float -> int array -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val strides : arr -> int array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val slice_size : arr -> int array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val flatten : arr -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val print : +Common (owl.Owl_dense_ndarray_intf.Common)

                                                            Module type Owl_dense_ndarray_intf.Common

                                                            include Owl_base_dense_ndarray_intf.Common
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:float -> int array -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val strides : arr -> int array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val slice_size : arr -> int array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val flatten : arr -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_dense_ndarray_intf/module-type-Complex/index.html b/docs/owl/Owl_dense_ndarray_intf/module-type-Complex/index.html index a7261aa48..c898dc29e 100644 --- a/docs/owl/Owl_dense_ndarray_intf/module-type-Complex/index.html +++ b/docs/owl/Owl_dense_ndarray_intf/module-type-Complex/index.html @@ -1,2 +1,2 @@ -Complex (owl.Owl_dense_ndarray_intf.Complex)

                                                            Module type Owl_dense_ndarray_intf.Complex

                                                            type elt
                                                            type arr
                                                            type cast_arr
                                                            Complex operations
                                                            val complex : cast_arr -> cast_arr -> arr

                                                            complex re im constructs a complex ndarray/matrix from re and im. re and im contain the real and imaginary part of x respectively.

                                                            Note that both re and im can be complex but must have same type. The real part of re will be the real part of x and the imaginary part of im will be the imaginary part of x.

                                                            val polar : cast_arr -> cast_arr -> arr

                                                            polar rho theta constructs a complex ndarray/matrix from polar coordinates rho and theta. rho contains the magnitudes and theta contains phase angles. Note that the behaviour is undefined if rho has negative elelments or theta has infinity elelments.

                                                            val re : arr -> cast_arr
                                                            val im : arr -> cast_arr
                                                            val sum' : arr -> elt
                                                            +Complex (owl.Owl_dense_ndarray_intf.Complex)

                                                            Module type Owl_dense_ndarray_intf.Complex

                                                            type elt
                                                            type arr
                                                            type cast_arr
                                                            Complex operations
                                                            val complex : cast_arr -> cast_arr -> arr

                                                            complex re im constructs a complex ndarray/matrix from re and im. re and im contain the real and imaginary part of x respectively.

                                                            Note that both re and im can be complex but must have same type. The real part of re will be the real part of x and the imaginary part of im will be the imaginary part of x.

                                                            val polar : cast_arr -> cast_arr -> arr

                                                            polar rho theta constructs a complex ndarray/matrix from polar coordinates rho and theta. rho contains the magnitudes and theta contains phase angles. Note that the behaviour is undefined if rho has negative elelments or theta has infinity elelments.

                                                            val re : arr -> cast_arr
                                                            val im : arr -> cast_arr
                                                            val sum' : arr -> elt
                                                            diff --git a/docs/owl/Owl_dense_ndarray_intf/module-type-Distribution/index.html b/docs/owl/Owl_dense_ndarray_intf/module-type-Distribution/index.html index db742b078..4970bdd47 100644 --- a/docs/owl/Owl_dense_ndarray_intf/module-type-Distribution/index.html +++ b/docs/owl/Owl_dense_ndarray_intf/module-type-Distribution/index.html @@ -1,2 +1,2 @@ -Distribution (owl.Owl_dense_ndarray_intf.Distribution)

                                                            Module type Owl_dense_ndarray_intf.Distribution

                                                            type arr
                                                            Stats & distribution functions
                                                            val uniform_rvs : a:arr -> b:arr -> n:int -> arr
                                                            val uniform_pdf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_logpdf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_cdf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_logcdf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_ppf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_sf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_logsf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_isf : a:arr -> b:arr -> arr -> arr
                                                            val gaussian_rvs : mu:arr -> sigma:arr -> n:int -> arr
                                                            val gaussian_pdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_logpdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_cdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_logcdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_ppf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_sf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_logsf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_isf : mu:arr -> sigma:arr -> arr -> arr
                                                            val exponential_rvs : lambda:arr -> n:int -> arr
                                                            val exponential_pdf : lambda:arr -> arr -> arr
                                                            val exponential_logpdf : lambda:arr -> arr -> arr
                                                            val exponential_cdf : lambda:arr -> arr -> arr
                                                            val exponential_logcdf : lambda:arr -> arr -> arr
                                                            val exponential_ppf : lambda:arr -> arr -> arr
                                                            val exponential_sf : lambda:arr -> arr -> arr
                                                            val exponential_logsf : lambda:arr -> arr -> arr
                                                            val exponential_isf : lambda:arr -> arr -> arr
                                                            val gamma_rvs : shape:arr -> scale:arr -> n:int -> arr
                                                            val gamma_pdf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_logpdf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_cdf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_logcdf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_ppf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_sf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_logsf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_isf : shape:arr -> scale:arr -> arr -> arr
                                                            val beta_rvs : a:arr -> b:arr -> n:int -> arr
                                                            val beta_pdf : a:arr -> b:arr -> arr -> arr
                                                            val beta_logpdf : a:arr -> b:arr -> arr -> arr
                                                            val beta_cdf : a:arr -> b:arr -> arr -> arr
                                                            val beta_logcdf : a:arr -> b:arr -> arr -> arr
                                                            val beta_ppf : a:arr -> b:arr -> arr -> arr
                                                            val beta_sf : a:arr -> b:arr -> arr -> arr
                                                            val beta_logsf : a:arr -> b:arr -> arr -> arr
                                                            val beta_isf : a:arr -> b:arr -> arr -> arr
                                                            val chi2_rvs : df:arr -> n:int -> arr
                                                            val chi2_pdf : df:arr -> arr -> arr
                                                            val chi2_logpdf : df:arr -> arr -> arr
                                                            val chi2_cdf : df:arr -> arr -> arr
                                                            val chi2_logcdf : df:arr -> arr -> arr
                                                            val chi2_ppf : df:arr -> arr -> arr
                                                            val chi2_sf : df:arr -> arr -> arr
                                                            val chi2_logsf : df:arr -> arr -> arr
                                                            val chi2_isf : df:arr -> arr -> arr
                                                            val f_rvs : dfnum:arr -> dfden:arr -> n:int -> arr
                                                            val f_pdf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_logpdf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_cdf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_logcdf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_ppf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_sf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_logsf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_isf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val cauchy_rvs : loc:arr -> scale:arr -> n:int -> arr
                                                            val cauchy_pdf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_logpdf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_cdf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_logcdf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_ppf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_sf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_logsf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_isf : loc:arr -> scale:arr -> arr -> arr
                                                            val lomax_rvs : shape:arr -> scale:arr -> n:int -> arr
                                                            val lomax_pdf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_logpdf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_cdf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_logcdf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_ppf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_sf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_logsf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_isf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_rvs : shape:arr -> scale:arr -> n:int -> arr
                                                            val weibull_pdf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_logpdf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_cdf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_logcdf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_ppf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_sf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_logsf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_isf : shape:arr -> scale:arr -> arr -> arr
                                                            val laplace_rvs : loc:arr -> scale:arr -> n:int -> arr
                                                            val laplace_pdf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_logpdf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_cdf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_logcdf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_ppf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_sf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_logsf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_isf : loc:arr -> scale:arr -> arr -> arr
                                                            val gumbel1_rvs : a:arr -> b:arr -> n:int -> arr
                                                            val gumbel1_pdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_logpdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_cdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_logcdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_ppf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_sf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_logsf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_isf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_rvs : a:arr -> b:arr -> n:int -> arr
                                                            val gumbel2_pdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_logpdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_cdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_logcdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_ppf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_sf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_logsf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_isf : a:arr -> b:arr -> arr -> arr
                                                            val logistic_rvs : loc:arr -> scale:arr -> n:int -> arr
                                                            val logistic_pdf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_logpdf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_cdf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_logcdf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_ppf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_sf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_logsf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_isf : loc:arr -> scale:arr -> arr -> arr
                                                            val lognormal_rvs : mu:arr -> sigma:arr -> n:int -> arr
                                                            val lognormal_pdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_logpdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_cdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_logcdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_ppf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_sf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_logsf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_isf : mu:arr -> sigma:arr -> arr -> arr
                                                            val rayleigh_rvs : sigma:arr -> n:int -> arr
                                                            val rayleigh_pdf : sigma:arr -> arr -> arr
                                                            val rayleigh_logpdf : sigma:arr -> arr -> arr
                                                            val rayleigh_cdf : sigma:arr -> arr -> arr
                                                            val rayleigh_logcdf : sigma:arr -> arr -> arr
                                                            val rayleigh_ppf : sigma:arr -> arr -> arr
                                                            val rayleigh_sf : sigma:arr -> arr -> arr
                                                            val rayleigh_logsf : sigma:arr -> arr -> arr
                                                            val rayleigh_isf : sigma:arr -> arr -> arr
                                                            +Distribution (owl.Owl_dense_ndarray_intf.Distribution)

                                                            Module type Owl_dense_ndarray_intf.Distribution

                                                            type arr
                                                            Stats & distribution functions
                                                            val uniform_rvs : a:arr -> b:arr -> n:int -> arr
                                                            val uniform_pdf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_logpdf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_cdf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_logcdf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_ppf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_sf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_logsf : a:arr -> b:arr -> arr -> arr
                                                            val uniform_isf : a:arr -> b:arr -> arr -> arr
                                                            val gaussian_rvs : mu:arr -> sigma:arr -> n:int -> arr
                                                            val gaussian_pdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_logpdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_cdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_logcdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_ppf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_sf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_logsf : mu:arr -> sigma:arr -> arr -> arr
                                                            val gaussian_isf : mu:arr -> sigma:arr -> arr -> arr
                                                            val exponential_rvs : lambda:arr -> n:int -> arr
                                                            val exponential_pdf : lambda:arr -> arr -> arr
                                                            val exponential_logpdf : lambda:arr -> arr -> arr
                                                            val exponential_cdf : lambda:arr -> arr -> arr
                                                            val exponential_logcdf : lambda:arr -> arr -> arr
                                                            val exponential_ppf : lambda:arr -> arr -> arr
                                                            val exponential_sf : lambda:arr -> arr -> arr
                                                            val exponential_logsf : lambda:arr -> arr -> arr
                                                            val exponential_isf : lambda:arr -> arr -> arr
                                                            val gamma_rvs : shape:arr -> scale:arr -> n:int -> arr
                                                            val gamma_pdf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_logpdf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_cdf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_logcdf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_ppf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_sf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_logsf : shape:arr -> scale:arr -> arr -> arr
                                                            val gamma_isf : shape:arr -> scale:arr -> arr -> arr
                                                            val beta_rvs : a:arr -> b:arr -> n:int -> arr
                                                            val beta_pdf : a:arr -> b:arr -> arr -> arr
                                                            val beta_logpdf : a:arr -> b:arr -> arr -> arr
                                                            val beta_cdf : a:arr -> b:arr -> arr -> arr
                                                            val beta_logcdf : a:arr -> b:arr -> arr -> arr
                                                            val beta_ppf : a:arr -> b:arr -> arr -> arr
                                                            val beta_sf : a:arr -> b:arr -> arr -> arr
                                                            val beta_logsf : a:arr -> b:arr -> arr -> arr
                                                            val beta_isf : a:arr -> b:arr -> arr -> arr
                                                            val chi2_rvs : df:arr -> n:int -> arr
                                                            val chi2_pdf : df:arr -> arr -> arr
                                                            val chi2_logpdf : df:arr -> arr -> arr
                                                            val chi2_cdf : df:arr -> arr -> arr
                                                            val chi2_logcdf : df:arr -> arr -> arr
                                                            val chi2_ppf : df:arr -> arr -> arr
                                                            val chi2_sf : df:arr -> arr -> arr
                                                            val chi2_logsf : df:arr -> arr -> arr
                                                            val chi2_isf : df:arr -> arr -> arr
                                                            val f_rvs : dfnum:arr -> dfden:arr -> n:int -> arr
                                                            val f_pdf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_logpdf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_cdf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_logcdf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_ppf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_sf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_logsf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val f_isf : dfnum:arr -> dfden:arr -> arr -> arr
                                                            val cauchy_rvs : loc:arr -> scale:arr -> n:int -> arr
                                                            val cauchy_pdf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_logpdf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_cdf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_logcdf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_ppf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_sf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_logsf : loc:arr -> scale:arr -> arr -> arr
                                                            val cauchy_isf : loc:arr -> scale:arr -> arr -> arr
                                                            val lomax_rvs : shape:arr -> scale:arr -> n:int -> arr
                                                            val lomax_pdf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_logpdf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_cdf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_logcdf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_ppf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_sf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_logsf : shape:arr -> scale:arr -> arr -> arr
                                                            val lomax_isf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_rvs : shape:arr -> scale:arr -> n:int -> arr
                                                            val weibull_pdf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_logpdf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_cdf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_logcdf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_ppf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_sf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_logsf : shape:arr -> scale:arr -> arr -> arr
                                                            val weibull_isf : shape:arr -> scale:arr -> arr -> arr
                                                            val laplace_rvs : loc:arr -> scale:arr -> n:int -> arr
                                                            val laplace_pdf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_logpdf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_cdf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_logcdf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_ppf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_sf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_logsf : loc:arr -> scale:arr -> arr -> arr
                                                            val laplace_isf : loc:arr -> scale:arr -> arr -> arr
                                                            val gumbel1_rvs : a:arr -> b:arr -> n:int -> arr
                                                            val gumbel1_pdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_logpdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_cdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_logcdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_ppf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_sf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_logsf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel1_isf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_rvs : a:arr -> b:arr -> n:int -> arr
                                                            val gumbel2_pdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_logpdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_cdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_logcdf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_ppf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_sf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_logsf : a:arr -> b:arr -> arr -> arr
                                                            val gumbel2_isf : a:arr -> b:arr -> arr -> arr
                                                            val logistic_rvs : loc:arr -> scale:arr -> n:int -> arr
                                                            val logistic_pdf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_logpdf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_cdf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_logcdf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_ppf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_sf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_logsf : loc:arr -> scale:arr -> arr -> arr
                                                            val logistic_isf : loc:arr -> scale:arr -> arr -> arr
                                                            val lognormal_rvs : mu:arr -> sigma:arr -> n:int -> arr
                                                            val lognormal_pdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_logpdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_cdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_logcdf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_ppf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_sf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_logsf : mu:arr -> sigma:arr -> arr -> arr
                                                            val lognormal_isf : mu:arr -> sigma:arr -> arr -> arr
                                                            val rayleigh_rvs : sigma:arr -> n:int -> arr
                                                            val rayleigh_pdf : sigma:arr -> arr -> arr
                                                            val rayleigh_logpdf : sigma:arr -> arr -> arr
                                                            val rayleigh_cdf : sigma:arr -> arr -> arr
                                                            val rayleigh_logcdf : sigma:arr -> arr -> arr
                                                            val rayleigh_ppf : sigma:arr -> arr -> arr
                                                            val rayleigh_sf : sigma:arr -> arr -> arr
                                                            val rayleigh_logsf : sigma:arr -> arr -> arr
                                                            val rayleigh_isf : sigma:arr -> arr -> arr
                                                            diff --git a/docs/owl/Owl_dense_ndarray_intf/module-type-NN/index.html b/docs/owl/Owl_dense_ndarray_intf/module-type-NN/index.html index 15a2ad67b..136af7988 100644 --- a/docs/owl/Owl_dense_ndarray_intf/module-type-NN/index.html +++ b/docs/owl/Owl_dense_ndarray_intf/module-type-NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_dense_ndarray_intf.NN)

                                                            Module type Owl_dense_ndarray_intf.NN

                                                            include Owl_base_dense_ndarray_intf.NN
                                                            type arr
                                                            val conv1d : +NN (owl.Owl_dense_ndarray_intf.NN)

                                                            Module type Owl_dense_ndarray_intf.NN

                                                            include Owl_base_dense_ndarray_intf.NN
                                                            type arr
                                                            val conv1d : ?padding:Owl_types_common.padding -> arr -> arr -> diff --git a/docs/owl/Owl_dense_ndarray_intf/module-type-Real/index.html b/docs/owl/Owl_dense_ndarray_intf/module-type-Real/index.html index efcc3a59f..11e551b44 100644 --- a/docs/owl/Owl_dense_ndarray_intf/module-type-Real/index.html +++ b/docs/owl/Owl_dense_ndarray_intf/module-type-Real/index.html @@ -1,2 +1,2 @@ -Real (owl.Owl_dense_ndarray_intf.Real)

                                                            Module type Owl_dense_ndarray_intf.Real

                                                            include Owl_base_dense_ndarray_intf.Real
                                                            type elt
                                                            type arr
                                                            val sum_slices : ?axis:int -> arr -> arr
                                                            val signum : arr -> arr
                                                            val relu : arr -> arr
                                                            val dawsn : arr -> arr
                                                            val l1norm' : arr -> elt
                                                            val l2norm' : arr -> elt
                                                            val l2norm_sqr' : arr -> elt
                                                            val clip_by_value : ?amin:elt -> ?amax:elt -> arr -> arr
                                                            val clip_by_l2norm : elt -> arr -> arr
                                                            val atan2 : arr -> arr -> arr
                                                            val approx_equal : ?eps:float -> arr -> arr -> bool
                                                            val approx_equal_scalar : ?eps:float -> arr -> float -> bool
                                                            val approx_elt_equal : ?eps:float -> arr -> arr -> arr
                                                            val approx_elt_equal_scalar : ?eps:float -> arr -> float -> arr
                                                            val dot : arr -> arr -> arr
                                                            val trace : arr -> elt
                                                            Helper functions
                                                            val float_to_elt : float -> elt
                                                            val elt_to_float : elt -> float
                                                            Real operations
                                                            val i0 : arr -> arr
                                                            val i0e : arr -> arr
                                                            val i1 : arr -> arr
                                                            val i1e : arr -> arr
                                                            val iv : v:arr -> arr -> arr
                                                            val scalar_iv : v:elt -> arr -> arr
                                                            val iv_scalar : v:arr -> elt -> arr
                                                            val j0 : arr -> arr
                                                            val j1 : arr -> arr
                                                            val jv : v:arr -> arr -> arr
                                                            val scalar_jv : v:elt -> arr -> arr
                                                            val jv_scalar : v:arr -> elt -> arr
                                                            val erf : arr -> arr
                                                            val erfc : arr -> arr
                                                            val logistic : arr -> arr
                                                            val elu : ?alpha:elt -> arr -> arr
                                                            val leaky_relu : ?alpha:elt -> arr -> arr
                                                            val softplus : arr -> arr
                                                            val softsign : arr -> arr
                                                            val softmax : ?axis:int -> arr -> arr
                                                            val sigmoid : arr -> arr
                                                            val log_sum_exp' : arr -> float
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> arr -> arr
                                                            val scalar_atan2 : elt -> arr -> arr
                                                            val atan2_scalar : arr -> elt -> arr
                                                            val hypot : arr -> arr -> arr
                                                            val fmod : arr -> arr -> arr
                                                            val fmod_scalar : arr -> elt -> arr
                                                            val scalar_fmod : elt -> arr -> arr
                                                            val cross_entropy' : arr -> arr -> float
                                                            val fused_adagrad_ : ?out:arr -> rate:float -> eps:float -> arr -> unit
                                                            val poisson : mu:elt -> int array -> arr
                                                            val poisson_ : mu:elt -> out:arr -> unit
                                                            +Real (owl.Owl_dense_ndarray_intf.Real)

                                                            Module type Owl_dense_ndarray_intf.Real

                                                            include Owl_base_dense_ndarray_intf.Real
                                                            type elt
                                                            type arr
                                                            val sum_slices : ?axis:int -> arr -> arr
                                                            val signum : arr -> arr
                                                            val relu : arr -> arr
                                                            val dawsn : arr -> arr
                                                            val l1norm' : arr -> elt
                                                            val l2norm' : arr -> elt
                                                            val l2norm_sqr' : arr -> elt
                                                            val clip_by_value : ?amin:elt -> ?amax:elt -> arr -> arr
                                                            val clip_by_l2norm : elt -> arr -> arr
                                                            val atan2 : arr -> arr -> arr
                                                            val approx_equal : ?eps:float -> arr -> arr -> bool
                                                            val approx_equal_scalar : ?eps:float -> arr -> float -> bool
                                                            val approx_elt_equal : ?eps:float -> arr -> arr -> arr
                                                            val approx_elt_equal_scalar : ?eps:float -> arr -> float -> arr
                                                            val dot : arr -> arr -> arr
                                                            val trace : arr -> elt
                                                            Helper functions
                                                            val float_to_elt : float -> elt
                                                            val elt_to_float : elt -> float
                                                            Real operations
                                                            val i0 : arr -> arr
                                                            val i0e : arr -> arr
                                                            val i1 : arr -> arr
                                                            val i1e : arr -> arr
                                                            val iv : v:arr -> arr -> arr
                                                            val scalar_iv : v:elt -> arr -> arr
                                                            val iv_scalar : v:arr -> elt -> arr
                                                            val j0 : arr -> arr
                                                            val j1 : arr -> arr
                                                            val jv : v:arr -> arr -> arr
                                                            val scalar_jv : v:elt -> arr -> arr
                                                            val jv_scalar : v:arr -> elt -> arr
                                                            val erf : arr -> arr
                                                            val erfc : arr -> arr
                                                            val logistic : arr -> arr
                                                            val elu : ?alpha:elt -> arr -> arr
                                                            val leaky_relu : ?alpha:elt -> arr -> arr
                                                            val softplus : arr -> arr
                                                            val softsign : arr -> arr
                                                            val softmax : ?axis:int -> arr -> arr
                                                            val sigmoid : arr -> arr
                                                            val log_sum_exp' : arr -> float
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> arr -> arr
                                                            val scalar_atan2 : elt -> arr -> arr
                                                            val atan2_scalar : arr -> elt -> arr
                                                            val hypot : arr -> arr -> arr
                                                            val fmod : arr -> arr -> arr
                                                            val fmod_scalar : arr -> elt -> arr
                                                            val scalar_fmod : elt -> arr -> arr
                                                            val cross_entropy' : arr -> arr -> float
                                                            val fused_adagrad_ : ?out:arr -> rate:float -> eps:float -> arr -> unit
                                                            val poisson : mu:elt -> int array -> arr
                                                            val poisson_ : mu:elt -> out:arr -> unit
                                                            diff --git a/docs/owl/Owl_dense_ndarray_s/index.html b/docs/owl/Owl_dense_ndarray_s/index.html index 7e0eaed42..afc9479ec 100644 --- a/docs/owl/Owl_dense_ndarray_s/index.html +++ b/docs/owl/Owl_dense_ndarray_s/index.html @@ -1,5 +1,5 @@ -Owl_dense_ndarray_s (owl.Owl_dense_ndarray_s)

                                                            Module Owl_dense_ndarray_s

                                                            type elt = float
                                                            type arr = +Owl_dense_ndarray_s (owl.Owl_dense_ndarray_s)

                                                            Module Owl_dense_ndarray_s

                                                            type elt = float
                                                            type arr = (float, Stdlib.Bigarray.float32_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            include Owl_dense_ndarray_intf.Common with type elt := elt and type arr := arr
                                                            include Owl_base_dense_ndarray_intf.Common with type elt := elt diff --git a/docs/owl/Owl_dense_ndarray_z/index.html b/docs/owl/Owl_dense_ndarray_z/index.html index 00c8608bb..ad780f69d 100644 --- a/docs/owl/Owl_dense_ndarray_z/index.html +++ b/docs/owl/Owl_dense_ndarray_z/index.html @@ -1,5 +1,5 @@ -Owl_dense_ndarray_z (owl.Owl_dense_ndarray_z)

                                                            Module Owl_dense_ndarray_z

                                                            type elt = Stdlib.Complex.t
                                                            type arr = +Owl_dense_ndarray_z (owl.Owl_dense_ndarray_z)

                                                            Module Owl_dense_ndarray_z

                                                            type elt = Stdlib.Complex.t
                                                            type arr = (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t
                                                            type cast_arr = (float, Stdlib.Bigarray.float64_elt, Stdlib.Bigarray.c_layout) diff --git a/docs/owl/Owl_distribution/Make/Beta/index.html b/docs/owl/Owl_distribution/Make/Beta/index.html index 022999f3d..75a663731 100644 --- a/docs/owl/Owl_distribution/Make/Beta/index.html +++ b/docs/owl/Owl_distribution/Make/Beta/index.html @@ -1,2 +1,2 @@ -Beta (owl.Owl_distribution.Make.Beta)

                                                            Module Make.Beta

                                                            type t = {
                                                            1. a : A.arr;
                                                            2. b : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : a:A.arr -> b:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Beta (owl.Owl_distribution.Make.Beta)

                                                            Module Make.Beta

                                                            type t = {
                                                            1. a : A.arr;
                                                            2. b : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : a:A.arr -> b:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Cauchy/index.html b/docs/owl/Owl_distribution/Make/Cauchy/index.html index f6896f3d6..872faf831 100644 --- a/docs/owl/Owl_distribution/Make/Cauchy/index.html +++ b/docs/owl/Owl_distribution/Make/Cauchy/index.html @@ -1,2 +1,2 @@ -Cauchy (owl.Owl_distribution.Make.Cauchy)

                                                            Module Make.Cauchy

                                                            type t = {
                                                            1. loc : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : loc:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Cauchy (owl.Owl_distribution.Make.Cauchy)

                                                            Module Make.Cauchy

                                                            type t = {
                                                            1. loc : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : loc:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Chi2/index.html b/docs/owl/Owl_distribution/Make/Chi2/index.html index 9a3d7b34c..8cb2f6f0e 100644 --- a/docs/owl/Owl_distribution/Make/Chi2/index.html +++ b/docs/owl/Owl_distribution/Make/Chi2/index.html @@ -1,2 +1,2 @@ -Chi2 (owl.Owl_distribution.Make.Chi2)

                                                            Module Make.Chi2

                                                            type t = {
                                                            1. df : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : df:A.arr -> _sigma:'a -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Chi2 (owl.Owl_distribution.Make.Chi2)

                                                            Module Make.Chi2

                                                            type t = {
                                                            1. df : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : df:A.arr -> _sigma:'a -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Exponential/index.html b/docs/owl/Owl_distribution/Make/Exponential/index.html index 086a107f1..fb820ca93 100644 --- a/docs/owl/Owl_distribution/Make/Exponential/index.html +++ b/docs/owl/Owl_distribution/Make/Exponential/index.html @@ -1,2 +1,2 @@ -Exponential (owl.Owl_distribution.Make.Exponential)

                                                            Module Make.Exponential

                                                            type t = {
                                                            1. lambda : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : lambda:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Exponential (owl.Owl_distribution.Make.Exponential)

                                                            Module Make.Exponential

                                                            type t = {
                                                            1. lambda : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : lambda:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/F/index.html b/docs/owl/Owl_distribution/Make/F/index.html index bc52138bf..d49521ec3 100644 --- a/docs/owl/Owl_distribution/Make/F/index.html +++ b/docs/owl/Owl_distribution/Make/F/index.html @@ -1,2 +1,2 @@ -F (owl.Owl_distribution.Make.F)

                                                            Module Make.F

                                                            type t = {
                                                            1. dfnum : A.arr;
                                                            2. dfden : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : dfnum:A.arr -> dfden:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +F (owl.Owl_distribution.Make.F)

                                                            Module Make.F

                                                            type t = {
                                                            1. dfnum : A.arr;
                                                            2. dfden : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : dfnum:A.arr -> dfden:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Gamma/index.html b/docs/owl/Owl_distribution/Make/Gamma/index.html index 0c3f16dd8..938ac0692 100644 --- a/docs/owl/Owl_distribution/Make/Gamma/index.html +++ b/docs/owl/Owl_distribution/Make/Gamma/index.html @@ -1,2 +1,2 @@ -Gamma (owl.Owl_distribution.Make.Gamma)

                                                            Module Make.Gamma

                                                            type t = {
                                                            1. shape : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : shape:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Gamma (owl.Owl_distribution.Make.Gamma)

                                                            Module Make.Gamma

                                                            type t = {
                                                            1. shape : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : shape:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Gaussian/index.html b/docs/owl/Owl_distribution/Make/Gaussian/index.html index abcaacdc3..a3fed762a 100644 --- a/docs/owl/Owl_distribution/Make/Gaussian/index.html +++ b/docs/owl/Owl_distribution/Make/Gaussian/index.html @@ -1,2 +1,2 @@ -Gaussian (owl.Owl_distribution.Make.Gaussian)

                                                            Module Make.Gaussian

                                                            type t = {
                                                            1. mu : A.arr;
                                                            2. sigma : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : mu:A.arr -> sigma:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Gaussian (owl.Owl_distribution.Make.Gaussian)

                                                            Module Make.Gaussian

                                                            type t = {
                                                            1. mu : A.arr;
                                                            2. sigma : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : mu:A.arr -> sigma:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Gumbel1/index.html b/docs/owl/Owl_distribution/Make/Gumbel1/index.html index daf869259..359c48158 100644 --- a/docs/owl/Owl_distribution/Make/Gumbel1/index.html +++ b/docs/owl/Owl_distribution/Make/Gumbel1/index.html @@ -1,2 +1,2 @@ -Gumbel1 (owl.Owl_distribution.Make.Gumbel1)

                                                            Module Make.Gumbel1

                                                            type t = {
                                                            1. a : A.arr;
                                                            2. b : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : a:A.arr -> b:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Gumbel1 (owl.Owl_distribution.Make.Gumbel1)

                                                            Module Make.Gumbel1

                                                            type t = {
                                                            1. a : A.arr;
                                                            2. b : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : a:A.arr -> b:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Gumbel2/index.html b/docs/owl/Owl_distribution/Make/Gumbel2/index.html index 5237cafd6..d309574b6 100644 --- a/docs/owl/Owl_distribution/Make/Gumbel2/index.html +++ b/docs/owl/Owl_distribution/Make/Gumbel2/index.html @@ -1,2 +1,2 @@ -Gumbel2 (owl.Owl_distribution.Make.Gumbel2)

                                                            Module Make.Gumbel2

                                                            type t = {
                                                            1. a : A.arr;
                                                            2. b : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : a:A.arr -> b:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Gumbel2 (owl.Owl_distribution.Make.Gumbel2)

                                                            Module Make.Gumbel2

                                                            type t = {
                                                            1. a : A.arr;
                                                            2. b : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : a:A.arr -> b:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Laplace/index.html b/docs/owl/Owl_distribution/Make/Laplace/index.html index bc6271f55..ac8b91dc9 100644 --- a/docs/owl/Owl_distribution/Make/Laplace/index.html +++ b/docs/owl/Owl_distribution/Make/Laplace/index.html @@ -1,2 +1,2 @@ -Laplace (owl.Owl_distribution.Make.Laplace)

                                                            Module Make.Laplace

                                                            type t = {
                                                            1. loc : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : loc:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Laplace (owl.Owl_distribution.Make.Laplace)

                                                            Module Make.Laplace

                                                            type t = {
                                                            1. loc : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : loc:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Logistic/index.html b/docs/owl/Owl_distribution/Make/Logistic/index.html index 011d28f4c..7e0f2ec04 100644 --- a/docs/owl/Owl_distribution/Make/Logistic/index.html +++ b/docs/owl/Owl_distribution/Make/Logistic/index.html @@ -1,2 +1,2 @@ -Logistic (owl.Owl_distribution.Make.Logistic)

                                                            Module Make.Logistic

                                                            type t = {
                                                            1. loc : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : loc:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Logistic (owl.Owl_distribution.Make.Logistic)

                                                            Module Make.Logistic

                                                            type t = {
                                                            1. loc : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : loc:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Lognormal/index.html b/docs/owl/Owl_distribution/Make/Lognormal/index.html index 12fa6d4cd..d44e85fe8 100644 --- a/docs/owl/Owl_distribution/Make/Lognormal/index.html +++ b/docs/owl/Owl_distribution/Make/Lognormal/index.html @@ -1,2 +1,2 @@ -Lognormal (owl.Owl_distribution.Make.Lognormal)

                                                            Module Make.Lognormal

                                                            type t = {
                                                            1. mu : A.arr;
                                                            2. sigma : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : mu:A.arr -> sigma:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Lognormal (owl.Owl_distribution.Make.Lognormal)

                                                            Module Make.Lognormal

                                                            type t = {
                                                            1. mu : A.arr;
                                                            2. sigma : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : mu:A.arr -> sigma:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Lomax/index.html b/docs/owl/Owl_distribution/Make/Lomax/index.html index 5145e7f5d..7e761fcde 100644 --- a/docs/owl/Owl_distribution/Make/Lomax/index.html +++ b/docs/owl/Owl_distribution/Make/Lomax/index.html @@ -1,2 +1,2 @@ -Lomax (owl.Owl_distribution.Make.Lomax)

                                                            Module Make.Lomax

                                                            type t = {
                                                            1. shape : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : shape:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Lomax (owl.Owl_distribution.Make.Lomax)

                                                            Module Make.Lomax

                                                            type t = {
                                                            1. shape : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : shape:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Poisson/index.html b/docs/owl/Owl_distribution/Make/Poisson/index.html index cdf2b5526..7cf6a8d9b 100644 --- a/docs/owl/Owl_distribution/Make/Poisson/index.html +++ b/docs/owl/Owl_distribution/Make/Poisson/index.html @@ -1,2 +1,2 @@ -Poisson (owl.Owl_distribution.Make.Poisson)

                                                            Module Make.Poisson

                                                            type t = {
                                                            1. mu : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : mu:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            +Poisson (owl.Owl_distribution.Make.Poisson)

                                                            Module Make.Poisson

                                                            type t = {
                                                            1. mu : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : mu:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Rayleigh/index.html b/docs/owl/Owl_distribution/Make/Rayleigh/index.html index fbd0b2afc..d690826d7 100644 --- a/docs/owl/Owl_distribution/Make/Rayleigh/index.html +++ b/docs/owl/Owl_distribution/Make/Rayleigh/index.html @@ -1,2 +1,2 @@ -Rayleigh (owl.Owl_distribution.Make.Rayleigh)

                                                            Module Make.Rayleigh

                                                            type t = {
                                                            1. sigma : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : sigma:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Rayleigh (owl.Owl_distribution.Make.Rayleigh)

                                                            Module Make.Rayleigh

                                                            type t = {
                                                            1. sigma : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : sigma:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Uniform/index.html b/docs/owl/Owl_distribution/Make/Uniform/index.html index 687558f5e..86d91460c 100644 --- a/docs/owl/Owl_distribution/Make/Uniform/index.html +++ b/docs/owl/Owl_distribution/Make/Uniform/index.html @@ -1,2 +1,2 @@ -Uniform (owl.Owl_distribution.Make.Uniform)

                                                            Module Make.Uniform

                                                            type t = {
                                                            1. a : A.arr;
                                                            2. b : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : a:A.arr -> b:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Uniform (owl.Owl_distribution.Make.Uniform)

                                                            Module Make.Uniform

                                                            type t = {
                                                            1. a : A.arr;
                                                            2. b : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : a:A.arr -> b:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/Weibull/index.html b/docs/owl/Owl_distribution/Make/Weibull/index.html index 216ff838c..1567cf47d 100644 --- a/docs/owl/Owl_distribution/Make/Weibull/index.html +++ b/docs/owl/Owl_distribution/Make/Weibull/index.html @@ -1,2 +1,2 @@ -Weibull (owl.Owl_distribution.Make.Weibull)

                                                            Module Make.Weibull

                                                            type t = {
                                                            1. shape : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : shape:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            +Weibull (owl.Owl_distribution.Make.Weibull)

                                                            Module Make.Weibull

                                                            type t = {
                                                            1. shape : A.arr;
                                                            2. scale : A.arr;
                                                            }

                                                            Type definition of a specific distribution

                                                            val make : shape:A.arr -> scale:A.arr -> t

                                                            Make a distribution of the given parameters.

                                                            val sample : t -> int -> A.arr

                                                            Sample a distribution of the given parameters.

                                                            val pdf : t -> A.arr -> A.arr

                                                            Probability density/mass function of the distribution.

                                                            val logpdf : t -> A.arr -> A.arr

                                                            Logarithm of the probability density/mass function of the distribution.

                                                            val cdf : t -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : t -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            val ppf : t -> A.arr -> A.arr

                                                            Percentile function of the distribution.

                                                            val sf : t -> A.arr -> A.arr

                                                            Survival function of the distribution.

                                                            val logsf : t -> A.arr -> A.arr

                                                            Logarithm of the survival function of the distribution.

                                                            val isf : t -> A.arr -> A.arr

                                                            Inverse survival function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/Make/argument-1-A/Linalg/index.html b/docs/owl/Owl_distribution/Make/argument-1-A/Linalg/index.html index 873b65829..c2d02b971 100644 --- a/docs/owl/Owl_distribution/Make/argument-1-A/Linalg/index.html +++ b/docs/owl/Owl_distribution/Make/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_distribution.Make.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_distribution.Make.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_distribution/Make/argument-1-A/Mat/index.html b/docs/owl/Owl_distribution/Make/argument-1-A/Mat/index.html index 86413ac7f..e858fe59a 100644 --- a/docs/owl/Owl_distribution/Make/argument-1-A/Mat/index.html +++ b/docs/owl/Owl_distribution/Make/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_distribution.Make.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_distribution.Make.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_distribution/Make/argument-1-A/Scalar/index.html b/docs/owl/Owl_distribution/Make/argument-1-A/Scalar/index.html index 57b996ec0..e7665be6e 100644 --- a/docs/owl/Owl_distribution/Make/argument-1-A/Scalar/index.html +++ b/docs/owl/Owl_distribution/Make/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_distribution.Make.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_distribution.Make.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_distribution/Make/argument-1-A/index.html b/docs/owl/Owl_distribution/Make/argument-1-A/index.html index 16151e56c..e676f8ccc 100644 --- a/docs/owl/Owl_distribution/Make/argument-1-A/index.html +++ b/docs/owl/Owl_distribution/Make/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_distribution.Make.A)

                                                            Parameter Make.A

                                                            include Owl_types_stats_dist.Sig
                                                            include Owl_types_ndarray_mutable.Sig
                                                            include Owl_types_ndarray_algodiff.Sig
                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_distribution.Make.A)

                                                            Parameter Make.A

                                                            include Owl_types_stats_dist.Sig
                                                            include Owl_types_ndarray_mutable.Sig
                                                            include Owl_types_ndarray_algodiff.Sig
                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_distribution/Make/index.html b/docs/owl/Owl_distribution/Make/index.html index f3aadbe06..216cf157c 100644 --- a/docs/owl/Owl_distribution/Make/index.html +++ b/docs/owl/Owl_distribution/Make/index.html @@ -1,2 +1,2 @@ -Make (owl.Owl_distribution.Make)

                                                            Module Owl_distribution.Make

                                                            Parameters

                                                            Signature

                                                            Uniform distribution
                                                            module Uniform : sig ... end
                                                            Gaussian distribution
                                                            module Gaussian : sig ... end
                                                            Exponential distribution
                                                            module Exponential : sig ... end
                                                            Poisson distribution
                                                            module Poisson : sig ... end
                                                            Gamma distribution
                                                            module Gamma : sig ... end
                                                            Beta distribution
                                                            module Beta : sig ... end
                                                            Chi2 distribution
                                                            module Chi2 : sig ... end
                                                            F distribution
                                                            module F : sig ... end
                                                            Cauchy distribution
                                                            module Cauchy : sig ... end
                                                            Lomax distribution
                                                            module Lomax : sig ... end
                                                            Weibull distribution
                                                            module Weibull : sig ... end
                                                            Laplace distribution
                                                            module Laplace : sig ... end
                                                            Gumbel1 distribution
                                                            module Gumbel1 : sig ... end
                                                            Gumbel2 distribution
                                                            module Gumbel2 : sig ... end
                                                            Logistic distribution
                                                            module Logistic : sig ... end
                                                            Lognormal distribution
                                                            module Lognormal : sig ... end
                                                            Rayleigh distribution
                                                            module Rayleigh : sig ... end
                                                            Type definition
                                                            type dist =
                                                            1. | Uniform of Uniform.t
                                                            2. | Gaussian of Gaussian.t
                                                            3. | Exponential of Exponential.t
                                                            4. | Gamma of Gamma.t
                                                            5. | Beta of Beta.t
                                                            6. | Chi2 of Chi2.t
                                                            7. | F of F.t
                                                            8. | Cauchy of Cauchy.t
                                                            9. | Lomax of Lomax.t
                                                            10. | Weibull of Weibull.t
                                                            11. | Laplace of Laplace.t
                                                            12. | Gumbel1 of Gumbel1.t
                                                            13. | Gumbel2 of Gumbel2.t
                                                            14. | Logistic of Logistic.t
                                                            15. | Lognormal of Lognormal.t
                                                            16. | Rayleigh of Rayleigh.t
                                                              (*

                                                              Type definition of various distributions

                                                              *)
                                                            Core functions
                                                            val sample : dist -> int -> A.arr

                                                            Sample a given distribution of the given parameters.

                                                            val prob : dist -> A.arr -> A.arr

                                                            Probability density/mass function of a given distribution.

                                                            val log_prob : dist -> A.arr -> A.arr

                                                            logarithmic probability density/mass function of a given distribution.

                                                            val cdf : dist -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : dist -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            +Make (owl.Owl_distribution.Make)

                                                            Module Owl_distribution.Make

                                                            Parameters

                                                            Signature

                                                            Uniform distribution
                                                            module Uniform : sig ... end
                                                            Gaussian distribution
                                                            module Gaussian : sig ... end
                                                            Exponential distribution
                                                            module Exponential : sig ... end
                                                            Poisson distribution
                                                            module Poisson : sig ... end
                                                            Gamma distribution
                                                            module Gamma : sig ... end
                                                            Beta distribution
                                                            module Beta : sig ... end
                                                            Chi2 distribution
                                                            module Chi2 : sig ... end
                                                            F distribution
                                                            module F : sig ... end
                                                            Cauchy distribution
                                                            module Cauchy : sig ... end
                                                            Lomax distribution
                                                            module Lomax : sig ... end
                                                            Weibull distribution
                                                            module Weibull : sig ... end
                                                            Laplace distribution
                                                            module Laplace : sig ... end
                                                            Gumbel1 distribution
                                                            module Gumbel1 : sig ... end
                                                            Gumbel2 distribution
                                                            module Gumbel2 : sig ... end
                                                            Logistic distribution
                                                            module Logistic : sig ... end
                                                            Lognormal distribution
                                                            module Lognormal : sig ... end
                                                            Rayleigh distribution
                                                            module Rayleigh : sig ... end
                                                            Type definition
                                                            type dist =
                                                            1. | Uniform of Uniform.t
                                                            2. | Gaussian of Gaussian.t
                                                            3. | Exponential of Exponential.t
                                                            4. | Gamma of Gamma.t
                                                            5. | Beta of Beta.t
                                                            6. | Chi2 of Chi2.t
                                                            7. | F of F.t
                                                            8. | Cauchy of Cauchy.t
                                                            9. | Lomax of Lomax.t
                                                            10. | Weibull of Weibull.t
                                                            11. | Laplace of Laplace.t
                                                            12. | Gumbel1 of Gumbel1.t
                                                            13. | Gumbel2 of Gumbel2.t
                                                            14. | Logistic of Logistic.t
                                                            15. | Lognormal of Lognormal.t
                                                            16. | Rayleigh of Rayleigh.t
                                                              (*

                                                              Type definition of various distributions

                                                              *)
                                                            Core functions
                                                            val sample : dist -> int -> A.arr

                                                            Sample a given distribution of the given parameters.

                                                            val prob : dist -> A.arr -> A.arr

                                                            Probability density/mass function of a given distribution.

                                                            val log_prob : dist -> A.arr -> A.arr

                                                            logarithmic probability density/mass function of a given distribution.

                                                            val cdf : dist -> A.arr -> A.arr

                                                            Cumulative density/mass function of the distribution.

                                                            val logcdf : dist -> A.arr -> A.arr

                                                            Logarithm of the cumulative density/mass function of the distribution.

                                                            diff --git a/docs/owl/Owl_distribution/index.html b/docs/owl/Owl_distribution/index.html index 06f3a62e0..6cf157bb6 100644 --- a/docs/owl/Owl_distribution/index.html +++ b/docs/owl/Owl_distribution/index.html @@ -1,2 +1,2 @@ -Owl_distribution (owl.Owl_distribution)

                                                            Module Owl_distribution

                                                            Functor to generate distribution module

                                                            module Make (A : Owl_types.Stats_Dist) : sig ... end
                                                            +Owl_distribution (owl.Owl_distribution)

                                                            Module Owl_distribution

                                                            Functor to generate distribution module

                                                            module Make (A : Owl_types.Stats_Dist) : sig ... end
                                                            diff --git a/docs/owl/Owl_distribution_common/index.html b/docs/owl/Owl_distribution_common/index.html index 357bbb4b9..2d15e58ae 100644 --- a/docs/owl/Owl_distribution_common/index.html +++ b/docs/owl/Owl_distribution_common/index.html @@ -1,5 +1,5 @@ -Owl_distribution_common (owl.Owl_distribution_common)

                                                            Module Owl_distribution_common

                                                            Interface to the C implementation of rvs, pdf, and cdf functions.

                                                            val owl_float32_uniform_rvs : +Owl_distribution_common (owl.Owl_distribution_common)

                                                            Module Owl_distribution_common

                                                            Interface to the C implementation of rvs, pdf, and cdf functions.

                                                            val owl_float32_uniform_rvs : ('a, 'b) Owl_core_types.owl_arr -> (int64, Stdlib.Bigarray.int64_elt) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> diff --git a/docs/owl/Owl_distribution_generic/index.html b/docs/owl/Owl_distribution_generic/index.html index ec4586692..9ed119e50 100644 --- a/docs/owl/Owl_distribution_generic/index.html +++ b/docs/owl/Owl_distribution_generic/index.html @@ -1,5 +1,5 @@ -Owl_distribution_generic (owl.Owl_distribution_generic)

                                                            Module Owl_distribution_generic

                                                            val broadcast_align_shape : +Owl_distribution_generic (owl.Owl_distribution_generic)

                                                            Module Owl_distribution_generic

                                                            val broadcast_align_shape : ('a, 'b) Owl_dense_ndarray_generic.t -> ('c, 'd) Owl_dense_ndarray_generic.t -> ('e, 'f) Owl_dense_ndarray_generic.t diff --git a/docs/owl/Owl_fft/D/index.html b/docs/owl/Owl_fft/D/index.html index 179a52e5c..82d84c6aa 100644 --- a/docs/owl/Owl_fft/D/index.html +++ b/docs/owl/Owl_fft/D/index.html @@ -1,5 +1,5 @@ -D (owl.Owl_fft.D)

                                                            Module Owl_fft.D

                                                            include module type of struct include Owl_fft_d end
                                                            val fft : +D (owl.Owl_fft.D)

                                                            Module Owl_fft.D

                                                            include module type of struct include Owl_fft_d end
                                                            val fft : ?axis:int -> (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) Owl_dense_ndarray_generic.t -> (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) Owl_dense_ndarray_generic.t
                                                            val ifft : diff --git a/docs/owl/Owl_fft/Generic/index.html b/docs/owl/Owl_fft/Generic/index.html index db096ca80..0548c9b03 100644 --- a/docs/owl/Owl_fft/Generic/index.html +++ b/docs/owl/Owl_fft/Generic/index.html @@ -1,5 +1,5 @@ -Generic (owl.Owl_fft.Generic)

                                                            Module Owl_fft.Generic

                                                            include module type of struct include Owl_fft_generic end
                                                            Basic functions
                                                            val fft : +Generic (owl.Owl_fft.Generic)

                                                            Module Owl_fft.Generic

                                                            include module type of struct include Owl_fft_generic end
                                                            Basic functions
                                                            val fft : ?axis:int -> (Stdlib.Complex.t, 'a) Owl_dense_ndarray_generic.t -> (Stdlib.Complex.t, 'a) Owl_dense_ndarray_generic.t

                                                            fft ~axis x performs 1-dimensional FFT on a complex input. axis is the highest dimension if not specified. The return is not scaled.

                                                            val ifft : diff --git a/docs/owl/Owl_fft/S/index.html b/docs/owl/Owl_fft/S/index.html index 2a219dfd3..75b189e01 100644 --- a/docs/owl/Owl_fft/S/index.html +++ b/docs/owl/Owl_fft/S/index.html @@ -1,5 +1,5 @@ -S (owl.Owl_fft.S)

                                                            Module Owl_fft.S

                                                            include module type of struct include Owl_fft_s end
                                                            val fft : +S (owl.Owl_fft.S)

                                                            Module Owl_fft.S

                                                            include module type of struct include Owl_fft_s end
                                                            val fft : ?axis:int -> (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) Owl_dense_ndarray_generic.t -> (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) Owl_dense_ndarray_generic.t
                                                            val ifft : diff --git a/docs/owl/Owl_fft/index.html b/docs/owl/Owl_fft/index.html index 0a38fdcfa..1dd7772ce 100644 --- a/docs/owl/Owl_fft/index.html +++ b/docs/owl/Owl_fft/index.html @@ -1,2 +1,2 @@ -Owl_fft (owl.Owl_fft)

                                                            Module Owl_fft

                                                            module Generic : sig ... end
                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            +Owl_fft (owl.Owl_fft)

                                                            Module Owl_fft

                                                            module Generic : sig ... end
                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            diff --git a/docs/owl/Owl_fft_d/index.html b/docs/owl/Owl_fft_d/index.html index 799fbc213..495806c4f 100644 --- a/docs/owl/Owl_fft_d/index.html +++ b/docs/owl/Owl_fft_d/index.html @@ -1,5 +1,5 @@ -Owl_fft_d (owl.Owl_fft_d)

                                                            Module Owl_fft_d

                                                            val fft : +Owl_fft_d (owl.Owl_fft_d)

                                                            Module Owl_fft_d

                                                            val fft : ?axis:int -> (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) Owl_dense_ndarray_generic.t -> (Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt) Owl_dense_ndarray_generic.t
                                                            val ifft : diff --git a/docs/owl/Owl_fft_generic/index.html b/docs/owl/Owl_fft_generic/index.html index 4ad995950..4f09716b4 100644 --- a/docs/owl/Owl_fft_generic/index.html +++ b/docs/owl/Owl_fft_generic/index.html @@ -1,5 +1,5 @@ -Owl_fft_generic (owl.Owl_fft_generic)

                                                            Module Owl_fft_generic

                                                            Fast Fourier Transform

                                                            Basic functions
                                                            val fft : +Owl_fft_generic (owl.Owl_fft_generic)

                                                            Module Owl_fft_generic

                                                            Fast Fourier Transform

                                                            Basic functions
                                                            val fft : ?axis:int -> (Stdlib.Complex.t, 'a) Owl_dense_ndarray_generic.t -> (Stdlib.Complex.t, 'a) Owl_dense_ndarray_generic.t

                                                            fft ~axis x performs 1-dimensional FFT on a complex input. axis is the highest dimension if not specified. The return is not scaled.

                                                            val ifft : diff --git a/docs/owl/Owl_fft_s/index.html b/docs/owl/Owl_fft_s/index.html index 1b49e973d..0fc88e693 100644 --- a/docs/owl/Owl_fft_s/index.html +++ b/docs/owl/Owl_fft_s/index.html @@ -1,5 +1,5 @@ -Owl_fft_s (owl.Owl_fft_s)

                                                            Module Owl_fft_s

                                                            val fft : +Owl_fft_s (owl.Owl_fft_s)

                                                            Module Owl_fft_s

                                                            val fft : ?axis:int -> (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) Owl_dense_ndarray_generic.t -> (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) Owl_dense_ndarray_generic.t
                                                            val ifft : diff --git a/docs/owl/Owl_fftpack/index.html b/docs/owl/Owl_fftpack/index.html index ffe61482e..b66d879b8 100644 --- a/docs/owl/Owl_fftpack/index.html +++ b/docs/owl/Owl_fftpack/index.html @@ -1,5 +1,5 @@ -Owl_fftpack (owl.Owl_fftpack)

                                                            Module Owl_fftpack

                                                            val owl_float32_rfftf : +Owl_fftpack (owl.Owl_fftpack)

                                                            Module Owl_fftpack

                                                            val owl_float32_rfftf : (float, Stdlib.Bigarray.float32_elt) Owl_core_types.owl_arr -> (Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt) Owl_core_types.owl_arr -> int -> diff --git a/docs/owl/Owl_lapacke/index.html b/docs/owl/Owl_lapacke/index.html index 9cb2e07eb..699ff90b1 100644 --- a/docs/owl/Owl_lapacke/index.html +++ b/docs/owl/Owl_lapacke/index.html @@ -1,5 +1,5 @@ -Owl_lapacke (owl.Owl_lapacke)

                                                            Module Owl_lapacke

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            Default data type

                                                            type lapacke_layout =
                                                            1. | RowMajor
                                                            2. | ColMajor
                                                              (*

                                                              Layout type.

                                                              *)
                                                            type lapacke_transpose =
                                                            1. | NoTrans
                                                            2. | Trans
                                                            3. | ConjTrans
                                                              (*

                                                              Transpose type.

                                                              *)
                                                            type lapacke_uplo =
                                                            1. | Upper
                                                            2. | Lower
                                                              (*

                                                              Upper or lower trangular.

                                                              *)
                                                            type lapacke_diag =
                                                            1. | NonUnit
                                                            2. | Unit
                                                              (*

                                                              Diangonal type.

                                                              *)
                                                            type lapacke_side =
                                                            1. | Left
                                                            2. | Right
                                                              (*

                                                              Side type.

                                                              *)
                                                            Basic functions
                                                            val gbtrs : +Owl_lapacke (owl.Owl_lapacke)

                                                            Module Owl_lapacke

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout) Stdlib.Bigarray.Genarray.t

                                                            Default data type

                                                            type lapacke_layout =
                                                            1. | RowMajor
                                                            2. | ColMajor
                                                              (*

                                                              Layout type.

                                                              *)
                                                            type lapacke_transpose =
                                                            1. | NoTrans
                                                            2. | Trans
                                                            3. | ConjTrans
                                                              (*

                                                              Transpose type.

                                                              *)
                                                            type lapacke_uplo =
                                                            1. | Upper
                                                            2. | Lower
                                                              (*

                                                              Upper or lower trangular.

                                                              *)
                                                            type lapacke_diag =
                                                            1. | NonUnit
                                                            2. | Unit
                                                              (*

                                                              Diangonal type.

                                                              *)
                                                            type lapacke_side =
                                                            1. | Left
                                                            2. | Right
                                                              (*

                                                              Side type.

                                                              *)
                                                            Basic functions
                                                            val gbtrs : trans:lapacke_transpose -> kl:int -> ku:int -> diff --git a/docs/owl/Owl_lapacke_generated/index.html b/docs/owl/Owl_lapacke_generated/index.html index d53312170..a88479de8 100644 --- a/docs/owl/Owl_lapacke_generated/index.html +++ b/docs/owl/Owl_lapacke_generated/index.html @@ -1,5 +1,5 @@ -Owl_lapacke_generated (owl.Owl_lapacke_generated)

                                                            Module Owl_lapacke_generated

                                                            LAPACKE interface: low-level interface to the LAPACKE functions

                                                            auto-generated lapacke interface file, timestamp:1582875920

                                                            val sbdsdc : +Owl_lapacke_generated (owl.Owl_lapacke_generated)

                                                            Module Owl_lapacke_generated

                                                            LAPACKE interface: low-level interface to the LAPACKE functions

                                                            auto-generated lapacke interface file, timestamp:1582875920

                                                            val sbdsdc : layout:int -> uplo:char -> compq:char -> diff --git a/docs/owl/Owl_linalg/C/index.html b/docs/owl/Owl_linalg/C/index.html index 3e329c439..a44fbc3a8 100644 --- a/docs/owl/Owl_linalg/C/index.html +++ b/docs/owl/Owl_linalg/C/index.html @@ -1,5 +1,5 @@ -C (owl.Owl_linalg.C)

                                                            Module Owl_linalg.C

                                                            include module type of struct include Owl_linalg_c end
                                                            type elt = Stdlib.Complex.t
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +C (owl.Owl_linalg.C)

                                                            Module Owl_linalg.C

                                                            include module type of struct include Owl_linalg_c end
                                                            type elt = Stdlib.Complex.t
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat = mat diff --git a/docs/owl/Owl_linalg/D/index.html b/docs/owl/Owl_linalg/D/index.html index 88d4f27ca..fd8011198 100644 --- a/docs/owl/Owl_linalg/D/index.html +++ b/docs/owl/Owl_linalg/D/index.html @@ -1,5 +1,5 @@ -D (owl.Owl_linalg.D)

                                                            Module Owl_linalg.D

                                                            include module type of struct include Owl_linalg_d end
                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_z.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +D (owl.Owl_linalg.D)

                                                            Module Owl_linalg.D

                                                            include module type of struct include Owl_linalg_d end
                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_z.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat := complex_mat diff --git a/docs/owl/Owl_linalg/Generic/index.html b/docs/owl/Owl_linalg/Generic/index.html index 30fea684d..82a501d2a 100644 --- a/docs/owl/Owl_linalg/Generic/index.html +++ b/docs/owl/Owl_linalg/Generic/index.html @@ -1,5 +1,23 @@ -Generic (owl.Owl_linalg.Generic)

                                                            Module Owl_linalg.Generic

                                                            include module type of struct include Owl_linalg_generic end

                                                            The module includes a set of advanced linear algebra operations such as singular value decomposition, and etc.

                                                            Currently, Linalg module supports dense matrix of four different number types, including float32, float64, complex32, and complex64. The support for sparse matrices will be provided in future.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b) Owl_dense_matrix_generic.t

                                                            Matrix type, a special case of N-dimensional array.

                                                            Basic functions
                                                            val inv : ('a, 'b) t -> ('a, 'b) t

                                                            inv x calculates the inverse of an invertible square matrix x such that x *@ x = I wherein I is an identity matrix. (If x is singular, inv will return a useless result.)

                                                            val pinv : ?tol:float -> ('a, 'b) t -> ('a, 'b) t

                                                            pinv x computes Moore-Penrose pseudoinverse of matrix x. tol specifies the tolerance, the absolute value of the elements smaller than tol will be set to zeros.

                                                            val det : ('a, 'b) t -> 'a

                                                            det x computes the determinant of a square matrix x.

                                                            val logdet : ('a, 'b) t -> 'a

                                                            logdet x computes the log of the determinant of a square matrix x. It is equivalent to log (det x) but may provide more accuracy and efficiency.

                                                            val rank : ?tol:float -> ('a, 'b) t -> int

                                                            rank x calculates the rank of a rectangular matrix x of shape m x n. The function does so by counting the number of singular values of x which are beyond a pre-defined threshold tol. By default, tol = max(m,n) * eps where eps = 1e-10.

                                                            val norm : ?p:float -> ('a, 'b) t -> float

                                                            norm ~p x computes the matrix p-norm of the passed in matrix x.

                                                            Parameters: * p is the order of norm, the default value is 2. * x is the input matrix.

                                                            Returns: * If p = 1, then returns the maximum absolute column sum of the matrix. * If p = 2, then returns approximately max (svd x). * If p = infinity, then returns the maximum absolute row sum of the matrix. * If p = -1, then returns the minimum absolute column sum of the matrix. * If p = -2, then returns approximately min (svd x). * If p = -infinity, then returns the minimum absolute row sum of the matrix.

                                                            val vecnorm : ?p:float -> ('a, 'b) t -> float

                                                            vecnorm ~p x calculates the generalised vector p-norm, defined as below. If x is a martrix, it will be flatten to a vector first. Different from the function of the same name in :doc:`owl_dense_ndarray_generic`, this function assumes the input is either 1d vector or 2d matrix.

                                                            .. math:: ||v||_p = \Big \sum_{k=0}^{N-1} |v_k|^p \Big^

                                                            /p

                                                            Parameters: * p is the order of norm, the default value is 2. * x is the input vector or matrix.

                                                            Returns: * If p = infinity, then returns :math:`||v||_\infty = \max_i(|v(i)|)`. * If p = -infinity, then returns :math:`||v||_

                                                            \infty

                                                            }

                                                            = \min_i(|v(i)|)`. * If p = 2 and x is a matrix, then returns Frobenius norm of x. * Otherwise returns generalised vector p-norm defined above.

                                                            val cond : ?p:float -> ('a, 'b) t -> float

                                                            cond ~p x computes the p-norm condition number of matrix x.

                                                            cond ~p:1. x returns the 1-norm condition number;

                                                            cond ~p:2. x or cond x returns the 2-norm condition number.

                                                            cond ~p:infinity x returns the infinity norm condition number.

                                                            The default value of p is 2.

                                                            val rcond : ('a, 'b) t -> float

                                                            rcond x returns an estimate for the reciprocal condition of x in 1-norm. If x is well conditioned, the returned result is near 1.0. If x is badly conditioned, the result is near 0.

                                                            Check matrix types
                                                            val is_square : ('a, 'b) t -> bool

                                                            is_square x returns true if x is a square matrix otherwise false.

                                                            val is_triu : ('a, 'b) t -> bool

                                                            is_triu x returns true if x is upper triangular otherwise false.

                                                            val is_tril : ('a, 'b) t -> bool

                                                            is_tril x returns true if x is lower triangular otherwise false.

                                                            val is_symmetric : ('a, 'b) t -> bool

                                                            is_symmetric x returns true if x is symmetric otherwise false.

                                                            val is_hermitian : (Stdlib.Complex.t, 'a) t -> bool

                                                            is_hermitian x returns true if x is hermitian otherwise false.

                                                            val is_diag : ('a, 'b) t -> bool

                                                            is_diag x returns true if x is diagonal otherwise false.

                                                            val is_posdef : ('a, 'b) t -> bool

                                                            is_posdef x checks whether x is a positive semi-definite matrix.

                                                            Factorisation
                                                            val lu : +Generic (owl.Owl_linalg.Generic)

                                                            Module Owl_linalg.Generic

                                                            include module type of struct include Owl_linalg_generic end

                                                            The module includes a set of advanced linear algebra operations such as singular value decomposition, and etc.

                                                            Currently, Linalg module supports dense matrix of four different number types, including float32, float64, complex32, and complex64. The support for sparse matrices will be provided in future.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b) Owl_dense_matrix_generic.t

                                                            Matrix type, a special case of N-dimensional array.

                                                            Basic functions
                                                            val inv : ('a, 'b) t -> ('a, 'b) t

                                                            inv x calculates the inverse of an invertible square matrix x such that x *@ x = I wherein I is an identity matrix. (If x is singular, inv will return a useless result.)

                                                            val pinv : ?tol:float -> ('a, 'b) t -> ('a, 'b) t

                                                            pinv x computes Moore-Penrose pseudoinverse of matrix x. tol specifies the tolerance, the absolute value of the elements smaller than tol will be set to zeros.

                                                            val det : ('a, 'b) t -> 'a

                                                            det x computes the determinant of a square matrix x.

                                                            val logdet : ('a, 'b) t -> 'a

                                                            logdet x computes the log of the determinant of a square matrix x. It is equivalent to log (det x) but may provide more accuracy and efficiency.

                                                            val rank : ?tol:float -> ('a, 'b) t -> int

                                                            rank x calculates the rank of a rectangular matrix x of shape m x n. The function does so by counting the number of singular values of x which are beyond a pre-defined threshold tol. By default, tol = max(m,n) * eps where eps = 1e-10.

                                                            val norm : ?p:float -> ('a, 'b) t -> float

                                                            norm ~p x computes the matrix p-norm of the passed in matrix x.

                                                            Parameters: * p is the order of norm, the default value is 2. * x is the input matrix.

                                                            Returns: * If p = 1, then returns the maximum absolute column sum of the matrix. * If p = 2, then returns approximately max (svd x). * If p = infinity, then returns the maximum absolute row sum of the matrix. * If p = -1, then returns the minimum absolute column sum of the matrix. * If p = -2, then returns approximately min (svd x). * If p = -infinity, then returns the minimum absolute row sum of the matrix.

                                                            val vecnorm : ?p:float -> ('a, 'b) t -> float

                                                            vecnorm ~p x calculates the generalised vector p-norm, defined as below. If x is a martrix, it will be flatten to a vector first. Different from the function of the same name in :doc:`owl_dense_ndarray_generic`, this function assumes the input is either 1d vector or 2d matrix.

                                                            +  ||v||_p = \Big[ \sum_{k=0}^{N-1} |v_k|^p \Big]^{1/p}

                                                            Parameters: * p is the order of norm, the default value is 2. * x is the input vector or matrix.

                                                            Returns: * If p = infinity, then returns ||v||_{\infty} = \max_i(|v(i)|) * If p = -infinity, then returns ||v||_{-\infty} = \min_i(|v(i)|). * If p = 2 and x is a matrix, then returns Frobenius norm of x. * Otherwise returns generalised vector p-norm defined above.

                                                            val cond : ?p:float -> ('a, 'b) t -> float

                                                            cond ~p x computes the p-norm condition number of matrix x.

                                                            cond ~p:1. x returns the 1-norm condition number;

                                                            cond ~p:2. x or cond x returns the 2-norm condition number.

                                                            cond ~p:infinity x returns the infinity norm condition number.

                                                            The default value of p is 2.

                                                            val rcond : ('a, 'b) t -> float

                                                            rcond x returns an estimate for the reciprocal condition of x in 1-norm. If x is well conditioned, the returned result is near 1.0. If x is badly conditioned, the result is near 0.

                                                            Check matrix types
                                                            val is_square : ('a, 'b) t -> bool

                                                            is_square x returns true if x is a square matrix otherwise false.

                                                            val is_triu : ('a, 'b) t -> bool

                                                            is_triu x returns true if x is upper triangular otherwise false.

                                                            val is_tril : ('a, 'b) t -> bool

                                                            is_tril x returns true if x is lower triangular otherwise false.

                                                            val is_symmetric : ('a, 'b) t -> bool

                                                            is_symmetric x returns true if x is symmetric otherwise false.

                                                            val is_hermitian : (Stdlib.Complex.t, 'a) t -> bool

                                                            is_hermitian x returns true if x is hermitian otherwise false.

                                                            val is_diag : ('a, 'b) t -> bool

                                                            is_diag x returns true if x is diagonal otherwise false.

                                                            val is_posdef : ('a, 'b) t -> bool

                                                            is_posdef x checks whether x is a positive semi-definite matrix.

                                                            Factorisation
                                                            val lu : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            lu x -> (l, u, ipiv) calculates LU decomposition of x. The pivoting is used by default.

                                                            val lq : ?thin:bool -> ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            lq x -> (l, q) calculates the LQ decomposition of x. By default, the reduced LQ decomposition is performed. But you can get full Q by setting parameter thin = false.

                                                            val qr : ?thin:bool -> @@ -11,16 +29,16 @@ ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t

                                                            gsvd x y -> (u, v, q, d1, d2, r) computes the generalised singular value decomposition of a pair of general rectangular matrices x and y. d1 and d2 contain the generalised singular value pairs of x and y. The shape of x is m x n and the shape of y is p x n.

                                                            .. code-block:: ocaml

                                                            let x = Mat.uniform 5 5;; let y = Mat.uniform 2 5;; let u, v, q, d1, d2, r = Linalg.gsvd x y;; Mat.(u *@ d1 *@ r *@ transpose q =~ x);; Mat.(v *@ d2 *@ r *@ transpose q =~ y);;

                                                            Please refer to: `Intel MKL Reference <https://software.intel.com/en-us/mkl-developer-reference-c-ggsvd3>`_

                                                            val gsvdvals : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            gsvdvals x y is similar to gsvd x y but only returns the singular values of the generalised singular value decomposition of x and y.

                                                            val schur : otyp:('c, 'd) Stdlib.Bigarray.kind -> ('a, 'b) t -> - ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            schur x -> (t, z, w) calculates Schur factorisation of x in the following form.

                                                            .. math:: X = Z T Z^H

                                                            Parameters: * otyp: the complex type of eigen values. * x: the n x n square matrix.

                                                            Returns: * t is (quasi) triangular Schur factor. * z is orthogonal/unitary Schur vectors. The eigen values are not sorted, they have the same order as that they appear on the diagonal of the output of Schur form t. * w contains the eigen values of x. otyp is used to specify the type of w. It needs to be consistent with input type. E.g., if the input x is float32 then otyp must be complex32. However, if you use S, D, C, Z module, then you do not need to worry about otyp.

                                                            val schur_tz : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            schur_tz x is similar to schur but only returns (t, z).

                                                            val ordschur : + ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            schur x -> (t, z, w) calculates Schur factorisation of x in the following form: X = Z T Z^H.

                                                            Parameters: * otyp: the complex type of eigen values. * x: the n x n square matrix.

                                                            Returns: * t is (quasi) triangular Schur factor. * z is orthogonal/unitary Schur vectors. The eigen values are not sorted, they have the same order as that they appear on the diagonal of the output of Schur form t. * w contains the eigen values of x. otyp is used to specify the type of w. It needs to be consistent with input type. E.g., if the input x is float32 then otyp must be complex32. However, if you use S, D, C, Z module, then you do not need to worry about otyp.

                                                            val schur_tz : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            schur_tz x is similar to schur but only returns (t, z).

                                                            val ordschur : otyp:('c, 'd) Stdlib.Bigarray.kind -> select:(int32, Stdlib.Bigarray.int32_elt) t -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            ordschur ~select t z -> (r, p) reorders t and z returned by Schur factorization schur x -> (t, z) according select such that

                                                            .. math:: X = P R P^H

                                                            Parameters: * otyp: the complex type of eigen values * select the logical vector to select eigenvalues, refer to select_ev. * t: the Schur matrix returned by schur x. * z: the unitary matrix z returned by schur x.

                                                            Returns: * r: reordered Schur matrix t. * p: reordered orthogonal matrix z.

                                                            val qz : + ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            ordschur ~select t z -> (r, p) reorders t and z returned by Schur factorization schur x -> (t, z) according select such that X = P R P^H.

                                                            Parameters: * otyp: the complex type of eigen values * select the logical vector to select eigenvalues, refer to select_ev. * t: the Schur matrix returned by schur x. * z: the unitary matrix z returned by schur x.

                                                            Returns: * r: reordered Schur matrix t. * p: reordered orthogonal matrix z.

                                                            val qz : otyp:('c, 'd) Stdlib.Bigarray.kind -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            qz x -> (s, t, q, z, w) calculates generalised Schur factorisation of x in the following form. It is also known as QZ decomposition.

                                                            .. math:: X = Q S Z^H Y = Z T Z^H

                                                            Parameters: * otyp: the complex type of eigen values. * x: the n x n square matrix. * y: the n x n square matrix.

                                                            Returns: * s: the upper quasitriangular matrices S. * t: the upper quasitriangular matrices T. * q: the unitary matrices Q. * z: the unitary matrices Z. * w: the generalised eigenvalue for a pair of matrices (X,Y).

                                                            val ordqz : + ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            qz x -> (s, t, q, z, w) calculates generalised Schur factorisation of x in the following form. It is also known as QZ decomposition.

                                                            X = Q S Z^H Y = Z T Z^H

                                                            Parameters: * otyp: the complex type of eigen values. * x: the n x n square matrix. * y: the n x n square matrix.

                                                            Returns: * s: the upper quasitriangular matrices S. * t: the upper quasitriangular matrices T. * q: the unitary matrices Q. * z: the unitary matrices Z. * w: the generalised eigenvalue for a pair of matrices (X,Y).

                                                            val ordqz : otyp:('c, 'd) Stdlib.Bigarray.kind -> select:(int32, Stdlib.Bigarray.int32_elt) t -> ('a, 'b) t -> @@ -31,7 +49,7 @@ otyp:('c, 'd) Stdlib.Bigarray.kind -> ('a, 'b) t -> ('a, 'b) t -> - ('c, 'd) t

                                                            qzvals ~otyp x y is similar to qz ~otyp x y but only returns the generalised eigen values.

                                                            val hess : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            hess x -> (h, q) calculates the Hessenberg form of a given matrix x. Both Hessenberg matrix h and unitary matrix q is returned, such that x = q *@ h *@ (transpose q).

                                                            .. math:: X = Q H Q^T

                                                            Eigenvalues & eigenvectors
                                                            val eig : + ('c, 'd) t

                                                            qzvals ~otyp x y is similar to qz ~otyp x y but only returns the generalised eigen values.

                                                            val hess : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            hess x -> (h, q) calculates the Hessenberg form of a given matrix x. Both Hessenberg matrix h and unitary matrix q is returned, such that x = q *@ h *@ (transpose q).

                                                            X = Q H Q^T

                                                            Eigenvalues & eigenvectors
                                                            val eig : ?permute:bool -> ?scale:bool -> otyp:('a, 'b) Stdlib.Bigarray.kind -> @@ -41,33 +59,35 @@ ?scale:bool -> otyp:('a, 'b) Stdlib.Bigarray.kind -> ('c, 'd) t -> - ('a, 'b) t

                                                            eigvals x -> w is similar to eig but only computes the eigenvalues of an arbitrary square matrix x.

                                                            Linear system of equations
                                                            val null : ('a, 'b) t -> ('a, 'b) t

                                                            null a -> x computes an orthonormal basis x for the null space of a obtained from the singular value decomposition. Namely, a *@ x has negligible elements, M.col_num x is the nullity of a, and transpose x *@ x = I. Namely,

                                                            .. math:: X^T X = I

                                                            val triangular_solve : + ('a, 'b) t

                                                            eigvals x -> w is similar to eig but only computes the eigenvalues of an arbitrary square matrix x.

                                                            Linear system of equations
                                                            val null : ('a, 'b) t -> ('a, 'b) t

                                                            null a -> x computes an orthonormal basis x for the null space of a obtained from the singular value decomposition. Namely, a *@ x has negligible elements, M.col_num x is the nullity of a, and transpose x *@ x = I. Namely,

                                                            X^T X = I

                                                            val triangular_solve : upper:bool -> ?trans:bool -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t

                                                            triangular_linsolve a b -> x solves a linear system of equations a * x = b where a is either a upper or a lower triangular matrix. This function uses cblas trsm under the hood.

                                                            .. math:: AX = B

                                                            By default, trans = false indicates no transpose. If trans = true, then function will solve A^T * x = b for real matrices; A^H * x = b for complex matrices.

                                                            .. math:: A^H X = B

                                                            val linsolve : + ('a, 'b) t

                                                            triangular_linsolve a b -> x solves a linear system of equations a * x = b where a is either a upper or a lower triangular matrix. This function uses cblas trsm under the hood.

                                                            AX = B

                                                            By default, trans = false indicates no transpose. If trans = true, then function will solve A^T * x = b for real matrices; A^H * x = b for complex matrices.

                                                            A^H X = B

                                                            val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t

                                                            linsolve a b -> x solves a linear system of equations a * x = b in the following form. By default, typ=`n and the function use LU factorisation with partial pivoting when a is square and QR factorisation with column pivoting otherwise. The number of rows of a must equal the number of rows of b. If a is a upper(lower) triangular matrix, the function calls the solve_triangular function when typ=`u(typ=`l).

                                                            .. math:: AX = B

                                                            By default, trans = false indicates no transpose. If trans = true, then function will solve A^T * x = b for real matrices; A^H * x = b for complex matrices.

                                                            .. math:: A^H X = B

                                                            The associated operator is /@, so you can simply use a /@ b to solve the linear equation system to get x. Please refer to :doc:`owl_operator`.

                                                            val linreg : ('a, 'b) t -> ('a, 'b) t -> 'a * 'a

                                                            linreg x y -> (a, b) solves y = a + b*x using Ordinary Least Squares.

                                                            .. math:: Y = A + BX

                                                            val sylvester : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            sylvester a b c solves a Sylvester equation in the following form. The function calls LAPACKE function trsyl solve the system.

                                                            .. math:: AX + XB = C

                                                            Parameters: * a : m x m matrix A. * b : n x n matrix B. * c : m x n matrix C.

                                                            Returns: * x : m x n matrix X.

                                                            val lyapunov : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            lyapunov a q solves a continuous Lyapunov equation in the following form. The function calls LAPACKE function trsyl solve the system. In Matlab, the same function is called lyap.

                                                            .. math:: AX + XA^H = Q

                                                            Parameters: * a : m x m matrix A. * q : n x n matrix Q.

                                                            Returns: * x : m x n matrix X.

                                                            val discrete_lyapunov : + ('a, 'b) t

                                                            linsolve a b -> x solves a linear system of equations a * x = b in the following form. By default, typ=`n and the function use LU factorisation with partial pivoting when a is square and QR factorisation with column pivoting otherwise. The number of rows of a must equal the number of rows of b. If a is a upper(lower) triangular matrix, the function calls the solve_triangular function when typ=`u(typ=`l).

                                                            AX = B

                                                            By default, trans = false indicates no transpose. If trans = true, then function will solve A^T * x = b for real matrices; A^H * x = b for complex matrices.

                                                            A^H X = B

                                                            The associated operator is /@, so you can simply use a /@ b to solve the linear equation system to get x. Please refer to :doc:`owl_operator`.

                                                            val linreg : ('a, 'b) t -> ('a, 'b) t -> 'a * 'a

                                                            linreg x y -> (a, b) solves y = a + b*x using Ordinary Least Squares.

                                                            Y = A + BX

                                                            val sylvester : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            sylvester a b c solves a Sylvester equation in the following form. The function calls LAPACKE function trsyl solve the system.

                                                            AX + XB = C

                                                            Parameters: * a : m x m matrix A. * b : n x n matrix B. * c : m x n matrix C.

                                                            Returns: * x : m x n matrix X.

                                                            val lyapunov : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            lyapunov a q solves a continuous Lyapunov equation in the following form. The function calls LAPACKE function trsyl solve the system. In Matlab, the same function is called lyap.

                                                            AX + XA^H = Q

                                                            Parameters: * a : m x m matrix A. * q : n x n matrix Q.

                                                            Returns: * x : m x n matrix X.

                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t

                                                            discrete_lyapunov a q solves a discrete-time Lyapunov equation in the following form.

                                                            .. math:: X - AXA^H = Q

                                                            Parameters: * a : m x m matrix A. * q : n x n matrix Q.

                                                            Returns: * x : m x n matrix X.

                                                            val care : + ('a, 'b) t

                                                            discrete_lyapunov a q solves a discrete-time Lyapunov equation in the following form.

                                                            X - AXA^H = Q

                                                            Parameters: * a : m x m matrix A. * q : n x n matrix Q.

                                                            Returns: * x : m x n matrix X.

                                                            val care : ?diag_r:bool -> (float, 'a) t -> (float, 'a) t -> (float, 'a) t -> (float, 'a) t -> - (float, 'a) t

                                                            care ?diag_r a b q r solves the continuous-time algebraic Riccati equation system in the following form. The algorithm is based on :cite:`laub1979schur`.

                                                            .. math:: A^T X + X A − X B R^

                                                            1

                                                            }

                                                            B^T X + Q = 0

                                                            Parameters: * a : real cofficient matrix A. * b : real cofficient matrix B. * q : real cofficient matrix Q. * r : real cofficient matrix R. R must be non-singular. * diag_r : true if R is a diagonal matrix, false by default.

                                                            Returns: * x : a solution matrix X.

                                                            val dare : + (float, 'a) t

                                                            care ?diag_r a b q r solves the continuous-time algebraic Riccati equation system in the following form. The algorithm is based on :cite:`laub1979schur`.

                                                            +  A^T X + X A − X B R^{-1} B^T X + Q = 0

                                                            Parameters: * a : real cofficient matrix A. * b : real cofficient matrix B. * q : real cofficient matrix Q. * r : real cofficient matrix R. R must be non-singular. * diag_r : true if R is a diagonal matrix, false by default.

                                                            Returns: * x : a solution matrix X.

                                                            val dare : ?diag_r:bool -> (float, 'a) t -> (float, 'a) t -> (float, 'a) t -> (float, 'a) t -> - (float, 'a) t

                                                            dare ?diag_r a b q r solves the discrete-time algebraic Riccati equation system in the following form. The algorithm is based on :cite:`laub1979schur`.

                                                            .. math:: A^T X A - X - (A^T X B) (B^T X B + R)^

                                                            1

                                                            }

                                                            (B^T X A) + Q = 0

                                                            Parameters: * a : real cofficient matrix A. A must be non-singular. * b : real cofficient matrix B. * q : real cofficient matrix Q. * r : real cofficient matrix R. R must be non-singular. * diag_r : true if R is a diagonal matrix, false by default.

                                                            Returns: * x : a symmetric solution matrix X.

                                                            Low-level factorisation functions
                                                            val lufact : ('a, 'b) t -> ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            lufact x -> (a, ipiv) calculates LU factorisation with pivot of a general matrix x.

                                                            val qrfact : + (float, 'a) t

                                                            dare ?diag_r a b q r solves the discrete-time algebraic Riccati equation system in the following form.

                                                            +  A^T X A - X - (A^T X B) (B^T X B + R)^{-1} (B^T X A) + Q = 0

                                                            Parameters: * a : real cofficient matrix A. A must be non-singular. * b : real cofficient matrix B. * q : real cofficient matrix Q. * r : real cofficient matrix R. R must be non-singular. * diag_r : true if R is a diagonal matrix, false by default.

                                                            Returns: * x : a symmetric solution matrix X.

                                                            Low-level factorisation functions
                                                            val lufact : ('a, 'b) t -> ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            lufact x -> (a, ipiv) calculates LU factorisation with pivot of a general matrix x.

                                                            val qrfact : ?pivot:bool -> ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            qrfact x -> (a, tau, jpvt) calculates QR factorisation of a general matrix x.

                                                            val bkfact : @@ -75,7 +95,8 @@ ?symmetric:bool -> ?rook:bool -> ('a, 'b) t -> - ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            bk x -> (a, ipiv) calculates Bunch-Kaufman factorisation of x. If symmetric = true then x is symmetric, if symmetric = false then x is hermitian. If rook = true the function performs bounded Bunch-Kaufman ("rook") diagonal pivoting method, if rook = false then Bunch-Kaufman diagonal pivoting method is used. a contains details of the block-diagonal matrix d and the multipliers used to obtain the factor u (or l).

                                                            The upper indicates whether the upper or lower triangular part of x is stored and how x is factored. If upper = true then upper triangular part is stored: x = u*d*u' else x = l*d*l'.

                                                            For ipiv, it indicates the details of the interchanges and the block structure of d. Please refer to the function sytrf, hetrf in MKL documentation for more details.

                                                            Matrix functions
                                                            val mpow : ('a, 'b) t -> float -> ('a, 'b) t

                                                            mpow x r returns the dot product of square matrix x with itself r times, and more generally raises the matrix to the rth power. r is a float that must be equal to an integer; it can be be negative, zero, or positive. Non-integer exponents are not yet implemented. (If r is negative, mpow calls inv, and warnings in documentation for inv apply.)

                                                            val expm : ('a, 'b) t -> ('a, 'b) t

                                                            expm x computes the matrix exponential of x defined by

                                                            .. math:: e^x = \sum_k=0^\infty \frac

                                                            k! x^k

                                                            The function implements the scaling and squaring algorithm which uses Padé approximation to compute the matrix exponential :cite:`al2009new`.

                                                            val sinm : ('a, 'b) t -> ('a, 'b) t

                                                            sinm x computes the matrix sine of input x. The function uses expm to compute the matrix exponentials.

                                                            val cosm : ('a, 'b) t -> ('a, 'b) t

                                                            cosm x computes the matrix cosine of input x. The function uses expm to compute the matrix exponentials.

                                                            val tanm : ('a, 'b) t -> ('a, 'b) t

                                                            tanm x computes the matrix tangent of input x. The function uses expm to compute the matrix exponentials.

                                                            val sincosm : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            sincosm x returns both matrix sine and cosine of x.

                                                            val sinhm : ('a, 'b) t -> ('a, 'b) t

                                                            sinhm x computes the hyperbolic matrix sine of input x. The function uses expm to compute the matrix exponentials.

                                                            val coshm : ('a, 'b) t -> ('a, 'b) t

                                                            coshm x computes the hyperbolic matrix cosine of input x. The function uses expm to compute the matrix exponentials.

                                                            val tanhm : ('a, 'b) t -> ('a, 'b) t

                                                            tanhm x computes the hyperbolic matrix tangent of input x. The function uses expm to compute the matrix exponentials.

                                                            val sinhcoshm : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            sinhcoshm x returns both hyperbolic matrix sine and cosine of x.

                                                            Helper functions
                                                            val select_ev : + ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            bk x -> (a, ipiv) calculates Bunch-Kaufman factorisation of x. If symmetric = true then x is symmetric, if symmetric = false then x is hermitian. If rook = true the function performs bounded Bunch-Kaufman ("rook") diagonal pivoting method, if rook = false then Bunch-Kaufman diagonal pivoting method is used. a contains details of the block-diagonal matrix d and the multipliers used to obtain the factor u (or l).

                                                            The upper indicates whether the upper or lower triangular part of x is stored and how x is factored. If upper = true then upper triangular part is stored: x = u*d*u' else x = l*d*l'.

                                                            For ipiv, it indicates the details of the interchanges and the block structure of d. Please refer to the function sytrf, hetrf in MKL documentation for more details.

                                                            Matrix functions
                                                            val mpow : ('a, 'b) t -> float -> ('a, 'b) t

                                                            mpow x r returns the dot product of square matrix x with itself r times, and more generally raises the matrix to the rth power. r is a float that must be equal to an integer; it can be be negative, zero, or positive. Non-integer exponents are not yet implemented. (If r is negative, mpow calls inv, and warnings in documentation for inv apply.)

                                                            val expm : ('a, 'b) t -> ('a, 'b) t

                                                            expm x computes the matrix exponential of x defined by

                                                            +  e^x = \sum_{k=0}^{\infty} \frac{1}{k!} x^k

                                                            The function implements the scaling and squaring algorithm which uses Padé approximation to compute the matrix exponential :cite:`al2009new`.

                                                            val sinm : ('a, 'b) t -> ('a, 'b) t

                                                            sinm x computes the matrix sine of input x. The function uses expm to compute the matrix exponentials.

                                                            val cosm : ('a, 'b) t -> ('a, 'b) t

                                                            cosm x computes the matrix cosine of input x. The function uses expm to compute the matrix exponentials.

                                                            val tanm : ('a, 'b) t -> ('a, 'b) t

                                                            tanm x computes the matrix tangent of input x. The function uses expm to compute the matrix exponentials.

                                                            val sincosm : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            sincosm x returns both matrix sine and cosine of x.

                                                            val sinhm : ('a, 'b) t -> ('a, 'b) t

                                                            sinhm x computes the hyperbolic matrix sine of input x. The function uses expm to compute the matrix exponentials.

                                                            val coshm : ('a, 'b) t -> ('a, 'b) t

                                                            coshm x computes the hyperbolic matrix cosine of input x. The function uses expm to compute the matrix exponentials.

                                                            val tanhm : ('a, 'b) t -> ('a, 'b) t

                                                            tanhm x computes the hyperbolic matrix tangent of input x. The function uses expm to compute the matrix exponentials.

                                                            val sinhcoshm : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            sinhcoshm x returns both hyperbolic matrix sine and cosine of x.

                                                            Helper functions
                                                            val select_ev : [ `LHP | `RHP | `UDI | `UDO ] -> ('a, 'b) t -> - (int32, Stdlib.Bigarray.int32_elt) t

                                                            select_ev keyword ev generates a logical vector (of same shape as ev) from eigen values ev according to the passed in keywards.

                                                            • LHP: Left-half plane :math:`(real(e) < 0)`.
                                                            • RHP: Left-half plane :math:`(real(e) \ge 0)`.
                                                            • UDI: Left-half plane :math:`(abs(e) < 1)`.
                                                            • UDO: Left-half plane :math:`(abs(e) \ge 0)`.
                                                            val peakflops : ?n:int -> unit -> float

                                                            peakflops () returns the peak number of float point operations using Owl_cblas_basic.dgemm function. The default matrix size is 2000 x 2000, but you can change this by setting n to other numbers as you like.

                                                            + (int32, Stdlib.Bigarray.int32_elt) t

                                                            select_ev keyword ev generates a logical vector (of same shape as ev) from eigen values ev according to the passed in keywards.

                                                            • LHP: Left-half plane (real(e) < 0).
                                                            • RHP: Left-half plane (real(e) \ge 0).
                                                            • UDI: Left-half plane (abs(e) < 1).
                                                            • UDO: Left-half plane (abs(e) \ge 0).
                                                            val peakflops : ?n:int -> unit -> float

                                                            peakflops () returns the peak number of float point operations using Owl_cblas_basic.dgemm function. The default matrix size is 2000 x 2000, but you can change this by setting n to other numbers as you like.

                                                            diff --git a/docs/owl/Owl_linalg/S/index.html b/docs/owl/Owl_linalg/S/index.html index ec1900538..05ade6cd4 100644 --- a/docs/owl/Owl_linalg/S/index.html +++ b/docs/owl/Owl_linalg/S/index.html @@ -1,5 +1,5 @@ -S (owl.Owl_linalg.S)

                                                            Module Owl_linalg.S

                                                            include module type of struct include Owl_linalg_s end
                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_c.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +S (owl.Owl_linalg.S)

                                                            Module Owl_linalg.S

                                                            include module type of struct include Owl_linalg_s end
                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_c.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat := complex_mat diff --git a/docs/owl/Owl_linalg/Z/index.html b/docs/owl/Owl_linalg/Z/index.html index 7b63e9e65..cdeee0e4a 100644 --- a/docs/owl/Owl_linalg/Z/index.html +++ b/docs/owl/Owl_linalg/Z/index.html @@ -1,5 +1,5 @@ -Z (owl.Owl_linalg.Z)

                                                            Module Owl_linalg.Z

                                                            include module type of struct include Owl_linalg_z end
                                                            type elt = Stdlib.Complex.t
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +Z (owl.Owl_linalg.Z)

                                                            Module Owl_linalg.Z

                                                            include module type of struct include Owl_linalg_z end
                                                            type elt = Stdlib.Complex.t
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat = mat diff --git a/docs/owl/Owl_linalg/index.html b/docs/owl/Owl_linalg/index.html index 1fac22701..a87ba8c24 100644 --- a/docs/owl/Owl_linalg/index.html +++ b/docs/owl/Owl_linalg/index.html @@ -1,2 +1,19 @@ -Owl_linalg (owl.Owl_linalg)

                                                            Module Owl_linalg

                                                            Linear algebra: module aliases

                                                            module Generic : sig ... end
                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            module C : sig ... end
                                                            module Z : sig ... end
                                                            +Owl_linalg (owl.Owl_linalg)

                                                            Module Owl_linalg

                                                            Linear algebra: module aliases

                                                            module Generic : sig ... end
                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            module C : sig ... end
                                                            module Z : sig ... end
                                                            diff --git a/docs/owl/Owl_linalg_c/index.html b/docs/owl/Owl_linalg_c/index.html index 3456e0710..ba5cc4cb7 100644 --- a/docs/owl/Owl_linalg_c/index.html +++ b/docs/owl/Owl_linalg_c/index.html @@ -1,5 +1,5 @@ -Owl_linalg_c (owl.Owl_linalg_c)

                                                            Module Owl_linalg_c

                                                            type elt = Stdlib.Complex.t
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +Owl_linalg_c (owl.Owl_linalg_c)

                                                            Module Owl_linalg_c

                                                            type elt = Stdlib.Complex.t
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat = mat diff --git a/docs/owl/Owl_linalg_d/index.html b/docs/owl/Owl_linalg_d/index.html index 8a3defe5c..847065117 100644 --- a/docs/owl/Owl_linalg_d/index.html +++ b/docs/owl/Owl_linalg_d/index.html @@ -1,5 +1,5 @@ -Owl_linalg_d (owl.Owl_linalg_d)

                                                            Module Owl_linalg_d

                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_z.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +Owl_linalg_d (owl.Owl_linalg_d)

                                                            Module Owl_linalg_d

                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_z.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat := complex_mat diff --git a/docs/owl/Owl_linalg_generic/index.html b/docs/owl/Owl_linalg_generic/index.html index 84e25dc36..c0a0db522 100644 --- a/docs/owl/Owl_linalg_generic/index.html +++ b/docs/owl/Owl_linalg_generic/index.html @@ -1,5 +1,23 @@ -Owl_linalg_generic (owl.Owl_linalg_generic)

                                                            Module Owl_linalg_generic

                                                            Linear algebra module including high-level functions to solve linear systems, factorisation, and etc.

                                                            The module includes a set of advanced linear algebra operations such as singular value decomposition, and etc.

                                                            Currently, Linalg module supports dense matrix of four different number types, including float32, float64, complex32, and complex64. The support for sparse matrices will be provided in future.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b) Owl_dense_matrix_generic.t

                                                            Matrix type, a special case of N-dimensional array.

                                                            Basic functions
                                                            val inv : ('a, 'b) t -> ('a, 'b) t

                                                            inv x calculates the inverse of an invertible square matrix x such that x *@ x = I wherein I is an identity matrix. (If x is singular, inv will return a useless result.)

                                                            val pinv : ?tol:float -> ('a, 'b) t -> ('a, 'b) t

                                                            pinv x computes Moore-Penrose pseudoinverse of matrix x. tol specifies the tolerance, the absolute value of the elements smaller than tol will be set to zeros.

                                                            val det : ('a, 'b) t -> 'a

                                                            det x computes the determinant of a square matrix x.

                                                            val logdet : ('a, 'b) t -> 'a

                                                            logdet x computes the log of the determinant of a square matrix x. It is equivalent to log (det x) but may provide more accuracy and efficiency.

                                                            val rank : ?tol:float -> ('a, 'b) t -> int

                                                            rank x calculates the rank of a rectangular matrix x of shape m x n. The function does so by counting the number of singular values of x which are beyond a pre-defined threshold tol. By default, tol = max(m,n) * eps where eps = 1e-10.

                                                            val norm : ?p:float -> ('a, 'b) t -> float

                                                            norm ~p x computes the matrix p-norm of the passed in matrix x.

                                                            Parameters: * p is the order of norm, the default value is 2. * x is the input matrix.

                                                            Returns: * If p = 1, then returns the maximum absolute column sum of the matrix. * If p = 2, then returns approximately max (svd x). * If p = infinity, then returns the maximum absolute row sum of the matrix. * If p = -1, then returns the minimum absolute column sum of the matrix. * If p = -2, then returns approximately min (svd x). * If p = -infinity, then returns the minimum absolute row sum of the matrix.

                                                            val vecnorm : ?p:float -> ('a, 'b) t -> float

                                                            vecnorm ~p x calculates the generalised vector p-norm, defined as below. If x is a martrix, it will be flatten to a vector first. Different from the function of the same name in :doc:`owl_dense_ndarray_generic`, this function assumes the input is either 1d vector or 2d matrix.

                                                            .. math:: ||v||_p = \Big \sum_{k=0}^{N-1} |v_k|^p \Big^

                                                            /p

                                                            Parameters: * p is the order of norm, the default value is 2. * x is the input vector or matrix.

                                                            Returns: * If p = infinity, then returns :math:`||v||_\infty = \max_i(|v(i)|)`. * If p = -infinity, then returns :math:`||v||_

                                                            \infty

                                                            }

                                                            = \min_i(|v(i)|)`. * If p = 2 and x is a matrix, then returns Frobenius norm of x. * Otherwise returns generalised vector p-norm defined above.

                                                            val cond : ?p:float -> ('a, 'b) t -> float

                                                            cond ~p x computes the p-norm condition number of matrix x.

                                                            cond ~p:1. x returns the 1-norm condition number;

                                                            cond ~p:2. x or cond x returns the 2-norm condition number.

                                                            cond ~p:infinity x returns the infinity norm condition number.

                                                            The default value of p is 2.

                                                            val rcond : ('a, 'b) t -> float

                                                            rcond x returns an estimate for the reciprocal condition of x in 1-norm. If x is well conditioned, the returned result is near 1.0. If x is badly conditioned, the result is near 0.

                                                            Check matrix types
                                                            val is_square : ('a, 'b) t -> bool

                                                            is_square x returns true if x is a square matrix otherwise false.

                                                            val is_triu : ('a, 'b) t -> bool

                                                            is_triu x returns true if x is upper triangular otherwise false.

                                                            val is_tril : ('a, 'b) t -> bool

                                                            is_tril x returns true if x is lower triangular otherwise false.

                                                            val is_symmetric : ('a, 'b) t -> bool

                                                            is_symmetric x returns true if x is symmetric otherwise false.

                                                            val is_hermitian : (Stdlib.Complex.t, 'a) t -> bool

                                                            is_hermitian x returns true if x is hermitian otherwise false.

                                                            val is_diag : ('a, 'b) t -> bool

                                                            is_diag x returns true if x is diagonal otherwise false.

                                                            val is_posdef : ('a, 'b) t -> bool

                                                            is_posdef x checks whether x is a positive semi-definite matrix.

                                                            Factorisation
                                                            val lu : +Owl_linalg_generic (owl.Owl_linalg_generic)

                                                            Module Owl_linalg_generic

                                                            Linear algebra module including high-level functions to solve linear systems, factorisation, and etc.

                                                            The module includes a set of advanced linear algebra operations such as singular value decomposition, and etc.

                                                            Currently, Linalg module supports dense matrix of four different number types, including float32, float64, complex32, and complex64. The support for sparse matrices will be provided in future.

                                                            Type definition
                                                            type ('a, 'b) t = ('a, 'b) Owl_dense_matrix_generic.t

                                                            Matrix type, a special case of N-dimensional array.

                                                            Basic functions
                                                            val inv : ('a, 'b) t -> ('a, 'b) t

                                                            inv x calculates the inverse of an invertible square matrix x such that x *@ x = I wherein I is an identity matrix. (If x is singular, inv will return a useless result.)

                                                            val pinv : ?tol:float -> ('a, 'b) t -> ('a, 'b) t

                                                            pinv x computes Moore-Penrose pseudoinverse of matrix x. tol specifies the tolerance, the absolute value of the elements smaller than tol will be set to zeros.

                                                            val det : ('a, 'b) t -> 'a

                                                            det x computes the determinant of a square matrix x.

                                                            val logdet : ('a, 'b) t -> 'a

                                                            logdet x computes the log of the determinant of a square matrix x. It is equivalent to log (det x) but may provide more accuracy and efficiency.

                                                            val rank : ?tol:float -> ('a, 'b) t -> int

                                                            rank x calculates the rank of a rectangular matrix x of shape m x n. The function does so by counting the number of singular values of x which are beyond a pre-defined threshold tol. By default, tol = max(m,n) * eps where eps = 1e-10.

                                                            val norm : ?p:float -> ('a, 'b) t -> float

                                                            norm ~p x computes the matrix p-norm of the passed in matrix x.

                                                            Parameters: * p is the order of norm, the default value is 2. * x is the input matrix.

                                                            Returns: * If p = 1, then returns the maximum absolute column sum of the matrix. * If p = 2, then returns approximately max (svd x). * If p = infinity, then returns the maximum absolute row sum of the matrix. * If p = -1, then returns the minimum absolute column sum of the matrix. * If p = -2, then returns approximately min (svd x). * If p = -infinity, then returns the minimum absolute row sum of the matrix.

                                                            val vecnorm : ?p:float -> ('a, 'b) t -> float

                                                            vecnorm ~p x calculates the generalised vector p-norm, defined as below. If x is a martrix, it will be flatten to a vector first. Different from the function of the same name in :doc:`owl_dense_ndarray_generic`, this function assumes the input is either 1d vector or 2d matrix.

                                                            +  ||v||_p = \Big[ \sum_{k=0}^{N-1} |v_k|^p \Big]^{1/p}

                                                            Parameters: * p is the order of norm, the default value is 2. * x is the input vector or matrix.

                                                            Returns: * If p = infinity, then returns ||v||_{\infty} = \max_i(|v(i)|) * If p = -infinity, then returns ||v||_{-\infty} = \min_i(|v(i)|). * If p = 2 and x is a matrix, then returns Frobenius norm of x. * Otherwise returns generalised vector p-norm defined above.

                                                            val cond : ?p:float -> ('a, 'b) t -> float

                                                            cond ~p x computes the p-norm condition number of matrix x.

                                                            cond ~p:1. x returns the 1-norm condition number;

                                                            cond ~p:2. x or cond x returns the 2-norm condition number.

                                                            cond ~p:infinity x returns the infinity norm condition number.

                                                            The default value of p is 2.

                                                            val rcond : ('a, 'b) t -> float

                                                            rcond x returns an estimate for the reciprocal condition of x in 1-norm. If x is well conditioned, the returned result is near 1.0. If x is badly conditioned, the result is near 0.

                                                            Check matrix types
                                                            val is_square : ('a, 'b) t -> bool

                                                            is_square x returns true if x is a square matrix otherwise false.

                                                            val is_triu : ('a, 'b) t -> bool

                                                            is_triu x returns true if x is upper triangular otherwise false.

                                                            val is_tril : ('a, 'b) t -> bool

                                                            is_tril x returns true if x is lower triangular otherwise false.

                                                            val is_symmetric : ('a, 'b) t -> bool

                                                            is_symmetric x returns true if x is symmetric otherwise false.

                                                            val is_hermitian : (Stdlib.Complex.t, 'a) t -> bool

                                                            is_hermitian x returns true if x is hermitian otherwise false.

                                                            val is_diag : ('a, 'b) t -> bool

                                                            is_diag x returns true if x is diagonal otherwise false.

                                                            val is_posdef : ('a, 'b) t -> bool

                                                            is_posdef x checks whether x is a positive semi-definite matrix.

                                                            Factorisation
                                                            val lu : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            lu x -> (l, u, ipiv) calculates LU decomposition of x. The pivoting is used by default.

                                                            val lq : ?thin:bool -> ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            lq x -> (l, q) calculates the LQ decomposition of x. By default, the reduced LQ decomposition is performed. But you can get full Q by setting parameter thin = false.

                                                            val qr : ?thin:bool -> @@ -11,16 +29,16 @@ ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t

                                                            gsvd x y -> (u, v, q, d1, d2, r) computes the generalised singular value decomposition of a pair of general rectangular matrices x and y. d1 and d2 contain the generalised singular value pairs of x and y. The shape of x is m x n and the shape of y is p x n.

                                                            .. code-block:: ocaml

                                                            let x = Mat.uniform 5 5;; let y = Mat.uniform 2 5;; let u, v, q, d1, d2, r = Linalg.gsvd x y;; Mat.(u *@ d1 *@ r *@ transpose q =~ x);; Mat.(v *@ d2 *@ r *@ transpose q =~ y);;

                                                            Please refer to: `Intel MKL Reference <https://software.intel.com/en-us/mkl-developer-reference-c-ggsvd3>`_

                                                            val gsvdvals : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            gsvdvals x y is similar to gsvd x y but only returns the singular values of the generalised singular value decomposition of x and y.

                                                            val schur : otyp:('c, 'd) Stdlib.Bigarray.kind -> ('a, 'b) t -> - ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            schur x -> (t, z, w) calculates Schur factorisation of x in the following form.

                                                            .. math:: X = Z T Z^H

                                                            Parameters: * otyp: the complex type of eigen values. * x: the n x n square matrix.

                                                            Returns: * t is (quasi) triangular Schur factor. * z is orthogonal/unitary Schur vectors. The eigen values are not sorted, they have the same order as that they appear on the diagonal of the output of Schur form t. * w contains the eigen values of x. otyp is used to specify the type of w. It needs to be consistent with input type. E.g., if the input x is float32 then otyp must be complex32. However, if you use S, D, C, Z module, then you do not need to worry about otyp.

                                                            val schur_tz : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            schur_tz x is similar to schur but only returns (t, z).

                                                            val ordschur : + ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            schur x -> (t, z, w) calculates Schur factorisation of x in the following form: X = Z T Z^H.

                                                            Parameters: * otyp: the complex type of eigen values. * x: the n x n square matrix.

                                                            Returns: * t is (quasi) triangular Schur factor. * z is orthogonal/unitary Schur vectors. The eigen values are not sorted, they have the same order as that they appear on the diagonal of the output of Schur form t. * w contains the eigen values of x. otyp is used to specify the type of w. It needs to be consistent with input type. E.g., if the input x is float32 then otyp must be complex32. However, if you use S, D, C, Z module, then you do not need to worry about otyp.

                                                            val schur_tz : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            schur_tz x is similar to schur but only returns (t, z).

                                                            val ordschur : otyp:('c, 'd) Stdlib.Bigarray.kind -> select:(int32, Stdlib.Bigarray.int32_elt) t -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            ordschur ~select t z -> (r, p) reorders t and z returned by Schur factorization schur x -> (t, z) according select such that

                                                            .. math:: X = P R P^H

                                                            Parameters: * otyp: the complex type of eigen values * select the logical vector to select eigenvalues, refer to select_ev. * t: the Schur matrix returned by schur x. * z: the unitary matrix z returned by schur x.

                                                            Returns: * r: reordered Schur matrix t. * p: reordered orthogonal matrix z.

                                                            val qz : + ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            ordschur ~select t z -> (r, p) reorders t and z returned by Schur factorization schur x -> (t, z) according select such that X = P R P^H.

                                                            Parameters: * otyp: the complex type of eigen values * select the logical vector to select eigenvalues, refer to select_ev. * t: the Schur matrix returned by schur x. * z: the unitary matrix z returned by schur x.

                                                            Returns: * r: reordered Schur matrix t. * p: reordered orthogonal matrix z.

                                                            val qz : otyp:('c, 'd) Stdlib.Bigarray.kind -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            qz x -> (s, t, q, z, w) calculates generalised Schur factorisation of x in the following form. It is also known as QZ decomposition.

                                                            .. math:: X = Q S Z^H Y = Z T Z^H

                                                            Parameters: * otyp: the complex type of eigen values. * x: the n x n square matrix. * y: the n x n square matrix.

                                                            Returns: * s: the upper quasitriangular matrices S. * t: the upper quasitriangular matrices T. * q: the unitary matrices Q. * z: the unitary matrices Z. * w: the generalised eigenvalue for a pair of matrices (X,Y).

                                                            val ordqz : + ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('a, 'b) t * ('c, 'd) t

                                                            qz x -> (s, t, q, z, w) calculates generalised Schur factorisation of x in the following form. It is also known as QZ decomposition.

                                                            X = Q S Z^H Y = Z T Z^H

                                                            Parameters: * otyp: the complex type of eigen values. * x: the n x n square matrix. * y: the n x n square matrix.

                                                            Returns: * s: the upper quasitriangular matrices S. * t: the upper quasitriangular matrices T. * q: the unitary matrices Q. * z: the unitary matrices Z. * w: the generalised eigenvalue for a pair of matrices (X,Y).

                                                            val ordqz : otyp:('c, 'd) Stdlib.Bigarray.kind -> select:(int32, Stdlib.Bigarray.int32_elt) t -> ('a, 'b) t -> @@ -31,7 +49,7 @@ otyp:('c, 'd) Stdlib.Bigarray.kind -> ('a, 'b) t -> ('a, 'b) t -> - ('c, 'd) t

                                                            qzvals ~otyp x y is similar to qz ~otyp x y but only returns the generalised eigen values.

                                                            val hess : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            hess x -> (h, q) calculates the Hessenberg form of a given matrix x. Both Hessenberg matrix h and unitary matrix q is returned, such that x = q *@ h *@ (transpose q).

                                                            .. math:: X = Q H Q^T

                                                            Eigenvalues & eigenvectors
                                                            val eig : + ('c, 'd) t

                                                            qzvals ~otyp x y is similar to qz ~otyp x y but only returns the generalised eigen values.

                                                            val hess : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            hess x -> (h, q) calculates the Hessenberg form of a given matrix x. Both Hessenberg matrix h and unitary matrix q is returned, such that x = q *@ h *@ (transpose q).

                                                            X = Q H Q^T

                                                            Eigenvalues & eigenvectors
                                                            val eig : ?permute:bool -> ?scale:bool -> otyp:('a, 'b) Stdlib.Bigarray.kind -> @@ -41,33 +59,35 @@ ?scale:bool -> otyp:('a, 'b) Stdlib.Bigarray.kind -> ('c, 'd) t -> - ('a, 'b) t

                                                            eigvals x -> w is similar to eig but only computes the eigenvalues of an arbitrary square matrix x.

                                                            Linear system of equations
                                                            val null : ('a, 'b) t -> ('a, 'b) t

                                                            null a -> x computes an orthonormal basis x for the null space of a obtained from the singular value decomposition. Namely, a *@ x has negligible elements, M.col_num x is the nullity of a, and transpose x *@ x = I. Namely,

                                                            .. math:: X^T X = I

                                                            val triangular_solve : + ('a, 'b) t

                                                            eigvals x -> w is similar to eig but only computes the eigenvalues of an arbitrary square matrix x.

                                                            Linear system of equations
                                                            val null : ('a, 'b) t -> ('a, 'b) t

                                                            null a -> x computes an orthonormal basis x for the null space of a obtained from the singular value decomposition. Namely, a *@ x has negligible elements, M.col_num x is the nullity of a, and transpose x *@ x = I. Namely,

                                                            X^T X = I

                                                            val triangular_solve : upper:bool -> ?trans:bool -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t

                                                            triangular_linsolve a b -> x solves a linear system of equations a * x = b where a is either a upper or a lower triangular matrix. This function uses cblas trsm under the hood.

                                                            .. math:: AX = B

                                                            By default, trans = false indicates no transpose. If trans = true, then function will solve A^T * x = b for real matrices; A^H * x = b for complex matrices.

                                                            .. math:: A^H X = B

                                                            val linsolve : + ('a, 'b) t

                                                            triangular_linsolve a b -> x solves a linear system of equations a * x = b where a is either a upper or a lower triangular matrix. This function uses cblas trsm under the hood.

                                                            AX = B

                                                            By default, trans = false indicates no transpose. If trans = true, then function will solve A^T * x = b for real matrices; A^H * x = b for complex matrices.

                                                            A^H X = B

                                                            val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t

                                                            linsolve a b -> x solves a linear system of equations a * x = b in the following form. By default, typ=`n and the function use LU factorisation with partial pivoting when a is square and QR factorisation with column pivoting otherwise. The number of rows of a must equal the number of rows of b. If a is a upper(lower) triangular matrix, the function calls the solve_triangular function when typ=`u(typ=`l).

                                                            .. math:: AX = B

                                                            By default, trans = false indicates no transpose. If trans = true, then function will solve A^T * x = b for real matrices; A^H * x = b for complex matrices.

                                                            .. math:: A^H X = B

                                                            The associated operator is /@, so you can simply use a /@ b to solve the linear equation system to get x. Please refer to :doc:`owl_operator`.

                                                            val linreg : ('a, 'b) t -> ('a, 'b) t -> 'a * 'a

                                                            linreg x y -> (a, b) solves y = a + b*x using Ordinary Least Squares.

                                                            .. math:: Y = A + BX

                                                            val sylvester : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            sylvester a b c solves a Sylvester equation in the following form. The function calls LAPACKE function trsyl solve the system.

                                                            .. math:: AX + XB = C

                                                            Parameters: * a : m x m matrix A. * b : n x n matrix B. * c : m x n matrix C.

                                                            Returns: * x : m x n matrix X.

                                                            val lyapunov : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            lyapunov a q solves a continuous Lyapunov equation in the following form. The function calls LAPACKE function trsyl solve the system. In Matlab, the same function is called lyap.

                                                            .. math:: AX + XA^H = Q

                                                            Parameters: * a : m x m matrix A. * q : n x n matrix Q.

                                                            Returns: * x : m x n matrix X.

                                                            val discrete_lyapunov : + ('a, 'b) t

                                                            linsolve a b -> x solves a linear system of equations a * x = b in the following form. By default, typ=`n and the function use LU factorisation with partial pivoting when a is square and QR factorisation with column pivoting otherwise. The number of rows of a must equal the number of rows of b. If a is a upper(lower) triangular matrix, the function calls the solve_triangular function when typ=`u(typ=`l).

                                                            AX = B

                                                            By default, trans = false indicates no transpose. If trans = true, then function will solve A^T * x = b for real matrices; A^H * x = b for complex matrices.

                                                            A^H X = B

                                                            The associated operator is /@, so you can simply use a /@ b to solve the linear equation system to get x. Please refer to :doc:`owl_operator`.

                                                            val linreg : ('a, 'b) t -> ('a, 'b) t -> 'a * 'a

                                                            linreg x y -> (a, b) solves y = a + b*x using Ordinary Least Squares.

                                                            Y = A + BX

                                                            val sylvester : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            sylvester a b c solves a Sylvester equation in the following form. The function calls LAPACKE function trsyl solve the system.

                                                            AX + XB = C

                                                            Parameters: * a : m x m matrix A. * b : n x n matrix B. * c : m x n matrix C.

                                                            Returns: * x : m x n matrix X.

                                                            val lyapunov : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

                                                            lyapunov a q solves a continuous Lyapunov equation in the following form. The function calls LAPACKE function trsyl solve the system. In Matlab, the same function is called lyap.

                                                            AX + XA^H = Q

                                                            Parameters: * a : m x m matrix A. * q : n x n matrix Q.

                                                            Returns: * x : m x n matrix X.

                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> ('a, 'b) t -> ('a, 'b) t -> - ('a, 'b) t

                                                            discrete_lyapunov a q solves a discrete-time Lyapunov equation in the following form.

                                                            .. math:: X - AXA^H = Q

                                                            Parameters: * a : m x m matrix A. * q : n x n matrix Q.

                                                            Returns: * x : m x n matrix X.

                                                            val care : + ('a, 'b) t

                                                            discrete_lyapunov a q solves a discrete-time Lyapunov equation in the following form.

                                                            X - AXA^H = Q

                                                            Parameters: * a : m x m matrix A. * q : n x n matrix Q.

                                                            Returns: * x : m x n matrix X.

                                                            val care : ?diag_r:bool -> (float, 'a) t -> (float, 'a) t -> (float, 'a) t -> (float, 'a) t -> - (float, 'a) t

                                                            care ?diag_r a b q r solves the continuous-time algebraic Riccati equation system in the following form. The algorithm is based on :cite:`laub1979schur`.

                                                            .. math:: A^T X + X A − X B R^

                                                            1

                                                            }

                                                            B^T X + Q = 0

                                                            Parameters: * a : real cofficient matrix A. * b : real cofficient matrix B. * q : real cofficient matrix Q. * r : real cofficient matrix R. R must be non-singular. * diag_r : true if R is a diagonal matrix, false by default.

                                                            Returns: * x : a solution matrix X.

                                                            val dare : + (float, 'a) t

                                                            care ?diag_r a b q r solves the continuous-time algebraic Riccati equation system in the following form. The algorithm is based on :cite:`laub1979schur`.

                                                            +  A^T X + X A − X B R^{-1} B^T X + Q = 0

                                                            Parameters: * a : real cofficient matrix A. * b : real cofficient matrix B. * q : real cofficient matrix Q. * r : real cofficient matrix R. R must be non-singular. * diag_r : true if R is a diagonal matrix, false by default.

                                                            Returns: * x : a solution matrix X.

                                                            val dare : ?diag_r:bool -> (float, 'a) t -> (float, 'a) t -> (float, 'a) t -> (float, 'a) t -> - (float, 'a) t

                                                            dare ?diag_r a b q r solves the discrete-time algebraic Riccati equation system in the following form. The algorithm is based on :cite:`laub1979schur`.

                                                            .. math:: A^T X A - X - (A^T X B) (B^T X B + R)^

                                                            1

                                                            }

                                                            (B^T X A) + Q = 0

                                                            Parameters: * a : real cofficient matrix A. A must be non-singular. * b : real cofficient matrix B. * q : real cofficient matrix Q. * r : real cofficient matrix R. R must be non-singular. * diag_r : true if R is a diagonal matrix, false by default.

                                                            Returns: * x : a symmetric solution matrix X.

                                                            Low-level factorisation functions
                                                            val lufact : ('a, 'b) t -> ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            lufact x -> (a, ipiv) calculates LU factorisation with pivot of a general matrix x.

                                                            val qrfact : + (float, 'a) t

                                                            dare ?diag_r a b q r solves the discrete-time algebraic Riccati equation system in the following form.

                                                            +  A^T X A - X - (A^T X B) (B^T X B + R)^{-1} (B^T X A) + Q = 0

                                                            Parameters: * a : real cofficient matrix A. A must be non-singular. * b : real cofficient matrix B. * q : real cofficient matrix Q. * r : real cofficient matrix R. R must be non-singular. * diag_r : true if R is a diagonal matrix, false by default.

                                                            Returns: * x : a symmetric solution matrix X.

                                                            Low-level factorisation functions
                                                            val lufact : ('a, 'b) t -> ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            lufact x -> (a, ipiv) calculates LU factorisation with pivot of a general matrix x.

                                                            val qrfact : ?pivot:bool -> ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            qrfact x -> (a, tau, jpvt) calculates QR factorisation of a general matrix x.

                                                            val bkfact : @@ -75,7 +95,8 @@ ?symmetric:bool -> ?rook:bool -> ('a, 'b) t -> - ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            bk x -> (a, ipiv) calculates Bunch-Kaufman factorisation of x. If symmetric = true then x is symmetric, if symmetric = false then x is hermitian. If rook = true the function performs bounded Bunch-Kaufman ("rook") diagonal pivoting method, if rook = false then Bunch-Kaufman diagonal pivoting method is used. a contains details of the block-diagonal matrix d and the multipliers used to obtain the factor u (or l).

                                                            The upper indicates whether the upper or lower triangular part of x is stored and how x is factored. If upper = true then upper triangular part is stored: x = u*d*u' else x = l*d*l'.

                                                            For ipiv, it indicates the details of the interchanges and the block structure of d. Please refer to the function sytrf, hetrf in MKL documentation for more details.

                                                            Matrix functions
                                                            val mpow : ('a, 'b) t -> float -> ('a, 'b) t

                                                            mpow x r returns the dot product of square matrix x with itself r times, and more generally raises the matrix to the rth power. r is a float that must be equal to an integer; it can be be negative, zero, or positive. Non-integer exponents are not yet implemented. (If r is negative, mpow calls inv, and warnings in documentation for inv apply.)

                                                            val expm : ('a, 'b) t -> ('a, 'b) t

                                                            expm x computes the matrix exponential of x defined by

                                                            .. math:: e^x = \sum_k=0^\infty \frac

                                                            k! x^k

                                                            The function implements the scaling and squaring algorithm which uses Padé approximation to compute the matrix exponential :cite:`al2009new`.

                                                            val sinm : ('a, 'b) t -> ('a, 'b) t

                                                            sinm x computes the matrix sine of input x. The function uses expm to compute the matrix exponentials.

                                                            val cosm : ('a, 'b) t -> ('a, 'b) t

                                                            cosm x computes the matrix cosine of input x. The function uses expm to compute the matrix exponentials.

                                                            val tanm : ('a, 'b) t -> ('a, 'b) t

                                                            tanm x computes the matrix tangent of input x. The function uses expm to compute the matrix exponentials.

                                                            val sincosm : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            sincosm x returns both matrix sine and cosine of x.

                                                            val sinhm : ('a, 'b) t -> ('a, 'b) t

                                                            sinhm x computes the hyperbolic matrix sine of input x. The function uses expm to compute the matrix exponentials.

                                                            val coshm : ('a, 'b) t -> ('a, 'b) t

                                                            coshm x computes the hyperbolic matrix cosine of input x. The function uses expm to compute the matrix exponentials.

                                                            val tanhm : ('a, 'b) t -> ('a, 'b) t

                                                            tanhm x computes the hyperbolic matrix tangent of input x. The function uses expm to compute the matrix exponentials.

                                                            val sinhcoshm : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            sinhcoshm x returns both hyperbolic matrix sine and cosine of x.

                                                            Helper functions
                                                            val select_ev : + ('a, 'b) t * (int32, Stdlib.Bigarray.int32_elt) t

                                                            bk x -> (a, ipiv) calculates Bunch-Kaufman factorisation of x. If symmetric = true then x is symmetric, if symmetric = false then x is hermitian. If rook = true the function performs bounded Bunch-Kaufman ("rook") diagonal pivoting method, if rook = false then Bunch-Kaufman diagonal pivoting method is used. a contains details of the block-diagonal matrix d and the multipliers used to obtain the factor u (or l).

                                                            The upper indicates whether the upper or lower triangular part of x is stored and how x is factored. If upper = true then upper triangular part is stored: x = u*d*u' else x = l*d*l'.

                                                            For ipiv, it indicates the details of the interchanges and the block structure of d. Please refer to the function sytrf, hetrf in MKL documentation for more details.

                                                            Matrix functions
                                                            val mpow : ('a, 'b) t -> float -> ('a, 'b) t

                                                            mpow x r returns the dot product of square matrix x with itself r times, and more generally raises the matrix to the rth power. r is a float that must be equal to an integer; it can be be negative, zero, or positive. Non-integer exponents are not yet implemented. (If r is negative, mpow calls inv, and warnings in documentation for inv apply.)

                                                            val expm : ('a, 'b) t -> ('a, 'b) t

                                                            expm x computes the matrix exponential of x defined by

                                                            +  e^x = \sum_{k=0}^{\infty} \frac{1}{k!} x^k

                                                            The function implements the scaling and squaring algorithm which uses Padé approximation to compute the matrix exponential :cite:`al2009new`.

                                                            val sinm : ('a, 'b) t -> ('a, 'b) t

                                                            sinm x computes the matrix sine of input x. The function uses expm to compute the matrix exponentials.

                                                            val cosm : ('a, 'b) t -> ('a, 'b) t

                                                            cosm x computes the matrix cosine of input x. The function uses expm to compute the matrix exponentials.

                                                            val tanm : ('a, 'b) t -> ('a, 'b) t

                                                            tanm x computes the matrix tangent of input x. The function uses expm to compute the matrix exponentials.

                                                            val sincosm : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            sincosm x returns both matrix sine and cosine of x.

                                                            val sinhm : ('a, 'b) t -> ('a, 'b) t

                                                            sinhm x computes the hyperbolic matrix sine of input x. The function uses expm to compute the matrix exponentials.

                                                            val coshm : ('a, 'b) t -> ('a, 'b) t

                                                            coshm x computes the hyperbolic matrix cosine of input x. The function uses expm to compute the matrix exponentials.

                                                            val tanhm : ('a, 'b) t -> ('a, 'b) t

                                                            tanhm x computes the hyperbolic matrix tangent of input x. The function uses expm to compute the matrix exponentials.

                                                            val sinhcoshm : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

                                                            sinhcoshm x returns both hyperbolic matrix sine and cosine of x.

                                                            Helper functions
                                                            val select_ev : [ `LHP | `RHP | `UDI | `UDO ] -> ('a, 'b) t -> - (int32, Stdlib.Bigarray.int32_elt) t

                                                            select_ev keyword ev generates a logical vector (of same shape as ev) from eigen values ev according to the passed in keywards.

                                                            • LHP: Left-half plane :math:`(real(e) < 0)`.
                                                            • RHP: Left-half plane :math:`(real(e) \ge 0)`.
                                                            • UDI: Left-half plane :math:`(abs(e) < 1)`.
                                                            • UDO: Left-half plane :math:`(abs(e) \ge 0)`.
                                                            val peakflops : ?n:int -> unit -> float

                                                            peakflops () returns the peak number of float point operations using Owl_cblas_basic.dgemm function. The default matrix size is 2000 x 2000, but you can change this by setting n to other numbers as you like.

                                                            + (int32, Stdlib.Bigarray.int32_elt) t

                                                            select_ev keyword ev generates a logical vector (of same shape as ev) from eigen values ev according to the passed in keywards.

                                                            • LHP: Left-half plane (real(e) < 0).
                                                            • RHP: Left-half plane (real(e) \ge 0).
                                                            • UDI: Left-half plane (abs(e) < 1).
                                                            • UDO: Left-half plane (abs(e) \ge 0).
                                                            val peakflops : ?n:int -> unit -> float

                                                            peakflops () returns the peak number of float point operations using Owl_cblas_basic.dgemm function. The default matrix size is 2000 x 2000, but you can change this by setting n to other numbers as you like.

                                                            diff --git a/docs/owl/Owl_linalg_intf/index.html b/docs/owl/Owl_linalg_intf/index.html index 069e2b7cd..684750b38 100644 --- a/docs/owl/Owl_linalg_intf/index.html +++ b/docs/owl/Owl_linalg_intf/index.html @@ -1,2 +1,2 @@ -Owl_linalg_intf (owl.Owl_linalg_intf)

                                                            Module Owl_linalg_intf

                                                            module type Common = sig ... end
                                                            module type Real = sig ... end
                                                            +Owl_linalg_intf (owl.Owl_linalg_intf)

                                                            Module Owl_linalg_intf

                                                            module type Common = sig ... end
                                                            module type Real = sig ... end
                                                            diff --git a/docs/owl/Owl_linalg_intf/module-type-Common/index.html b/docs/owl/Owl_linalg_intf/module-type-Common/index.html index f803e7fe5..7b2d6c899 100644 --- a/docs/owl/Owl_linalg_intf/module-type-Common/index.html +++ b/docs/owl/Owl_linalg_intf/module-type-Common/index.html @@ -1,5 +1,5 @@ -Common (owl.Owl_linalg_intf.Common)

                                                            Module type Owl_linalg_intf.Common

                                                            include Owl_base_linalg_intf.Common
                                                            type elt
                                                            type mat
                                                            type complex_mat
                                                            type int32_mat
                                                            Basic functions
                                                            val inv : mat -> mat
                                                            val det : mat -> elt
                                                            val logdet : mat -> elt
                                                            val is_triu : mat -> bool
                                                            val is_tril : mat -> bool
                                                            val is_symmetric : mat -> bool
                                                            val is_diag : mat -> bool
                                                            Factorisation
                                                            val svd : ?thin:bool -> mat -> mat * mat * mat
                                                            val chol : ?upper:bool -> mat -> mat
                                                            val qr : ?thin:bool -> ?pivot:bool -> mat -> mat * mat * int32_mat
                                                            val lq : ?thin:bool -> mat -> mat * mat
                                                            Linear system of equations
                                                            val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> mat -> mat -> mat
                                                            val sylvester : mat -> mat -> mat -> mat
                                                            val lyapunov : mat -> mat -> mat
                                                            val discrete_lyapunov : +Common (owl.Owl_linalg_intf.Common)

                                                            Module type Owl_linalg_intf.Common

                                                            include Owl_base_linalg_intf.Common
                                                            type elt
                                                            type mat
                                                            type complex_mat
                                                            type int32_mat
                                                            Basic functions
                                                            val inv : mat -> mat
                                                            val det : mat -> elt
                                                            val logdet : mat -> elt
                                                            val is_triu : mat -> bool
                                                            val is_tril : mat -> bool
                                                            val is_symmetric : mat -> bool
                                                            val is_diag : mat -> bool
                                                            Factorisation
                                                            val svd : ?thin:bool -> mat -> mat * mat * mat
                                                            val chol : ?upper:bool -> mat -> mat
                                                            val qr : ?thin:bool -> ?pivot:bool -> mat -> mat * mat * int32_mat
                                                            val lq : ?thin:bool -> mat -> mat * mat
                                                            Linear system of equations
                                                            val linsolve : ?trans:bool -> ?typ:[ `n | `u | `l ] -> mat -> mat -> mat
                                                            val sylvester : mat -> mat -> mat -> mat
                                                            val lyapunov : mat -> mat -> mat
                                                            val discrete_lyapunov : ?solver:[ `default | `direct | `bilinear ] -> mat -> mat -> diff --git a/docs/owl/Owl_linalg_intf/module-type-Real/index.html b/docs/owl/Owl_linalg_intf/module-type-Real/index.html index 4c4bf35e7..272d3c5fd 100644 --- a/docs/owl/Owl_linalg_intf/module-type-Real/index.html +++ b/docs/owl/Owl_linalg_intf/module-type-Real/index.html @@ -1,2 +1,2 @@ -Real (owl.Owl_linalg_intf.Real)

                                                            Module type Owl_linalg_intf.Real

                                                            include Owl_base_linalg_intf.Real
                                                            type elt
                                                            type mat
                                                            val care : ?diag_r:bool -> mat -> mat -> mat -> mat -> mat
                                                            val dare : ?diag_r:bool -> mat -> mat -> mat -> mat -> mat
                                                            +Real (owl.Owl_linalg_intf.Real)

                                                            Module type Owl_linalg_intf.Real

                                                            include Owl_base_linalg_intf.Real
                                                            type elt
                                                            type mat
                                                            val care : ?diag_r:bool -> mat -> mat -> mat -> mat -> mat
                                                            val dare : ?diag_r:bool -> mat -> mat -> mat -> mat -> mat
                                                            diff --git a/docs/owl/Owl_linalg_s/index.html b/docs/owl/Owl_linalg_s/index.html index ff7261847..6d5cd9db8 100644 --- a/docs/owl/Owl_linalg_s/index.html +++ b/docs/owl/Owl_linalg_s/index.html @@ -1,5 +1,5 @@ -Owl_linalg_s (owl.Owl_linalg_s)

                                                            Module Owl_linalg_s

                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_c.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +Owl_linalg_s (owl.Owl_linalg_s)

                                                            Module Owl_linalg_s

                                                            type elt = float
                                                            type complex_mat = Owl_dense_matrix_c.mat
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat := complex_mat diff --git a/docs/owl/Owl_linalg_z/index.html b/docs/owl/Owl_linalg_z/index.html index 35f962a2c..fd5918e27 100644 --- a/docs/owl/Owl_linalg_z/index.html +++ b/docs/owl/Owl_linalg_z/index.html @@ -1,5 +1,5 @@ -Owl_linalg_z (owl.Owl_linalg_z)

                                                            Module Owl_linalg_z

                                                            type elt = Stdlib.Complex.t
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common +Owl_linalg_z (owl.Owl_linalg_z)

                                                            Module Owl_linalg_z

                                                            type elt = Stdlib.Complex.t
                                                            type int32_mat = (int32, Stdlib.Bigarray.int32_elt) Owl_dense_matrix_generic.t
                                                            include Owl_linalg_intf.Common with type elt := elt and type mat := mat and type complex_mat = mat diff --git a/docs/owl/Owl_maths/index.html b/docs/owl/Owl_maths/index.html index 996572362..9ca64469a 100644 --- a/docs/owl/Owl_maths/index.html +++ b/docs/owl/Owl_maths/index.html @@ -1,5 +1,5 @@ -Owl_maths (owl.Owl_maths)

                                                            Module Owl_maths

                                                            Maths: fundamental and advanced mathematical functions.

                                                            This module contains some basic and advanced mathematical operations. If you cannot find some function in this module, try Stats module.

                                                            Please refer to Scipy documentation.

                                                            Basic functions
                                                            val add : float -> float -> float

                                                            add x y returns :math:`x + y`.

                                                            val sub : float -> float -> float

                                                            sub x y returns :math:`x - y`.

                                                            val mul : float -> float -> float

                                                            mul x y returns :math:`x * y`.

                                                            val div : float -> float -> float

                                                            div x y returns :math:`x / y`.

                                                            val fmod : float -> float -> float

                                                            fmod x y returns :math:`x % y`.

                                                            val atan2 : float -> float -> float

                                                            atan2 y x returns :math:`\arctan(y/x)`, accounting for the sign of the arguments; this is the angle to the vector :math:`(x, y)` counting from the x-axis.

                                                            val abs : float -> float

                                                            abs x returns :math:`|x|`.

                                                            val neg : float -> float

                                                            neg x returns :math:`-x`.

                                                            val reci : float -> float

                                                            reci x returns :math:`1/x`.

                                                            val floor : float -> float

                                                            floor x returns the largest integer :math:`\leq x`.

                                                            val ceil : float -> float

                                                            ceil x returns the smallest integer :math:`\geq x`.

                                                            val round : float -> float

                                                            round x rounds, towards the bigger integer when on the fence.

                                                            val trunc : float -> float

                                                            trunc x integer part.

                                                            val sqr : float -> float

                                                            sqr x square.

                                                            val sqrt : float -> float

                                                            sqrt x square root.

                                                            val pow : float -> float -> float

                                                            pow x y returns x^y.

                                                            val exp : float -> float

                                                            exp x exponential.

                                                            val exp2 : float -> float

                                                            exp2 x exponential.

                                                            val exp10 : float -> float

                                                            exp10 x exponential.

                                                            val expm1 : float -> float

                                                            expm1 x returns :math:`\exp(x) - 1` but more accurate for :math:`x \sim 0`.

                                                            val log : float -> float

                                                            log x natural logarithm

                                                            val log2 : float -> float

                                                            log2 x base-2 logarithm.

                                                            val log10 : float -> float

                                                            log10 x base-10 logarithm.

                                                            val logn : float -> float -> float

                                                            logn x base-n logarithm.

                                                            val log1p : float -> float

                                                            log1p x returns :math:`\log (x + 1)` but more accurate for :math:`x \sim 0`. Inverse of expm1.

                                                            val logabs : float -> float

                                                            logabs x returns :math:`\log(|x|)`.

                                                            val sigmoid : float -> float

                                                            sigmoid x returns the logistic sigmoid function :math:`1 / (1 + \exp(-x))`.

                                                            val signum : float -> float

                                                            signum x returns the sign of :math:`x`: -1, 0 or 1.

                                                            val softsign : float -> float

                                                            Smoothed sign function.

                                                            val softplus : float -> float

                                                            softplus x returns :math:`\log(1 + \exp(x))`.

                                                            val relu : float -> float

                                                            relu x returns :math:`\max(0, x)`.

                                                            val sin : float -> float

                                                            sin x returns :math:`\sin(x)`.

                                                            val cos : float -> float

                                                            cos x returns :math:`\cos(x)`.

                                                            val tan : float -> float

                                                            tan x returns :math:`\tan(x)`.

                                                            val cot : float -> float

                                                            cot x returns :math:`1/\tan(x)`.

                                                            val sec : float -> float

                                                            sec x returns :math:`1/\cos(x)`.

                                                            val csc : float -> float

                                                            csc x returns :math:`1/\sin(x)`.

                                                            val asin : float -> float

                                                            asin x returns :math:`\arcsin(x)`.

                                                            val acos : float -> float

                                                            acos x returns :math:`\arccos(x)`.

                                                            val atan : float -> float

                                                            atan x returns :math:`\arctan(x)`.

                                                            val acot : float -> float

                                                            Inverse function of cot.

                                                            val asec : float -> float

                                                            Inverse function of sec.

                                                            val acsc : float -> float

                                                            Inverse function of csc.

                                                            val sinh : float -> float

                                                            Returns :math:`\sinh(x)`.

                                                            val cosh : float -> float

                                                            cosh x returns :math:`\cosh(x)`.

                                                            val tanh : float -> float

                                                            tanh x returns :math:`\tanh(x)`.

                                                            val coth : float -> float

                                                            coth x returns :math:`\coth(x)`.

                                                            val sech : float -> float

                                                            sech x returns :math:`1/\cosh(x)`.

                                                            val csch : float -> float

                                                            csch x returns :math:`1/\sinh(x)`.

                                                            val asinh : float -> float

                                                            Inverse function of sinh.

                                                            val acosh : float -> float

                                                            Inverse function of cosh.

                                                            val atanh : float -> float

                                                            Inverse function of tanh.

                                                            val acoth : float -> float

                                                            Inverse function of coth.

                                                            val asech : float -> float

                                                            Inverse function of sech.

                                                            val acsch : float -> float

                                                            Inverse function of csch.

                                                            val sinc : float -> float

                                                            sinc x returns :math:`\sin(x)/x` and :math:`1` for :math:`x=0`.

                                                            val logsinh : float -> float

                                                            logsinh x returns :math:`\log(\sinh(x))` but handles large :math:`|x|`.

                                                            val logcosh : float -> float

                                                            logcosh x returns :math:`\log(\cosh(x))` but handles large :math:`|x|`.

                                                            val sindg : float -> float

                                                            Sine of angle given in degrees.

                                                            val cosdg : float -> float

                                                            Cosine of the angle given in degrees.

                                                            val tandg : float -> float

                                                            Tangent of angle given in degrees.

                                                            val cotdg : float -> float

                                                            Cotangent of the angle given in degrees.

                                                            val hypot : float -> float -> float

                                                            hypot x y returns :math:`\sqrtx^2 + y^2`.

                                                            val xlogy : float -> float -> float

                                                            xlogy(x, y) returns :math:`x \log(y)`.

                                                            val xlog1py : float -> float -> float

                                                            xlog1py(x, y) returns :math:`x \log(y+1)`.

                                                            val logit : float -> float

                                                            logit(x) returns :math:`\logp/(1-p)`.

                                                            val expit : float -> float

                                                            expit(x) returns :math:`1/(1+\exp(-x))`.

                                                            val log1mexp : float -> float

                                                            log1mexp(x) returns :math:`log(1-exp(x))`.

                                                            val log1pexp : float -> float

                                                            log1pexp(x) returns :math:`log(1+exp(x))`.

                                                            Airy functions
                                                            val airy : float -> float * float * float * float

                                                            Airy function airy x returns (Ai, Ai', Bi, Bi') evaluated at :math:`x`. Ai' is the derivative of Ai whilst Bi' is the derivative of Bi.

                                                            Bessel functions
                                                            val j0 : float -> float

                                                            Bessel function of the first kind of order 0.

                                                            val j1 : float -> float

                                                            Bessel function of the first kind of order 1.

                                                            val jv : float -> float -> float

                                                            Bessel function of real order.

                                                            val y0 : float -> float

                                                            Bessel function of the second kind of order 0.

                                                            val y1 : float -> float

                                                            Bessel function of the second kind of order 1.

                                                            val yv : float -> float -> float

                                                            Bessel function of the second kind of real order.

                                                            val yn : int -> float -> float

                                                            Bessel function of the second kind of integer order.

                                                            val i0 : float -> float

                                                            Modified Bessel function of order 0.

                                                            val i0e : float -> float

                                                            Exponentially scaled modified Bessel function of order 0.

                                                            val i1 : float -> float

                                                            Modified Bessel function of order 1.

                                                            val i1e : float -> float

                                                            Exponentially scaled modified Bessel function of order 1.

                                                            val iv : float -> float -> float

                                                            Modified Bessel function of the first kind of real order.

                                                            val k0 : float -> float

                                                            Modified Bessel function of the second kind of order 0, :math:`K_0`.

                                                            val k0e : float -> float

                                                            Exponentially scaled modified Bessel function K of order 0.

                                                            val k1 : float -> float

                                                            Modified Bessel function of the second kind of order 1, :math:`K_1(x)`.

                                                            val k1e : float -> float

                                                            Exponentially scaled modified Bessel function K of order 1.

                                                            Elliptic functions
                                                            val ellipj : float -> float -> float * float * float * float

                                                            Jacobian Elliptic function ellipj u m returns (sn, cn, dn, phi).

                                                            val ellipk : float -> float

                                                            ellipk m returns the complete elliptic integral of the first kind.

                                                            val ellipkm1 : float -> float

                                                            FIXME. Complete elliptic integral of the first kind around :math:`m = 1`.

                                                            val ellipkinc : float -> float -> float

                                                            ellipkinc phi m incomplete elliptic integral of the first kind.

                                                            val ellipe : float -> float

                                                            ellipe m complete elliptic integral of the second kind.

                                                            val ellipeinc : float -> float -> float

                                                            ellipeinc phi m incomplete elliptic integral of the second kind.

                                                            Gamma Functions
                                                            val gamma : float -> float

                                                            gamma z returns the value of the Gamma function

                                                            .. math:: \Gamma(z) = \int_0^\infty x^z-1 e^

                                                            x

                                                            }

                                                            dx = (z - 1)! .

                                                            The gamma function is often referred to as the generalized factorial since :math:`z\ gamma(z) = \gamma(z+1)` and :math:`gamma(n+1) = n!` for natural number :math:`n`.

                                                            val rgamma : float -> float

                                                            Reciprocal Gamma function.

                                                            val loggamma : float -> float

                                                            Logarithm of the gamma function.

                                                            val gammainc : float -> float -> float

                                                            Incomplete gamma function.

                                                            val gammaincinv : float -> float -> float

                                                            Inverse function of gammainc.

                                                            val gammaincc : float -> float -> float

                                                            Complemented incomplete gamma integral.

                                                            val gammainccinv : float -> float -> float

                                                            Inverse function of gammaincc.

                                                            val psi : float -> float

                                                            The digamma function.

                                                            Beta functions
                                                            val beta : float -> float -> float

                                                            Beta function.

                                                            .. math:: \mathrmB(a, b) = \frac\Gamma(a) \Gamma(b)\Gamma(a+b)

                                                            val betainc : float -> float -> float -> float

                                                            Incomplete beta integral.

                                                            val betaincinv : float -> float -> float -> float

                                                            Inverse function of betainc.

                                                            Factorials
                                                            val fact : int -> float

                                                            Factorial function fact n calculates :math:`n!`.

                                                            val log_fact : int -> float

                                                            Logarithm of factorial function log_fact n calculates :math:`\log n!`.

                                                            val doublefact : int -> float

                                                            Double factorial function doublefact n calculates :math:`n!! = n(n-2)(n-4)\dots 2` or :math:`\dots 1`

                                                            val log_doublefact : int -> float

                                                            Logarithm of double factorial function.

                                                            val permutation : int -> int -> int

                                                            permutation n k returns the number :math:`n!/(n-k)!` of ordered subsets * of length :math:`k`, taken from a set of :math:`n` elements.

                                                            val permutation_float : int -> int -> float

                                                            permutation_float is like permutation but deals with larger range.

                                                            val combination : int -> int -> int

                                                            combination n k returns the number :math:`n!/(k!(n-k)!)` of subsets of k elements of a set of n elements. This is the binomial coefficient :math:`\binomnk`

                                                            val combination_float : int -> int -> float

                                                            combination_float is like combination but can deal with a larger range.

                                                            val log_combination : int -> int -> float

                                                            log_combination n k returns the logarithm of :math:`\binomnk`.

                                                            Error functions
                                                            val erf : float -> float

                                                            Error function. :math:`\int_

                                                            \infty

                                                            }

                                                            ^x \frac

                                                            \sqrt(2\pi) \exp(-(1/2) y^2) dy`

                                                            val erfc : float -> float

                                                            Complementary error function, :math:`\int^\infty_x \frac

                                                            \sqrt(2\pi) \exp(-(1/2) y^2) dy`

                                                            val erfcx : float -> float

                                                            Scaled complementary error function, :math:`\exp(x^2) \mathrmrfc(x)`.

                                                            val erfinv : float -> float

                                                            Inverse function of erf.

                                                            val erfcinv : float -> float

                                                            Inverse function of erfc.

                                                            Dawson & Fresnel integrals
                                                            val dawsn : float -> float

                                                            Dawson’s integral.

                                                            val fresnel : float -> float * float

                                                            Fresnel trigonometric integrals. fresnel x returns a tuple consisting of (Fresnel sin integral, Fresnel cos integral).

                                                            Struve functions
                                                            val struve : float -> float -> float

                                                            struve v x returns the value of the Struve function of order :math:`v` at :math:`x`. The Struve function is defined as,

                                                            .. math:: H_v(x) = (z/2)^

                                                            + 1} \sum_{n=0}^\infty \frac{(-1)^n (z/2)^{2n}}{\Gamma(n + \frac{3}{2}) \Gamma(n + v + \frac{3}{2})},
                                                            -
                                                            -where :math:`\Gamma` is the gamma function. :math:`x` must be positive unless :math:`v` is an integer
                                                            Other special functions
                                                            val expn : int -> float -> float

                                                            Exponential integral :math:`E_n`.

                                                            val shichi : float -> float * float

                                                            Hyperbolic sine and cosine integrals, shichi x returns * :math:`(\mathrmshi, \mathrmchi)``.

                                                            val shi : float -> float

                                                            Hyperbolic sine integral.

                                                            val chi : float -> float

                                                            Hyperbolic cosine integral.

                                                            val sici : float -> float * float

                                                            Sine and cosine integrals, sici x returns :math:`(\mathrmsi, \mathrmci)`.

                                                            val si : float -> float

                                                            Sine integral.

                                                            val ci : float -> float

                                                            Cosine integral.

                                                            val zeta : float -> float -> float

                                                            zeta x q returns the Hurwitz zeta function :math:`\zeta(x, q)`, which reduces to the Riemann zeta function :math:`\zeta(x)` when :math:`q=1`.

                                                            val zetac : float -> float

                                                            Riemann zeta function minus 1.

                                                            Raw statistical functions
                                                            val bdtr : int -> int -> float -> float

                                                            Binomial distribution cumulative distribution function.

                                                            bdtr k n p calculates the sum of the terms :math:`0` through :math:`k` of the Binomial probability density.

                                                            .. math:: \mathrmdtr(k, n, p) = \sum_j=0^k {n\choosej

                                                            }

                                                            p^j (1-p)^n-j

                                                            Parameters: * k: Number of successes. * n: Number of events. * p: Probability of success in a single event.

                                                            Returns: * Probability of :math:`k` or fewer successes in :math:`n` independent events with success probability :math:`p`.

                                                            val bdtrc : int -> int -> float -> float

                                                            Binomial distribution survival function.

                                                            bdtrc k n p calculates the sum of the terms :math:`k + 1` through :math:`n` of the binomial probability density,

                                                            .. math:: \mathrmdtrc(k, n, p) = \sum_j=k+1^n {n\choosej

                                                            }

                                                            p^j (1-p)^n-j

                                                            val bdtri : int -> int -> float -> float

                                                            Inverse function to bdtr with respect to :math:`p`.

                                                            Finds the event probability :math:`p` such that the sum of the terms 0 through :math:`k` of the binomial probability density is equal to the given cumulative probability :math:`y`.

                                                            val btdtr : float -> float -> float -> float

                                                            Cumulative density function of the beta distribution.

                                                            btdtr a b x returns the integral from 0 to :math:`x` of the beta probability density function,

                                                            .. math:: I = \int_0^x \frac\Gamma(a + b)\Gamma(a)\Gamma(b) t^a-1 (1-t)^-1\,dt

                                                            where :math:`\Gamma` is the gamma function.

                                                            Parameters: * a: Shape parameter (:math:`a > 0`). * b: Shape parameter (:math:`a > 0`). * x: Upper limit of integration, in :math:`0, 1`.

                                                            Returns: * Cumulative density function of the beta distribution with :math:`a` and :math:`b` at :math:`x`.

                                                            val btdtri : float -> float -> float -> float

                                                            The :math:`p`-th quantile of the Beta distribution.

                                                            This function is the inverse of the beta cumulative distribution function, btdtr, returning the value of :math:`x` for which :math:`\mathrmtdtr(a, b, x) = p`,

                                                            .. math:: p = \int_0^x \frac\Gamma(a + b)\Gamma(a)\Gamma(b) t^a-1 (1-t)^-1\,dt

                                                            where :math:`\Gamma` is the gamma function.

                                                            Parameters: * a: Shape parameter (:math:`a > 0`). * b: Shape parameter (:math:`a > 0`). * x: Cumulative probability, in :math:`0, 1`.

                                                            Returns: * The quantile corresponding to :math:`p`.

                                                            Helper functions
                                                            val is_nan : float -> bool

                                                            is_nan x returns true exactly if x is nan.

                                                            val is_inf : float -> bool

                                                            is_inf x returns true exactly if x is infinity or neg_infinity.

                                                            val is_normal : float -> bool

                                                            is_normal x returns true if x is a normal float number.

                                                            val is_subnormal : float -> bool

                                                            is_nan x returns true if x is subnormal float number.

                                                            val is_odd : int -> bool

                                                            is_odd x returns true exactly if x is odd.

                                                            val is_even : int -> bool

                                                            is_even x returns true exactly if x is even.

                                                            val is_pow2 : int -> bool

                                                            is_pow2 x return true exactly if x is an integer power of 2, e.g. 32, 64, etc.

                                                            val same_sign : float -> float -> bool

                                                            same_sign x y returns true if x and y have the same sign, otherwise it returns false. Positive and negative zeros are special cases and always returns true.

                                                            val is_simplex : float array -> bool

                                                            is_simplex x checks whether the vector :math:`x` lies on a simplex. In other words, :math:`\sum_i^K x_i = 1` and :math:`x_i \ge 0, \forall i \in 1,K`, where :math:`K` is the dimension of :math:`x`.

                                                            val is_int : float -> bool
                                                            val is_sqr : int -> bool

                                                            is_sqr x checks if x is the square of an integer.

                                                            val mulmod : int -> int -> int -> int

                                                            mulmod a b m computes (a*b) mod m.

                                                            val powmod : int -> int -> int -> int

                                                            powmod a b m computes (a^b) mod m.

                                                            val is_prime : int -> bool

                                                            is_prime x returns true if x is a prime number. The function is deterministic for all numbers representable by an int. The function uses the Rabin–Miller primality test.

                                                            val fermat_fact : int -> int * int

                                                            fermat_fact x performs Fermat factorisation over x, i.e. into two roughly equal factors. x must be an odd number.

                                                            val nextafter : float -> float -> float

                                                            nextafter from to returns the next representable double precision value of from in the direction of to. If from equals to, this value is returned.

                                                            val nextafterf : float -> float -> float

                                                            nextafter from to returns the next representable single precision value of from in the direction of to. If from equals to, this value is returned.

                                                            +

                                                            Module Owl_maths

                                                            Maths: fundamental and advanced mathematical functions.

                                                            This module contains some basic and advanced mathematical operations. If you cannot find some function in this module, try Stats module.

                                                            Please refer to Scipy documentation.

                                                            Basic functions
                                                            val add : float -> float -> float

                                                            add x y returns x + y.

                                                            val sub : float -> float -> float

                                                            sub x y returns x - y.

                                                            val mul : float -> float -> float

                                                            mul x y returns x * y.

                                                            val div : float -> float -> float

                                                            div x y returns x / y.

                                                            val fmod : float -> float -> float

                                                            fmod x y returns x % y.

                                                            val atan2 : float -> float -> float

                                                            atan2 y x returns \arctan(y/x), accounting for the sign of the arguments; this is the angle to the vector (x, y) counting from the x-axis.

                                                            val abs : float -> float

                                                            abs x returns =|x|.

                                                            val neg : float -> float

                                                            neg x returns -x.

                                                            val reci : float -> float

                                                            reci x returns 1/x.

                                                            val floor : float -> float

                                                            floor x returns the largest integer \leq x.

                                                            val ceil : float -> float

                                                            ceil x returns the smallest integer \geq x.

                                                            val round : float -> float

                                                            round x rounds, towards the bigger integer when on the fence.

                                                            val trunc : float -> float

                                                            trunc x integer part.

                                                            val sqr : float -> float

                                                            sqr x square.

                                                            val sqrt : float -> float

                                                            sqrt x square root.

                                                            val pow : float -> float -> float

                                                            pow x y returns x^y.

                                                            val exp : float -> float

                                                            exp x exponential.

                                                            val exp2 : float -> float

                                                            exp2 x exponential.

                                                            val exp10 : float -> float

                                                            exp10 x exponential.

                                                            val expm1 : float -> float

                                                            expm1 x returns \exp(x) - 1 but more accurate for x \sim 0.

                                                            val log : float -> float

                                                            log x natural logarithm

                                                            val log2 : float -> float

                                                            log2 x base-2 logarithm.

                                                            val log10 : float -> float

                                                            log10 x base-10 logarithm.

                                                            val logn : float -> float -> float

                                                            logn x base-n logarithm.

                                                            val log1p : float -> float

                                                            log1p x returns \log (x + 1) but more accurate for x \sim 0. Inverse of expm1.

                                                            val logabs : float -> float

                                                            logabs x returns \log(|x|).

                                                            val sigmoid : float -> float

                                                            sigmoid x returns the logistic sigmoid function 1 / (1 + \exp(-x)).

                                                            val signum : float -> float

                                                            signum x returns the sign of x -1, 0 or 1.

                                                            val softsign : float -> float

                                                            Smoothed sign function.

                                                            val softplus : float -> float

                                                            softplus x returns \log(1 + \exp(x)).

                                                            val relu : float -> float

                                                            relu x returns \max(0, x).

                                                            val sin : float -> float

                                                            sin x returns \sin(x).

                                                            val cos : float -> float

                                                            cos x returns \cos(x).

                                                            val tan : float -> float

                                                            tan x returns \tan(x).

                                                            val cot : float -> float

                                                            cot x returns 1/\tan(x).

                                                            val sec : float -> float

                                                            sec x returns 1/\cos(x).

                                                            val csc : float -> float

                                                            csc x returns 1/\sin(x).

                                                            val asin : float -> float

                                                            asin x returns \arcsin(x).

                                                            val acos : float -> float

                                                            acos x returns \arccos(x).

                                                            val atan : float -> float
                                                            val acot : float -> float

                                                            Inverse function of cot.

                                                            val asec : float -> float

                                                            Inverse function of sec.

                                                            val acsc : float -> float

                                                            Inverse function of csc.

                                                            val sinh : float -> float

                                                            Returns \sinh(x).

                                                            val cosh : float -> float

                                                            cosh x returns \cosh(x).

                                                            val tanh : float -> float

                                                            tanh x returns \tanh(x).

                                                            val coth : float -> float

                                                            coth x returns \coth(x).

                                                            val sech : float -> float

                                                            sech x returns 1/\cosh(x).

                                                            val csch : float -> float

                                                            csch x returns 1/\sinh(x).

                                                            val asinh : float -> float

                                                            Inverse function of sinh.

                                                            val acosh : float -> float

                                                            Inverse function of cosh.

                                                            val atanh : float -> float

                                                            Inverse function of tanh.

                                                            val acoth : float -> float

                                                            Inverse function of coth.

                                                            val asech : float -> float

                                                            Inverse function of sech.

                                                            val acsch : float -> float

                                                            Inverse function of csch.

                                                            val sinc : float -> float

                                                            sinc x returns \sin(x)/x and 1 for x=0.

                                                            val logsinh : float -> float

                                                            logsinh x returns \log(\sinh(x)) but handles large |x|.

                                                            val logcosh : float -> float

                                                            logcosh x returns \log(\cosh(x)) but handles large |x|.

                                                            val sindg : float -> float

                                                            Sine of angle given in degrees.

                                                            val cosdg : float -> float

                                                            Cosine of the angle given in degrees.

                                                            val tandg : float -> float

                                                            Tangent of angle given in degrees.

                                                            val cotdg : float -> float

                                                            Cotangent of the angle given in degrees.

                                                            val hypot : float -> float -> float

                                                            hypot x y returns \sqrt{x^2 + y^2}.

                                                            val xlogy : float -> float -> float

                                                            xlogy(x, y) returns x \log(y).

                                                            val xlog1py : float -> float -> float

                                                            xlog1py(x, y) returns x \log(y+1).

                                                            val logit : float -> float

                                                            logit(x) returns \log\left(\frac{p}{1-p}\right).

                                                            val expit : float -> float

                                                            expit(x) returns \frac{1}{1+\exp(-x)}.

                                                            val log1mexp : float -> float

                                                            log1mexp(x) returns \log(1-\exp(x)).

                                                            val log1pexp : float -> float

                                                            log1pexp(x) returns \log(1+\exp(x)).

                                                            Airy functions
                                                            val airy : float -> float * float * float * float

                                                            Airy function airy x returns (Ai, Ai', Bi, Bi') evaluated at x. Ai' is the derivative of Ai whilst Bi' is the derivative of Bi.

                                                            Bessel functions
                                                            val j0 : float -> float

                                                            Bessel function of the first kind of order 0.

                                                            val j1 : float -> float

                                                            Bessel function of the first kind of order 1.

                                                            val jv : float -> float -> float

                                                            Bessel function of real order.

                                                            val y0 : float -> float

                                                            Bessel function of the second kind of order 0.

                                                            val y1 : float -> float

                                                            Bessel function of the second kind of order 1.

                                                            val yv : float -> float -> float

                                                            Bessel function of the second kind of real order.

                                                            val yn : int -> float -> float

                                                            Bessel function of the second kind of integer order.

                                                            val i0 : float -> float

                                                            Modified Bessel function of order 0.

                                                            val i0e : float -> float

                                                            Exponentially scaled modified Bessel function of order 0.

                                                            val i1 : float -> float

                                                            Modified Bessel function of order 1.

                                                            val i1e : float -> float

                                                            Exponentially scaled modified Bessel function of order 1.

                                                            val iv : float -> float -> float

                                                            Modified Bessel function of the first kind of real order.

                                                            val k0 : float -> float

                                                            Modified Bessel function of the second kind of order 0, K_0.

                                                            val k0e : float -> float

                                                            Exponentially scaled modified Bessel function K of order 0.

                                                            val k1 : float -> float

                                                            Modified Bessel function of the second kind of order 1, K_1(x).

                                                            val k1e : float -> float

                                                            Exponentially scaled modified Bessel function K of order 1.

                                                            Elliptic functions
                                                            val ellipj : float -> float -> float * float * float * float

                                                            Jacobian Elliptic function ellipj u m returns (sn, cn, dn, phi).

                                                            val ellipk : float -> float

                                                            ellipk m returns the complete elliptic integral of the first kind.

                                                            val ellipkm1 : float -> float

                                                            FIXME. Complete elliptic integral of the first kind around m = 1.

                                                            val ellipkinc : float -> float -> float

                                                            ellipkinc phi m incomplete elliptic integral of the first kind.

                                                            val ellipe : float -> float

                                                            ellipe m complete elliptic integral of the second kind.

                                                            val ellipeinc : float -> float -> float

                                                            ellipeinc phi m incomplete elliptic integral of the second kind.

                                                            Gamma Functions
                                                            val gamma : float -> float

                                                            gamma z returns the value of the Gamma function

                                                            \Gamma(z) = \int_0^\infty x^{z-1} e^{-x} dx = (z - 1)!.

                                                            The gamma function is often referred to as the generalized factorial since z\gamma(z) = \gamma(z+1) and \gamma(n+1) = n! for natural number n.

                                                            val rgamma : float -> float

                                                            Reciprocal Gamma function.

                                                            val loggamma : float -> float

                                                            Logarithm of the gamma function.

                                                            val gammainc : float -> float -> float

                                                            Incomplete gamma function.

                                                            val gammaincinv : float -> float -> float

                                                            Inverse function of gammainc.

                                                            val gammaincc : float -> float -> float

                                                            Complemented incomplete gamma integral.

                                                            val gammainccinv : float -> float -> float

                                                            Inverse function of gammaincc.

                                                            val psi : float -> float

                                                            The digamma function.

                                                            Beta functions
                                                            val beta : float -> float -> float

                                                            Beta function.

                                                              \mathrm{B}(a, b) =  \frac{\Gamma(a) \Gamma(b)}{\Gamma(a+b)}
                                                            val betainc : float -> float -> float -> float

                                                            Incomplete beta integral.

                                                            val betaincinv : float -> float -> float -> float

                                                            Inverse function of betainc.

                                                            Factorials
                                                            val fact : int -> float

                                                            Factorial function fact n calculates n!.

                                                            val log_fact : int -> float

                                                            Logarithm of factorial function log_fact n calculates \log n!.

                                                            val doublefact : int -> float

                                                            Double factorial function doublefact n calculates n!! = n(n-2)(n-4)\dots 2 or \dots 1.

                                                            val log_doublefact : int -> float

                                                            Logarithm of double factorial function.

                                                            val permutation : int -> int -> int

                                                            permutation n k returns the number \frac{n!}{(n-k)!} of ordered subsets of length k, taken from a set of n elements.

                                                            val permutation_float : int -> int -> float

                                                            permutation_float is like permutation but deals with larger range.

                                                            val combination : int -> int -> int

                                                            combination n k returns the number n!/(k!(n-k)!) of subsets of k elements of a set of n elements. This is the binomial coefficient \binom{n}{k}

                                                            val combination_float : int -> int -> float

                                                            combination_float is like combination but can deal with a larger range.

                                                            val log_combination : int -> int -> float

                                                            log_combination n k returns the logarithm of \binom{n}{k}.

                                                            Error functions
                                                            val erf : float -> float

                                                            Error function. \int_{-\infty}^x \frac{1}{\sqrt(2\pi)} \exp(-(1/2) y^2) dy

                                                            val erfc : float -> float

                                                            Complementary error function, \int^{\infty}_x \frac{1}{\sqrt(2\pi)} \exp(-(1/2) y^2) dy

                                                            val erfcx : float -> float

                                                            Scaled complementary error function, \exp(x^2) \mathrm{erfc}(x).

                                                            val erfinv : float -> float

                                                            Inverse function of erf.

                                                            val erfcinv : float -> float

                                                            Inverse function of erfc.

                                                            Dawson & Fresnel integrals
                                                            val dawsn : float -> float

                                                            Dawson’s integral.

                                                            val fresnel : float -> float * float

                                                            Fresnel trigonometric integrals. fresnel x returns a tuple consisting of (Fresnel sin integral, Fresnel cos integral).

                                                            Struve functions
                                                            val struve : float -> float -> float

                                                            struve v x returns the value of the Struve function of order v at x. The Struve function is defined as,

                                                             H_v(x) = (z/2)^{v + 1}\sum_{n=0}^\infty \frac{(-1)^n (z/2)^{2n}}{\Gamma(n + \frac{3}{2})\Gamma(n + v + \frac{3}{2})}

                                                            where \Gamma is the gamma function. x must be positive unless v is an integer

                                                            Other special functions
                                                            val expn : int -> float -> float

                                                            Exponential integral E_n.

                                                            val shichi : float -> float * float

                                                            Hyperbolic sine and cosine integrals, shichi x returns * (\mathrm{shi}, \mathrm{chi}).

                                                            val shi : float -> float

                                                            Hyperbolic sine integral.

                                                            val chi : float -> float

                                                            Hyperbolic cosine integral.

                                                            val sici : float -> float * float

                                                            Sine and cosine integrals, sici x returns (\mathrm{si}, \mathrm{ci}).

                                                            val si : float -> float

                                                            Sine integral.

                                                            val ci : float -> float

                                                            Cosine integral.

                                                            val zeta : float -> float -> float

                                                            zeta x q returns the Hurwitz zeta function \zeta(x, q), which reduces to the Riemann zeta function \zeta(x) when q=1.

                                                            val zetac : float -> float

                                                            Riemann zeta function minus 1.

                                                            Raw statistical functions
                                                            val bdtr : int -> int -> float -> float

                                                            Binomial distribution cumulative distribution function.

                                                            bdtr k n p calculates the sum of the terms 0 through k of the Binomial probability density.

                                                            \mathrm{bdtr}(k, n, p) = \sum_{j=0}^k \binom{n}{j} p^j (1-p)^{n-j}

                                                            Parameters: * k: Number of successes. * n: Number of events. * p: Probability of success in a single event.

                                                            Returns: * Probability of k or fewer successes in n independent events with success probability p.

                                                            val bdtrc : int -> int -> float -> float

                                                            Binomial distribution survival function.

                                                            bdtrc k n p calculates the sum of the terms k + 1 through n of the binomial probability density,

                                                            \mathrm{bdtrc}(k, n, p) = \sum_{j=k+1}^n \binom{n}{j} p^j (1-p)^{n-j}

                                                            val bdtri : int -> int -> float -> float

                                                            Inverse function to bdtr with respect to p.

                                                            Finds the event probability p such that the sum of the terms 0 through k of the binomial probability density is equal to the given cumulative probability y.

                                                            val btdtr : float -> float -> float -> float

                                                            Cumulative density function of the beta distribution.

                                                            btdtr a b x returns the integral from 0 to x of the beta probability density function,

                                                            I = \int_0^x \frac{\Gamma(a + b)}{\Gamma(a)\Gamma(b)} t^{a-1} (1-t)^{b-1}\,dt

                                                            where \Gamma is the gamma function.

                                                            Parameters: * a: Shape parameter (a > 0). * b: Shape parameter (b > 0). * x: Upper limit of integration, in [0, 1].

                                                            Returns: * Cumulative density function of the beta distribution with a and b at x.

                                                            val btdtri : float -> float -> float -> float

                                                            The p-th quantile of the Beta distribution.

                                                            This function is the inverse of the beta cumulative distribution function, btdtr, returning the value of x for which \mathrm{btdtr}(a, b, x) = p,

                                                            +  p = \int_0^x \frac{\Gamma(a + b)}{\Gamma(a)\Gamma(b)} t^{a-1} (1-t)^{b-1}\,dt

                                                            where \Gamma is the gamma function.

                                                            Parameters: * a: Shape parameter (a > 0). * b: Shape parameter (b > 0). * x: Cumulative probability, in [0, 1].

                                                            Returns: * The quantile corresponding to p.

                                                            Helper functions
                                                            val is_nan : float -> bool

                                                            is_nan x returns true exactly if x is nan.

                                                            val is_inf : float -> bool

                                                            is_inf x returns true exactly if x is infinity or neg_infinity.

                                                            val is_normal : float -> bool

                                                            is_normal x returns true if x is a normal float number.

                                                            val is_subnormal : float -> bool

                                                            is_nan x returns true if x is subnormal float number.

                                                            val is_odd : int -> bool

                                                            is_odd x returns true exactly if x is odd.

                                                            val is_even : int -> bool

                                                            is_even x returns true exactly if x is even.

                                                            val is_pow2 : int -> bool

                                                            is_pow2 x return true exactly if x is an integer power of 2, e.g. 32, 64, etc.

                                                            val same_sign : float -> float -> bool

                                                            same_sign x y returns true if x and y have the same sign, otherwise it returns false. Positive and negative zeros are special cases and always returns true.

                                                            val is_simplex : float array -> bool

                                                            is_simplex x checks whether the vector x lies on a simplex. In other words, \sum_i^K x_i = 1 and x_i\ge~0,\forall~i\in~[1,K], where K is the dimension of x.

                                                            val is_int : float -> bool
                                                            val is_sqr : int -> bool

                                                            is_sqr x checks if x is the square of an integer.

                                                            val mulmod : int -> int -> int -> int

                                                            mulmod a b m computes (a*b) mod m.

                                                            val powmod : int -> int -> int -> int

                                                            powmod a b m computes (a^b) mod m.

                                                            val is_prime : int -> bool

                                                            is_prime x returns true if x is a prime number. The function is deterministic for all numbers representable by an int. The function uses the Rabin–Miller primality test.

                                                            val fermat_fact : int -> int * int

                                                            fermat_fact x performs Fermat factorisation over x, i.e. into two roughly equal factors. x must be an odd number.

                                                            val nextafter : float -> float -> float

                                                            nextafter from to returns the next representable double precision value of from in the direction of to. If from equals to, this value is returned.

                                                            val nextafterf : float -> float -> float

                                                            nextafter from to returns the next representable single precision value of from in the direction of to. If from equals to, this value is returned.

                                                            diff --git a/docs/owl/Owl_maths_special/index.html b/docs/owl/Owl_maths_special/index.html index 90eec68c4..66ac50a9b 100644 --- a/docs/owl/Owl_maths_special/index.html +++ b/docs/owl/Owl_maths_special/index.html @@ -1,5 +1,5 @@ -Owl_maths_special (owl.Owl_maths_special)

                                                            Module Owl_maths_special

                                                            module CI = Cstubs_internals
                                                            val airy : +Owl_maths_special (owl.Owl_maths_special)

                                                            Module Owl_maths_special

                                                            module CI = Cstubs_internals
                                                            val airy : float -> ('a, [ `C ]) CI.pointer -> ('b, [ `C ]) CI.pointer -> diff --git a/docs/owl/Owl_matrix/index.html b/docs/owl/Owl_matrix/index.html index b46861f73..0a90e8477 100644 --- a/docs/owl/Owl_matrix/index.html +++ b/docs/owl/Owl_matrix/index.html @@ -1,5 +1,5 @@ -Owl_matrix (owl.Owl_matrix)

                                                            Module Owl_matrix

                                                            include module type of struct include Owl_matrix_check end
                                                            val owl_float32_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_float64_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex32_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex64_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val _matrix_is_triu : +Owl_matrix (owl.Owl_matrix)

                                                            Module Owl_matrix

                                                            include module type of struct include Owl_matrix_check end
                                                            val owl_float32_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_float64_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex32_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex64_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val _matrix_is_triu : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_float32_matrix_is_tril : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_float64_matrix_is_tril : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex32_matrix_is_tril : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex64_matrix_is_tril : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val _matrix_is_tril : diff --git a/docs/owl/Owl_matrix_check/index.html b/docs/owl/Owl_matrix_check/index.html index 227dcf197..597c0836f 100644 --- a/docs/owl/Owl_matrix_check/index.html +++ b/docs/owl/Owl_matrix_check/index.html @@ -1,5 +1,5 @@ -Owl_matrix_check (owl.Owl_matrix_check)

                                                            Module Owl_matrix_check

                                                            val owl_float32_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_float64_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex32_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex64_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val _matrix_is_triu : +Owl_matrix_check (owl.Owl_matrix_check)

                                                            Module Owl_matrix_check

                                                            val owl_float32_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_float64_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex32_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex64_matrix_is_triu : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val _matrix_is_triu : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_float32_matrix_is_tril : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_float64_matrix_is_tril : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex32_matrix_is_tril : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val owl_complex64_matrix_is_tril : ('a, 'b) Owl_core_types.owl_arr -> bool
                                                            val _matrix_is_tril : diff --git a/docs/owl/Owl_matrix_swap/index.html b/docs/owl/Owl_matrix_swap/index.html index 950e2adde..ee66f60e3 100644 --- a/docs/owl/Owl_matrix_swap/index.html +++ b/docs/owl/Owl_matrix_swap/index.html @@ -1,5 +1,5 @@ -Owl_matrix_swap (owl.Owl_matrix_swap)

                                                            Module Owl_matrix_swap

                                                            val owl_float32_matrix_swap_rows : +Owl_matrix_swap (owl.Owl_matrix_swap)

                                                            Module Owl_matrix_swap

                                                            val owl_float32_matrix_swap_rows : ('a, 'b) Owl_core_types.owl_arr -> int -> int -> diff --git a/docs/owl/Owl_ndarray/index.html b/docs/owl/Owl_ndarray/index.html index 01c0fd3b7..eea9db367 100644 --- a/docs/owl/Owl_ndarray/index.html +++ b/docs/owl/Owl_ndarray/index.html @@ -1,5 +1,5 @@ -Owl_ndarray (owl.Owl_ndarray)

                                                            Module Owl_ndarray

                                                            Tensor operations implementation in Owl's Ndarray

                                                            include module type of struct include Owl_ndarray_maths end
                                                            val _owl_uniform_fun : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
                                                            val _owl_gaussian_fun : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
                                                            val owl_float32_copy : +Owl_ndarray (owl.Owl_ndarray)

                                                            Module Owl_ndarray

                                                            Tensor operations implementation in Owl's Ndarray

                                                            include module type of struct include Owl_ndarray_maths end
                                                            val _owl_uniform_fun : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
                                                            val _owl_gaussian_fun : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
                                                            val owl_float32_copy : int -> ('a, 'b) Owl_core_types.owl_arr -> int -> diff --git a/docs/owl/Owl_ndarray_contract/index.html b/docs/owl/Owl_ndarray_contract/index.html index a06309098..4fc9ac101 100644 --- a/docs/owl/Owl_ndarray_contract/index.html +++ b/docs/owl/Owl_ndarray_contract/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_contract (owl.Owl_ndarray_contract)

                                                            Module Owl_ndarray_contract

                                                            val owl_float32_ndarray_contract_one : +Owl_ndarray_contract (owl.Owl_ndarray_contract)

                                                            Module Owl_ndarray_contract

                                                            val owl_float32_ndarray_contract_one : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> (int64, Stdlib.Bigarray.int64_elt) Owl_core_types.owl_arr -> diff --git a/docs/owl/Owl_ndarray_conv/index.html b/docs/owl/Owl_ndarray_conv/index.html index 9c9f211c8..96692dcf6 100644 --- a/docs/owl/Owl_ndarray_conv/index.html +++ b/docs/owl/Owl_ndarray_conv/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_conv (owl.Owl_ndarray_conv)

                                                            Module Owl_ndarray_conv

                                                            val owl_float32_ndarray_conv_spatial : +Owl_ndarray_conv (owl.Owl_ndarray_conv)

                                                            Module Owl_ndarray_conv

                                                            val owl_float32_ndarray_conv_spatial : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> diff --git a/docs/owl/Owl_ndarray_fma/index.html b/docs/owl/Owl_ndarray_fma/index.html index 81ab7562b..dabb98c92 100644 --- a/docs/owl/Owl_ndarray_fma/index.html +++ b/docs/owl/Owl_ndarray_fma/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_fma (owl.Owl_ndarray_fma)

                                                            Module Owl_ndarray_fma

                                                            val owl_float32_ndarray_fma : +Owl_ndarray_fma (owl.Owl_ndarray_fma)

                                                            Module Owl_ndarray_fma

                                                            val owl_float32_ndarray_fma : int -> ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> diff --git a/docs/owl/Owl_ndarray_maths/index.html b/docs/owl/Owl_ndarray_maths/index.html index b18099c5a..dc3868333 100644 --- a/docs/owl/Owl_ndarray_maths/index.html +++ b/docs/owl/Owl_ndarray_maths/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_maths (owl.Owl_ndarray_maths)

                                                            Module Owl_ndarray_maths

                                                            val _owl_uniform_fun : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
                                                            val _owl_gaussian_fun : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
                                                            val owl_float32_copy : +Owl_ndarray_maths (owl.Owl_ndarray_maths)

                                                            Module Owl_ndarray_maths

                                                            val _owl_uniform_fun : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
                                                            val _owl_gaussian_fun : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> float -> 'a
                                                            val owl_float32_copy : int -> ('a, 'b) Owl_core_types.owl_arr -> int -> diff --git a/docs/owl/Owl_ndarray_pool/index.html b/docs/owl/Owl_ndarray_pool/index.html index e54a25d95..3a2493b51 100644 --- a/docs/owl/Owl_ndarray_pool/index.html +++ b/docs/owl/Owl_ndarray_pool/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_pool (owl.Owl_ndarray_pool)

                                                            Module Owl_ndarray_pool

                                                            val owl_float32_ndarray_maxpool_spatial : +Owl_ndarray_pool (owl.Owl_ndarray_pool)

                                                            Module Owl_ndarray_pool

                                                            val owl_float32_ndarray_maxpool_spatial : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> int -> diff --git a/docs/owl/Owl_ndarray_repeat/index.html b/docs/owl/Owl_ndarray_repeat/index.html index 56aa3ead2..303d3fa0c 100644 --- a/docs/owl/Owl_ndarray_repeat/index.html +++ b/docs/owl/Owl_ndarray_repeat/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_repeat (owl.Owl_ndarray_repeat)

                                                            Module Owl_ndarray_repeat

                                                            val owl_float32_ndarray_repeat : +Owl_ndarray_repeat (owl.Owl_ndarray_repeat)

                                                            Module Owl_ndarray_repeat

                                                            val owl_float32_ndarray_repeat : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> (int64, 'c) Owl_core_types.owl_arr -> diff --git a/docs/owl/Owl_ndarray_slide/index.html b/docs/owl/Owl_ndarray_slide/index.html index 048c53105..9ca72a823 100644 --- a/docs/owl/Owl_ndarray_slide/index.html +++ b/docs/owl/Owl_ndarray_slide/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_slide (owl.Owl_ndarray_slide)

                                                            Module Owl_ndarray_slide

                                                            val owl_float32_ndarray_slide : +Owl_ndarray_slide (owl.Owl_ndarray_slide)

                                                            Module Owl_ndarray_slide

                                                            val owl_float32_ndarray_slide : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> int -> diff --git a/docs/owl/Owl_ndarray_sort/index.html b/docs/owl/Owl_ndarray_sort/index.html index 8347ba8cf..c33107662 100644 --- a/docs/owl/Owl_ndarray_sort/index.html +++ b/docs/owl/Owl_ndarray_sort/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_sort (owl.Owl_ndarray_sort)

                                                            Module Owl_ndarray_sort

                                                            val owl_float32_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_float64_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_complex32_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_complex64_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_int8_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_uint8_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_int16_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_uint16_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_int32_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_int64_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val _owl_sort : +Owl_ndarray_sort (owl.Owl_ndarray_sort)

                                                            Module Owl_ndarray_sort

                                                            val owl_float32_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_float64_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_complex32_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_complex64_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_int8_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_uint8_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_int16_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_uint16_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_int32_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val owl_int64_sort : int -> ('a, 'b) Owl_core_types.owl_arr -> unit
                                                            val _owl_sort : 'a 'b. ('a, 'b) Stdlib.Bigarray.kind -> int -> ('a, 'b) Owl_core_types.owl_arr -> diff --git a/docs/owl/Owl_ndarray_transpose/index.html b/docs/owl/Owl_ndarray_transpose/index.html index b6c75ba6b..34ae779ef 100644 --- a/docs/owl/Owl_ndarray_transpose/index.html +++ b/docs/owl/Owl_ndarray_transpose/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_transpose (owl.Owl_ndarray_transpose)

                                                            Module Owl_ndarray_transpose

                                                            val owl_float32_ndarray_transpose : +Owl_ndarray_transpose (owl.Owl_ndarray_transpose)

                                                            Module Owl_ndarray_transpose

                                                            val owl_float32_ndarray_transpose : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> (int64, Stdlib.Bigarray.int64_elt) Owl_core_types.owl_arr -> diff --git a/docs/owl/Owl_ndarray_upsampling/index.html b/docs/owl/Owl_ndarray_upsampling/index.html index 3331df313..39d6d825d 100644 --- a/docs/owl/Owl_ndarray_upsampling/index.html +++ b/docs/owl/Owl_ndarray_upsampling/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_upsampling (owl.Owl_ndarray_upsampling)

                                                            Module Owl_ndarray_upsampling

                                                            val owl_float32_ndarray_upsampling_spatial_backward : +Owl_ndarray_upsampling (owl.Owl_ndarray_upsampling)

                                                            Module Owl_ndarray_upsampling

                                                            val owl_float32_ndarray_upsampling_spatial_backward : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> int -> diff --git a/docs/owl/Owl_ndarray_utils/index.html b/docs/owl/Owl_ndarray_utils/index.html index 684a0733d..1bd963b40 100644 --- a/docs/owl/Owl_ndarray_utils/index.html +++ b/docs/owl/Owl_ndarray_utils/index.html @@ -1,5 +1,5 @@ -Owl_ndarray_utils (owl.Owl_ndarray_utils)

                                                            Module Owl_ndarray_utils

                                                            val owl_ndarray_same_data : +Owl_ndarray_utils (owl.Owl_ndarray_utils)

                                                            Module Owl_ndarray_utils

                                                            val owl_ndarray_same_data : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> int
                                                            val _owl_ndarray_same_data : diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Activation/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Activation/index.html index 53f25367d..4fca4388a 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Activation/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Activation/index.html @@ -1,5 +1,5 @@ -Activation (owl.Owl_neural.D.Graph.Neuron.Activation)

                                                            Module Neuron.Activation

                                                            type typ = +Activation (owl.Owl_neural.D.Graph.Neuron.Activation)

                                                            Module Neuron.Activation

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Activation.typ =
                                                            1. | Elu
                                                            2. | Relu
                                                            3. | Sigmoid
                                                            4. | HardSigmoid
                                                            5. | Softmax of int
                                                            6. | Softplus
                                                            7. | Softsign
                                                            8. | Tanh
                                                            9. | Relu6
                                                            10. | LeakyRelu of float
                                                            11. | TRelu of float
                                                            12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                            13. | None
                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Activation.neuron_typ = diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Add/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Add/index.html index 78ad08b1e..865220a80 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Add/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Add/index.html @@ -1,4 +1,4 @@ -Add (owl.Owl_neural.D.Graph.Neuron.Add)

                                                            Module Neuron.Add

                                                            type neuron_typ = +Add (owl.Owl_neural.D.Graph.Neuron.Add)

                                                            Module Neuron.Add

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Add.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/AlphaDropout/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/AlphaDropout/index.html index 9c74041e9..425f50d41 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/AlphaDropout/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/AlphaDropout/index.html @@ -1,4 +1,4 @@ -AlphaDropout (owl.Owl_neural.D.Graph.Neuron.AlphaDropout)

                                                            Module Neuron.AlphaDropout

                                                            type neuron_typ = +AlphaDropout (owl.Owl_neural.D.Graph.Neuron.AlphaDropout)

                                                            Module Neuron.AlphaDropout

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.AlphaDropout.neuron_typ = {
                                                            1. mutable rate : float;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : float -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Average/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Average/index.html index e3deb9d03..3cfd46845 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Average/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Average/index.html @@ -1,4 +1,4 @@ -Average (owl.Owl_neural.D.Graph.Neuron.Average)

                                                            Module Neuron.Average

                                                            type neuron_typ = +Average (owl.Owl_neural.D.Graph.Neuron.Average)

                                                            Module Neuron.Average

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Average.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/AvgPool1D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/AvgPool1D/index.html index c8a5e2789..4a5baa4b9 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/AvgPool1D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/AvgPool1D/index.html @@ -1,4 +1,4 @@ -AvgPool1D (owl.Owl_neural.D.Graph.Neuron.AvgPool1D)

                                                            Module Neuron.AvgPool1D

                                                            type neuron_typ = +AvgPool1D (owl.Owl_neural.D.Graph.Neuron.AvgPool1D)

                                                            Module Neuron.AvgPool1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.AvgPool1D.neuron_typ = {
                                                            1. mutable padding : Owl_types.padding;
                                                            2. mutable kernel : int array;
                                                            3. mutable stride : int array;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/AvgPool2D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/AvgPool2D/index.html index 2b3d52dea..0d19e64d6 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/AvgPool2D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/AvgPool2D/index.html @@ -1,4 +1,4 @@ -AvgPool2D (owl.Owl_neural.D.Graph.Neuron.AvgPool2D)

                                                            Module Neuron.AvgPool2D

                                                            type neuron_typ = +AvgPool2D (owl.Owl_neural.D.Graph.Neuron.AvgPool2D)

                                                            Module Neuron.AvgPool2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.AvgPool2D.neuron_typ = {
                                                            1. mutable padding : Owl_types.padding;
                                                            2. mutable kernel : int array;
                                                            3. mutable stride : int array;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Concatenate/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Concatenate/index.html index ef12702d8..c4965bb4a 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Concatenate/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Concatenate/index.html @@ -1,4 +1,4 @@ -Concatenate (owl.Owl_neural.D.Graph.Neuron.Concatenate)

                                                            Module Neuron.Concatenate

                                                            type neuron_typ = +Concatenate (owl.Owl_neural.D.Graph.Neuron.Concatenate)

                                                            Module Neuron.Concatenate

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Concatenate.neuron_typ = {
                                                            1. mutable axis : int;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : int -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Conv1D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Conv1D/index.html index 8374570e3..d54d0ebb6 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Conv1D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl.Owl_neural.D.Graph.Neuron.Conv1D)

                                                            Module Neuron.Conv1D

                                                            type neuron_typ = +Conv1D (owl.Owl_neural.D.Graph.Neuron.Conv1D)

                                                            Module Neuron.Conv1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Conv1D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Conv2D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Conv2D/index.html index d4c2e1ac1..ac3a901c9 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Conv2D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl.Owl_neural.D.Graph.Neuron.Conv2D)

                                                            Module Neuron.Conv2D

                                                            type neuron_typ = +Conv2D (owl.Owl_neural.D.Graph.Neuron.Conv2D)

                                                            Module Neuron.Conv2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Conv2D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Conv3D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Conv3D/index.html index 88ecc9a9e..0ab418ee6 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Conv3D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl.Owl_neural.D.Graph.Neuron.Conv3D)

                                                            Module Neuron.Conv3D

                                                            type neuron_typ = +Conv3D (owl.Owl_neural.D.Graph.Neuron.Conv3D)

                                                            Module Neuron.Conv3D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Conv3D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv1D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv1D/index.html index da250fb35..af77d8124 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv1D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl.Owl_neural.D.Graph.Neuron.DilatedConv1D)

                                                            Module Neuron.DilatedConv1D

                                                            type neuron_typ = +DilatedConv1D (owl.Owl_neural.D.Graph.Neuron.DilatedConv1D)

                                                            Module Neuron.DilatedConv1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.DilatedConv1D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable rate : int array;
                                                            6. mutable padding : Owl_types.padding;
                                                            7. mutable init_typ : Init.typ;
                                                            8. mutable in_shape : int array;
                                                            9. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv2D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv2D/index.html index aefcb024b..7c5ceb9fd 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv2D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl.Owl_neural.D.Graph.Neuron.DilatedConv2D)

                                                            Module Neuron.DilatedConv2D

                                                            type neuron_typ = +DilatedConv2D (owl.Owl_neural.D.Graph.Neuron.DilatedConv2D)

                                                            Module Neuron.DilatedConv2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.DilatedConv2D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable rate : int array;
                                                            6. mutable padding : Owl_types.padding;
                                                            7. mutable init_typ : Init.typ;
                                                            8. mutable in_shape : int array;
                                                            9. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv3D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv3D/index.html index e7fdc6b79..16d7d7165 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv3D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl.Owl_neural.D.Graph.Neuron.DilatedConv3D)

                                                            Module Neuron.DilatedConv3D

                                                            type neuron_typ = +DilatedConv3D (owl.Owl_neural.D.Graph.Neuron.DilatedConv3D)

                                                            Module Neuron.DilatedConv3D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.DilatedConv3D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable rate : int array;
                                                            6. mutable padding : Owl_types.padding;
                                                            7. mutable init_typ : Init.typ;
                                                            8. mutable in_shape : int array;
                                                            9. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Dot/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Dot/index.html index 5f56ca72b..5fdb75f4f 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Dot/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Dot/index.html @@ -1,4 +1,4 @@ -Dot (owl.Owl_neural.D.Graph.Neuron.Dot)

                                                            Module Neuron.Dot

                                                            type neuron_typ = +Dot (owl.Owl_neural.D.Graph.Neuron.Dot)

                                                            Module Neuron.Dot

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Dot.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Dropout/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Dropout/index.html index ef6a32531..6afc49cb7 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Dropout/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Dropout/index.html @@ -1,4 +1,4 @@ -Dropout (owl.Owl_neural.D.Graph.Neuron.Dropout)

                                                            Module Neuron.Dropout

                                                            type neuron_typ = +Dropout (owl.Owl_neural.D.Graph.Neuron.Dropout)

                                                            Module Neuron.Dropout

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Dropout.neuron_typ = {
                                                            1. mutable rate : float;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : float -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Embedding/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Embedding/index.html index 585e95191..6f29d2d09 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Embedding/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Embedding/index.html @@ -1,4 +1,4 @@ -Embedding (owl.Owl_neural.D.Graph.Neuron.Embedding)

                                                            Module Neuron.Embedding

                                                            type neuron_typ = +Embedding (owl.Owl_neural.D.Graph.Neuron.Embedding)

                                                            Module Neuron.Embedding

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Embedding.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable init_typ : Init.typ;
                                                            3. mutable in_dim : int;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Flatten/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Flatten/index.html index 0be334298..854d603db 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Flatten/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Flatten/index.html @@ -1,4 +1,4 @@ -Flatten (owl.Owl_neural.D.Graph.Neuron.Flatten)

                                                            Module Neuron.Flatten

                                                            type neuron_typ = +Flatten (owl.Owl_neural.D.Graph.Neuron.Flatten)

                                                            Module Neuron.Flatten

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Flatten.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/FullyConnected/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/FullyConnected/index.html index a18f13f62..238ede34e 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/FullyConnected/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/FullyConnected/index.html @@ -1,4 +1,4 @@ -FullyConnected (owl.Owl_neural.D.Graph.Neuron.FullyConnected)

                                                            Module Neuron.FullyConnected

                                                            type neuron_typ = +FullyConnected (owl.Owl_neural.D.Graph.Neuron.FullyConnected)

                                                            Module Neuron.FullyConnected

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.FullyConnected.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable init_typ : Init.typ;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/GRU/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/GRU/index.html index 78c01b83a..afb684b54 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/GRU/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/GRU/index.html @@ -1,4 +1,4 @@ -GRU (owl.Owl_neural.D.Graph.Neuron.GRU)

                                                            Module Neuron.GRU

                                                            type neuron_typ = +GRU (owl.Owl_neural.D.Graph.Neuron.GRU)

                                                            Module Neuron.GRU

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.GRU.neuron_typ = {
                                                            1. mutable wxz : Optimise.Algodiff.t;
                                                            2. mutable whz : Optimise.Algodiff.t;
                                                            3. mutable wxr : Optimise.Algodiff.t;
                                                            4. mutable whr : Optimise.Algodiff.t;
                                                            5. mutable wxh : Optimise.Algodiff.t;
                                                            6. mutable whh : Optimise.Algodiff.t;
                                                            7. mutable bz : Optimise.Algodiff.t;
                                                            8. mutable br : Optimise.Algodiff.t;
                                                            9. mutable bh : Optimise.Algodiff.t;
                                                            10. mutable h : Optimise.Algodiff.t;
                                                            11. mutable init_typ : Init.typ;
                                                            12. mutable in_shape : int array;
                                                            13. mutable out_shape : int array;
                                                            }
                                                            val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/GaussianDropout/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/GaussianDropout/index.html index c5fc73d28..12c6857fe 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/GaussianDropout/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/GaussianDropout/index.html @@ -1,4 +1,4 @@ -GaussianDropout (owl.Owl_neural.D.Graph.Neuron.GaussianDropout)

                                                            Module Neuron.GaussianDropout

                                                            type neuron_typ = +GaussianDropout (owl.Owl_neural.D.Graph.Neuron.GaussianDropout)

                                                            Module Neuron.GaussianDropout

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.GaussianDropout.neuron_typ = {
                                                            1. mutable rate : float;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : float -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/GaussianNoise/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/GaussianNoise/index.html index aab710953..7eabfdbca 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/GaussianNoise/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/GaussianNoise/index.html @@ -1,4 +1,4 @@ -GaussianNoise (owl.Owl_neural.D.Graph.Neuron.GaussianNoise)

                                                            Module Neuron.GaussianNoise

                                                            type neuron_typ = +GaussianNoise (owl.Owl_neural.D.Graph.Neuron.GaussianNoise)

                                                            Module Neuron.GaussianNoise

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.GaussianNoise.neuron_typ = {
                                                            1. mutable sigma : float;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : float -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/GlobalAvgPool1D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/GlobalAvgPool1D/index.html index cc66b77a1..4685def83 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/GlobalAvgPool1D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/GlobalAvgPool1D/index.html @@ -1,4 +1,4 @@ -GlobalAvgPool1D (owl.Owl_neural.D.Graph.Neuron.GlobalAvgPool1D)

                                                            Module Neuron.GlobalAvgPool1D

                                                            type neuron_typ = +GlobalAvgPool1D (owl.Owl_neural.D.Graph.Neuron.GlobalAvgPool1D)

                                                            Module Neuron.GlobalAvgPool1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.GlobalAvgPool1D.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/GlobalAvgPool2D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/GlobalAvgPool2D/index.html index 31dfca794..752aa1dfe 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/GlobalAvgPool2D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/GlobalAvgPool2D/index.html @@ -1,4 +1,4 @@ -GlobalAvgPool2D (owl.Owl_neural.D.Graph.Neuron.GlobalAvgPool2D)

                                                            Module Neuron.GlobalAvgPool2D

                                                            type neuron_typ = +GlobalAvgPool2D (owl.Owl_neural.D.Graph.Neuron.GlobalAvgPool2D)

                                                            Module Neuron.GlobalAvgPool2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.GlobalAvgPool2D.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/GlobalMaxPool1D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/GlobalMaxPool1D/index.html index 174e06187..382b10049 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/GlobalMaxPool1D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/GlobalMaxPool1D/index.html @@ -1,4 +1,4 @@ -GlobalMaxPool1D (owl.Owl_neural.D.Graph.Neuron.GlobalMaxPool1D)

                                                            Module Neuron.GlobalMaxPool1D

                                                            type neuron_typ = +GlobalMaxPool1D (owl.Owl_neural.D.Graph.Neuron.GlobalMaxPool1D)

                                                            Module Neuron.GlobalMaxPool1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.GlobalMaxPool1D.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/GlobalMaxPool2D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/GlobalMaxPool2D/index.html index 09e3b839d..c63f03c3c 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/GlobalMaxPool2D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/GlobalMaxPool2D/index.html @@ -1,4 +1,4 @@ -GlobalMaxPool2D (owl.Owl_neural.D.Graph.Neuron.GlobalMaxPool2D)

                                                            Module Neuron.GlobalMaxPool2D

                                                            type neuron_typ = +GlobalMaxPool2D (owl.Owl_neural.D.Graph.Neuron.GlobalMaxPool2D)

                                                            Module Neuron.GlobalMaxPool2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.GlobalMaxPool2D.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Init/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Init/index.html index 2066d5ebc..8b0a9a45e 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Init/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Init/index.html @@ -1,4 +1,4 @@ -Init (owl.Owl_neural.D.Graph.Neuron.Init)

                                                            Module Neuron.Init

                                                            type typ = +Init (owl.Owl_neural.D.Graph.Neuron.Init)

                                                            Module Neuron.Init

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Init.typ =
                                                            1. | Uniform of float * float
                                                            2. | Gaussian of float * float
                                                            3. | Standard
                                                            4. | Tanh
                                                            5. | GlorotNormal
                                                            6. | GlorotUniform
                                                            7. | LecunNormal
                                                            8. | HeNormal
                                                            9. | Custom of int array -> Optimise.Algodiff.t
                                                            val calc_fans : int array -> float * float
                                                            val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                            val to_string : typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Input/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Input/index.html index a9dd7565c..77ff0b6bf 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Input/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Input/index.html @@ -1,4 +1,4 @@ -Input (owl.Owl_neural.D.Graph.Neuron.Input)

                                                            Module Neuron.Input

                                                            type neuron_typ = +Input (owl.Owl_neural.D.Graph.Neuron.Input)

                                                            Module Neuron.Input

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Input.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : int array -> neuron_typ
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/LSTM/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/LSTM/index.html index 493455b98..ce41cacdc 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/LSTM/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/LSTM/index.html @@ -1,4 +1,4 @@ -LSTM (owl.Owl_neural.D.Graph.Neuron.LSTM)

                                                            Module Neuron.LSTM

                                                            type neuron_typ = +LSTM (owl.Owl_neural.D.Graph.Neuron.LSTM)

                                                            Module Neuron.LSTM

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.LSTM.neuron_typ = {
                                                            1. mutable wxi : Optimise.Algodiff.t;
                                                            2. mutable whi : Optimise.Algodiff.t;
                                                            3. mutable wxc : Optimise.Algodiff.t;
                                                            4. mutable whc : Optimise.Algodiff.t;
                                                            5. mutable wxf : Optimise.Algodiff.t;
                                                            6. mutable whf : Optimise.Algodiff.t;
                                                            7. mutable wxo : Optimise.Algodiff.t;
                                                            8. mutable who : Optimise.Algodiff.t;
                                                            9. mutable bi : Optimise.Algodiff.t;
                                                            10. mutable bc : Optimise.Algodiff.t;
                                                            11. mutable bf : Optimise.Algodiff.t;
                                                            12. mutable bo : Optimise.Algodiff.t;
                                                            13. mutable c : Optimise.Algodiff.t;
                                                            14. mutable h : Optimise.Algodiff.t;
                                                            15. mutable init_typ : Init.typ;
                                                            16. mutable in_shape : int array;
                                                            17. mutable out_shape : int array;
                                                            }
                                                            val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Lambda/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Lambda/index.html index 558cf7c10..fff6fa5dd 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Lambda/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl.Owl_neural.D.Graph.Neuron.Lambda)

                                                            Module Neuron.Lambda

                                                            type neuron_typ = +Lambda (owl.Owl_neural.D.Graph.Neuron.Lambda)

                                                            Module Neuron.Lambda

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Lambda.neuron_typ = {
                                                            1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : ?out_shape:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/LambdaArray/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/LambdaArray/index.html index 64a01c874..cbc47de60 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/LambdaArray/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl.Owl_neural.D.Graph.Neuron.LambdaArray)

                                                            Module Neuron.LambdaArray

                                                            type neuron_typ = +LambdaArray (owl.Owl_neural.D.Graph.Neuron.LambdaArray)

                                                            Module Neuron.LambdaArray

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.LambdaArray.neuron_typ = {
                                                            1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Linear/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Linear/index.html index d1b2c25e9..582529fa3 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Linear/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Linear/index.html @@ -1,4 +1,4 @@ -Linear (owl.Owl_neural.D.Graph.Neuron.Linear)

                                                            Module Neuron.Linear

                                                            type neuron_typ = +Linear (owl.Owl_neural.D.Graph.Neuron.Linear)

                                                            Module Neuron.Linear

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Linear.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable init_typ : Init.typ;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/LinearNoBias/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/LinearNoBias/index.html index a1a8ab184..364ca01a6 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/LinearNoBias/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/LinearNoBias/index.html @@ -1,4 +1,4 @@ -LinearNoBias (owl.Owl_neural.D.Graph.Neuron.LinearNoBias)

                                                            Module Neuron.LinearNoBias

                                                            type neuron_typ = +LinearNoBias (owl.Owl_neural.D.Graph.Neuron.LinearNoBias)

                                                            Module Neuron.LinearNoBias

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.LinearNoBias.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable init_typ : Init.typ;
                                                            3. mutable in_shape : int array;
                                                            4. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Masking/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Masking/index.html index 3222d8cfe..e9e967b48 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Masking/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl.Owl_neural.D.Graph.Neuron.Masking)

                                                            Module Neuron.Masking

                                                            +Masking (owl.Owl_neural.D.Graph.Neuron.Masking)

                                                            Module Neuron.Masking

                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Max/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Max/index.html index 301b14524..56999f929 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Max/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Max/index.html @@ -1,4 +1,4 @@ -Max (owl.Owl_neural.D.Graph.Neuron.Max)

                                                            Module Neuron.Max

                                                            type neuron_typ = +Max (owl.Owl_neural.D.Graph.Neuron.Max)

                                                            Module Neuron.Max

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Max.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/MaxPool1D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/MaxPool1D/index.html index 93b30a928..2f9d43b12 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/MaxPool1D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/MaxPool1D/index.html @@ -1,4 +1,4 @@ -MaxPool1D (owl.Owl_neural.D.Graph.Neuron.MaxPool1D)

                                                            Module Neuron.MaxPool1D

                                                            type neuron_typ = +MaxPool1D (owl.Owl_neural.D.Graph.Neuron.MaxPool1D)

                                                            Module Neuron.MaxPool1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.MaxPool1D.neuron_typ = {
                                                            1. mutable padding : Owl_types.padding;
                                                            2. mutable kernel : int array;
                                                            3. mutable stride : int array;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/MaxPool2D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/MaxPool2D/index.html index 0ed3779db..94e304d29 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/MaxPool2D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/MaxPool2D/index.html @@ -1,4 +1,4 @@ -MaxPool2D (owl.Owl_neural.D.Graph.Neuron.MaxPool2D)

                                                            Module Neuron.MaxPool2D

                                                            type neuron_typ = +MaxPool2D (owl.Owl_neural.D.Graph.Neuron.MaxPool2D)

                                                            Module Neuron.MaxPool2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.MaxPool2D.neuron_typ = {
                                                            1. mutable padding : Owl_types.padding;
                                                            2. mutable kernel : int array;
                                                            3. mutable stride : int array;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Mul/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Mul/index.html index 881a32298..c6b7dfa70 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Mul/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Mul/index.html @@ -1,4 +1,4 @@ -Mul (owl.Owl_neural.D.Graph.Neuron.Mul)

                                                            Module Neuron.Mul

                                                            type neuron_typ = +Mul (owl.Owl_neural.D.Graph.Neuron.Mul)

                                                            Module Neuron.Mul

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Mul.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Normalisation/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Normalisation/index.html index 39958835a..95e63e264 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Normalisation/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl.Owl_neural.D.Graph.Neuron.Normalisation)

                                                            Module Neuron.Normalisation

                                                            type neuron_typ = +Normalisation (owl.Owl_neural.D.Graph.Neuron.Normalisation)

                                                            Module Neuron.Normalisation

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Normalisation.neuron_typ = {
                                                            1. mutable axis : int;
                                                            2. mutable beta : Optimise.Algodiff.t;
                                                            3. mutable gamma : Optimise.Algodiff.t;
                                                            4. mutable mu : Optimise.Algodiff.t;
                                                            5. mutable var : Optimise.Algodiff.t;
                                                            6. mutable decay : Optimise.Algodiff.t;
                                                            7. mutable training : bool;
                                                            8. mutable in_shape : int array;
                                                            9. mutable out_shape : int array;
                                                            }
                                                            val create : ?training:bool -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html index 6628f15c9..2b1bc9440 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html index de4029c7e..67aa8c139 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html index c0ef60b54..df23ab1d8 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/index.html index 0176d947d..a39c78a54 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            type arr = +A (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Arr/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Arr/index.html index 8695caa87..223e41437 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Arr/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/index.html index e3c62220d..aa7609336 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html index 0f3bde69e..ad284a59b 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html index b78388234..5cd8372ce 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html index 87deb6582..57149026b 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 2a5564741..b228d5d99 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html index 2ef42e52d..5e6515d11 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html index 736bd920d..f4d966cd6 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Linalg/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Linalg/index.html index e7bd3a8f5..2382933fa 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Mat/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Mat/index.html index 98bfd8b15..3b2152b20 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Mat/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Maths/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Maths/index.html index de2b85357..92238a697 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Maths/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/NN/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/NN/index.html index 2c607e4cd..7ea74bd50 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/NN/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/index.html index 16f1b95cd..fb341a399 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Algodiff/index.html @@ -1,4 +1,4 @@ -Algodiff (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = +Algodiff (owl.Owl_neural.D.Graph.Neuron.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Algodiff.t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Batch/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Batch/index.html index 1aaba8fb7..0aad2a622 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Batch/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Batch/index.html @@ -1,4 +1,4 @@ -Batch (owl.Owl_neural.D.Graph.Neuron.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = +Batch (owl.Owl_neural.D.Graph.Neuron.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Checkpoint/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Checkpoint/index.html index ec8ac5a4e..e89a5e590 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Checkpoint/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl.Owl_neural.D.Graph.Neuron.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = +Checkpoint (owl.Owl_neural.D.Graph.Neuron.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Checkpoint.state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }
                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Checkpoint.typ = diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Clipping/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Clipping/index.html index e0d284f95..c10d62cf2 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Clipping/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Clipping/index.html @@ -1,4 +1,4 @@ -Clipping (owl.Owl_neural.D.Graph.Neuron.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            type typ = +Clipping (owl.Owl_neural.D.Graph.Neuron.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Clipping.typ =
                                                            1. | L2norm of float
                                                            2. | Value of float * float
                                                            3. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Gradient/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Gradient/index.html index 4ba0fd032..6cd7252f5 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Gradient/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_neural.D.Graph.Neuron.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = +Gradient (owl.Owl_neural.D.Graph.Neuron.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Gradient.typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton
                                                            val run : typ -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Learning_Rate/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Learning_Rate/index.html index a29371dd4..ca32ccf89 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Learning_Rate/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl.Owl_neural.D.Graph.Neuron.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = +Learning_Rate (owl.Owl_neural.D.Graph.Neuron.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Learning_Rate.typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                            val default : typ -> typ
                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Loss/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Loss/index.html index 087ab0d06..9c7e1ba54 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Loss/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Loss/index.html @@ -1,4 +1,4 @@ -Loss (owl.Owl_neural.D.Graph.Neuron.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = +Loss (owl.Owl_neural.D.Graph.Neuron.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Momentum/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Momentum/index.html index 7b2eb91bd..9f6392a0d 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Momentum/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Momentum/index.html @@ -1,4 +1,4 @@ -Momentum (owl.Owl_neural.D.Graph.Neuron.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            type typ = +Momentum (owl.Owl_neural.D.Graph.Neuron.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Params/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Params/index.html index 360bac249..7515dab9a 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Params/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_neural.D.Graph.Neuron.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = +Params (owl.Owl_neural.D.Graph.Neuron.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : ?batch:Batch.typ -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Regularisation/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Regularisation/index.html index 92e4fd6f4..33a96bdeb 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Regularisation/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl.Owl_neural.D.Graph.Neuron.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = +Regularisation (owl.Owl_neural.D.Graph.Neuron.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Optimise.Regularisation.typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Stopping/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Stopping/index.html index e472a79fc..f84b4b496 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Stopping/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Stopping/index.html @@ -1,4 +1,4 @@ -Stopping (owl.Owl_neural.D.Graph.Neuron.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            type typ = +Stopping (owl.Owl_neural.D.Graph.Neuron.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            val run : typ -> float -> bool
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Utils/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Utils/index.html index a3b13f872..5265ae1b4 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Utils/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_neural.D.Graph.Neuron.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : +Utils (owl.Owl_neural.D.Graph.Neuron.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/index.html index 9ee7ae154..820b508f4 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl.Owl_neural.D.Graph.Neuron.Optimise)

                                                            Module Neuron.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : +Optimise (owl.Owl_neural.D.Graph.Neuron.Optimise)

                                                            Module Neuron.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Padding1D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Padding1D/index.html index 0a615646c..32f97de7a 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Padding1D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl.Owl_neural.D.Graph.Neuron.Padding1D)

                                                            Module Neuron.Padding1D

                                                            +Padding1D (owl.Owl_neural.D.Graph.Neuron.Padding1D)

                                                            Module Neuron.Padding1D

                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Padding2D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Padding2D/index.html index 9f0edeb65..6f68d2b76 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Padding2D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Padding2D/index.html @@ -1,4 +1,4 @@ -Padding2D (owl.Owl_neural.D.Graph.Neuron.Padding2D)

                                                            Module Neuron.Padding2D

                                                            type neuron_typ = +Padding2D (owl.Owl_neural.D.Graph.Neuron.Padding2D)

                                                            Module Neuron.Padding2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Padding2D.neuron_typ = {
                                                            1. mutable padding : int array array;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : int array array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Padding3D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Padding3D/index.html index 7cab1340b..e3cf9346e 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Padding3D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl.Owl_neural.D.Graph.Neuron.Padding3D)

                                                            Module Neuron.Padding3D

                                                            +Padding3D (owl.Owl_neural.D.Graph.Neuron.Padding3D)

                                                            Module Neuron.Padding3D

                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Recurrent/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Recurrent/index.html index b2c5cee1a..2715b5936 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Recurrent/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl.Owl_neural.D.Graph.Neuron.Recurrent)

                                                            Module Neuron.Recurrent

                                                            type neuron_typ = +Recurrent (owl.Owl_neural.D.Graph.Neuron.Recurrent)

                                                            Module Neuron.Recurrent

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Recurrent.neuron_typ = {
                                                            1. mutable whh : Optimise.Algodiff.t;
                                                            2. mutable wxh : Optimise.Algodiff.t;
                                                            3. mutable why : Optimise.Algodiff.t;
                                                            4. mutable bh : Optimise.Algodiff.t;
                                                            5. mutable by : Optimise.Algodiff.t;
                                                            6. mutable h : Optimise.Algodiff.t;
                                                            7. mutable hiddens : int;
                                                            8. mutable act : Activation.typ;
                                                            9. mutable init_typ : Init.typ;
                                                            10. mutable in_shape : int array;
                                                            11. mutable out_shape : int array;
                                                            }
                                                            val create : ?time_steps:int -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Reshape/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Reshape/index.html index b161ffbfc..732712aac 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Reshape/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Reshape/index.html @@ -1,4 +1,4 @@ -Reshape (owl.Owl_neural.D.Graph.Neuron.Reshape)

                                                            Module Neuron.Reshape

                                                            type neuron_typ = +Reshape (owl.Owl_neural.D.Graph.Neuron.Reshape)

                                                            Module Neuron.Reshape

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Reshape.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/Slice/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/Slice/index.html index f3074975b..11efb4aad 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/Slice/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/Slice/index.html @@ -1,4 +1,4 @@ -Slice (owl.Owl_neural.D.Graph.Neuron.Slice)

                                                            Module Neuron.Slice

                                                            type neuron_typ = +Slice (owl.Owl_neural.D.Graph.Neuron.Slice)

                                                            Module Neuron.Slice

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.Slice.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            3. mutable slice : int list list;
                                                            }
                                                            val create : int list list -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv1D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv1D/index.html index 10a2a228f..5e41d9ebd 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv1D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl.Owl_neural.D.Graph.Neuron.TransposeConv1D)

                                                            Module Neuron.TransposeConv1D

                                                            type neuron_typ = +TransposeConv1D (owl.Owl_neural.D.Graph.Neuron.TransposeConv1D)

                                                            Module Neuron.TransposeConv1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.TransposeConv1D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv2D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv2D/index.html index 8784d1ee8..4f477723e 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv2D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl.Owl_neural.D.Graph.Neuron.TransposeConv2D)

                                                            Module Neuron.TransposeConv2D

                                                            type neuron_typ = +TransposeConv2D (owl.Owl_neural.D.Graph.Neuron.TransposeConv2D)

                                                            Module Neuron.TransposeConv2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.TransposeConv2D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv3D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv3D/index.html index 97beb54d4..9668f4089 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv3D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl.Owl_neural.D.Graph.Neuron.TransposeConv3D)

                                                            Module Neuron.TransposeConv3D

                                                            type neuron_typ = +TransposeConv3D (owl.Owl_neural.D.Graph.Neuron.TransposeConv3D)

                                                            Module Neuron.TransposeConv3D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.TransposeConv3D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling1D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling1D/index.html index 24658eed9..434a47802 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling1D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl.Owl_neural.D.Graph.Neuron.UpSampling1D)

                                                            Module Neuron.UpSampling1D

                                                            +UpSampling1D (owl.Owl_neural.D.Graph.Neuron.UpSampling1D)

                                                            Module Neuron.UpSampling1D

                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling2D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling2D/index.html index 2010678b6..aaa8143b5 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling2D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling2D/index.html @@ -1,4 +1,4 @@ -UpSampling2D (owl.Owl_neural.D.Graph.Neuron.UpSampling2D)

                                                            Module Neuron.UpSampling2D

                                                            type neuron_typ = +UpSampling2D (owl.Owl_neural.D.Graph.Neuron.UpSampling2D)

                                                            Module Neuron.UpSampling2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.UpSampling2D.neuron_typ = {
                                                            1. mutable size : int array;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling3D/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling3D/index.html index 9c3b87f43..7effd133d 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling3D/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl.Owl_neural.D.Graph.Neuron.UpSampling3D)

                                                            Module Neuron.UpSampling3D

                                                            +UpSampling3D (owl.Owl_neural.D.Graph.Neuron.UpSampling3D)

                                                            Module Neuron.UpSampling3D

                                                            diff --git a/docs/owl/Owl_neural/D/Graph/Neuron/index.html b/docs/owl/Owl_neural/D/Graph/Neuron/index.html index ed73d986e..6969f3b1b 100644 --- a/docs/owl/Owl_neural/D/Graph/Neuron/index.html +++ b/docs/owl/Owl_neural/D/Graph/Neuron/index.html @@ -1,4 +1,4 @@ -Neuron (owl.Owl_neural.D.Graph.Neuron)

                                                            Module Graph.Neuron

                                                            module Optimise : sig ... end
                                                            module Init : sig ... end
                                                            module Input : sig ... end
                                                            module Activation : sig ... end
                                                            module Linear : sig ... end
                                                            module LinearNoBias : sig ... end
                                                            module Recurrent : sig ... end
                                                            module LSTM : sig ... end
                                                            module GRU : sig ... end
                                                            module Conv1D : sig ... end
                                                            module Conv2D : sig ... end
                                                            module Conv3D : sig ... end
                                                            module DilatedConv1D : sig ... end
                                                            module DilatedConv2D : sig ... end
                                                            module DilatedConv3D : sig ... end
                                                            module TransposeConv1D : sig ... end
                                                            module TransposeConv2D : sig ... end
                                                            module TransposeConv3D : sig ... end
                                                            module FullyConnected : sig ... end
                                                            module MaxPool1D : sig ... end
                                                            module MaxPool2D : sig ... end
                                                            module AvgPool1D : sig ... end
                                                            module AvgPool2D : sig ... end
                                                            module GlobalMaxPool1D : sig ... end
                                                            module GlobalMaxPool2D : sig ... end
                                                            module GlobalAvgPool1D : sig ... end
                                                            module GlobalAvgPool2D : sig ... end
                                                            module UpSampling1D : sig ... end
                                                            module UpSampling2D : sig ... end
                                                            module UpSampling3D : sig ... end
                                                            module Padding1D : sig ... end
                                                            module Padding2D : sig ... end
                                                            module Padding3D : sig ... end
                                                            module Lambda : sig ... end
                                                            module LambdaArray : sig ... end
                                                            module Dropout : sig ... end
                                                            module Reshape : sig ... end
                                                            module Flatten : sig ... end
                                                            module Slice : sig ... end
                                                            module Add : sig ... end
                                                            module Mul : sig ... end
                                                            module Dot : sig ... end
                                                            module Max : sig ... end
                                                            module Average : sig ... end
                                                            module Concatenate : sig ... end
                                                            module Normalisation : sig ... end
                                                            module GaussianNoise : sig ... end
                                                            module GaussianDropout : sig ... end
                                                            module AlphaDropout : sig ... end
                                                            module Embedding : sig ... end
                                                            module Masking : sig ... end
                                                            type neuron = +Neuron (owl.Owl_neural.D.Graph.Neuron)

                                                            Module Graph.Neuron

                                                            module Optimise : sig ... end
                                                            module Init : sig ... end
                                                            module Input : sig ... end
                                                            module Activation : sig ... end
                                                            module Linear : sig ... end
                                                            module LinearNoBias : sig ... end
                                                            module Recurrent : sig ... end
                                                            module LSTM : sig ... end
                                                            module GRU : sig ... end
                                                            module Conv1D : sig ... end
                                                            module Conv2D : sig ... end
                                                            module Conv3D : sig ... end
                                                            module DilatedConv1D : sig ... end
                                                            module DilatedConv2D : sig ... end
                                                            module DilatedConv3D : sig ... end
                                                            module TransposeConv1D : sig ... end
                                                            module TransposeConv2D : sig ... end
                                                            module TransposeConv3D : sig ... end
                                                            module FullyConnected : sig ... end
                                                            module MaxPool1D : sig ... end
                                                            module MaxPool2D : sig ... end
                                                            module AvgPool1D : sig ... end
                                                            module AvgPool2D : sig ... end
                                                            module GlobalMaxPool1D : sig ... end
                                                            module GlobalMaxPool2D : sig ... end
                                                            module GlobalAvgPool1D : sig ... end
                                                            module GlobalAvgPool2D : sig ... end
                                                            module UpSampling1D : sig ... end
                                                            module UpSampling2D : sig ... end
                                                            module UpSampling3D : sig ... end
                                                            module Padding1D : sig ... end
                                                            module Padding2D : sig ... end
                                                            module Padding3D : sig ... end
                                                            module Lambda : sig ... end
                                                            module LambdaArray : sig ... end
                                                            module Dropout : sig ... end
                                                            module Reshape : sig ... end
                                                            module Flatten : sig ... end
                                                            module Slice : sig ... end
                                                            module Add : sig ... end
                                                            module Mul : sig ... end
                                                            module Dot : sig ... end
                                                            module Max : sig ... end
                                                            module Average : sig ... end
                                                            module Concatenate : sig ... end
                                                            module Normalisation : sig ... end
                                                            module GaussianNoise : sig ... end
                                                            module GaussianDropout : sig ... end
                                                            module AlphaDropout : sig ... end
                                                            module Embedding : sig ... end
                                                            module Masking : sig ... end
                                                            type neuron = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).Neuron.neuron =
                                                            1. | Input of Input.neuron_typ
                                                            2. | Linear of Linear.neuron_typ
                                                            3. | LinearNoBias of LinearNoBias.neuron_typ
                                                            4. | Embedding of Embedding.neuron_typ
                                                            5. | LSTM of LSTM.neuron_typ
                                                            6. | GRU of GRU.neuron_typ
                                                            7. | Recurrent of Recurrent.neuron_typ
                                                            8. | Conv1D of Conv1D.neuron_typ
                                                            9. | Conv2D of Conv2D.neuron_typ
                                                            10. | Conv3D of Conv3D.neuron_typ
                                                            11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                            12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                            13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                            14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                            15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                            16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                            17. | FullyConnected of FullyConnected.neuron_typ
                                                            18. | MaxPool1D of MaxPool1D.neuron_typ
                                                            19. | MaxPool2D of MaxPool2D.neuron_typ
                                                            20. | AvgPool1D of AvgPool1D.neuron_typ
                                                            21. | AvgPool2D of AvgPool2D.neuron_typ
                                                            22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                            23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                            24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                            25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                            26. | UpSampling2D of UpSampling2D.neuron_typ
                                                            27. | Padding2D of Padding2D.neuron_typ
                                                            28. | Dropout of Dropout.neuron_typ
                                                            29. | Reshape of Reshape.neuron_typ
                                                            30. | Flatten of Flatten.neuron_typ
                                                            31. | Slice of Slice.neuron_typ
                                                            32. | Lambda of Lambda.neuron_typ
                                                            33. | LambdaArray of LambdaArray.neuron_typ
                                                            34. | Activation of Activation.neuron_typ
                                                            35. | GaussianNoise of GaussianNoise.neuron_typ
                                                            36. | GaussianDropout of GaussianDropout.neuron_typ
                                                            37. | AlphaDropout of AlphaDropout.neuron_typ
                                                            38. | Normalisation of Normalisation.neuron_typ
                                                            39. | Add of Add.neuron_typ
                                                            40. | Mul of Mul.neuron_typ
                                                            41. | Dot of Dot.neuron_typ
                                                            42. | Max of Max.neuron_typ
                                                            43. | Average of Average.neuron_typ
                                                            44. | Concatenate of Concatenate.neuron_typ
                                                            val get_in_out_shape : neuron -> int array * int array
                                                            val get_in_shape : neuron -> int array
                                                            val get_out_shape : neuron -> int array
                                                            val connect : int array array -> neuron -> unit
                                                            val init : neuron -> unit
                                                            val reset : neuron -> unit
                                                            val mktag : int -> neuron -> unit
                                                            val mkpar : neuron -> Optimise.Algodiff.t array
                                                            val mkpri : neuron -> Optimise.Algodiff.t array
                                                            val mkadj : neuron -> Optimise.Algodiff.t array
                                                            val update : neuron -> Optimise.Algodiff.t array -> unit
                                                            val load_weights : neuron -> Optimise.Algodiff.t array -> unit
                                                            val save_weights : neuron -> Optimise.Algodiff.t array
                                                            val copy : neuron -> neuron
                                                            val to_string : neuron -> string
                                                            val to_name : neuron -> string
                                                            diff --git a/docs/owl/Owl_neural/D/Graph/index.html b/docs/owl/Owl_neural/D/Graph/index.html index cf226da5d..c5026f998 100644 --- a/docs/owl/Owl_neural/D/Graph/index.html +++ b/docs/owl/Owl_neural/D/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl.Owl_neural.D.Graph)

                                                            Module D.Graph

                                                            module Neuron : sig ... end
                                                            type node = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).node = {
                                                            1. mutable name : string;
                                                            2. mutable prev : node array;
                                                            3. mutable next : node array;
                                                            4. mutable neuron : Neuron.neuron;
                                                            5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                            6. mutable network : network;
                                                            7. mutable train : bool;
                                                            }
                                                            and network = +Graph (owl.Owl_neural.D.Graph)

                                                            Module D.Graph

                                                            module Neuron : sig ... end
                                                            type node = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).node = {
                                                            1. mutable name : string;
                                                            2. mutable prev : node array;
                                                            3. mutable next : node array;
                                                            4. mutable neuron : Neuron.neuron;
                                                            5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                            6. mutable network : network;
                                                            7. mutable train : bool;
                                                            }
                                                            and network = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.D).network = {
                                                            1. mutable nnid : string;
                                                            2. mutable size : int;
                                                            3. mutable roots : node array;
                                                            4. mutable outputs : node array;
                                                            5. mutable topo : node array;
                                                            }
                                                            val make_network : ?nnid:string -> int -> node array -> node array -> network
                                                            val make_node : ?name:string -> diff --git a/docs/owl/Owl_neural/D/index.html b/docs/owl/Owl_neural/D/index.html index eb13de0ba..50005781f 100644 --- a/docs/owl/Owl_neural/D/index.html +++ b/docs/owl/Owl_neural/D/index.html @@ -1,2 +1,2 @@ -D (owl.Owl_neural.D)

                                                            Module Owl_neural.D

                                                            include sig ... end
                                                            module Graph : sig ... end
                                                            module Optimise = Graph.Neuron.Optimise
                                                            module Init = Graph.Neuron.Init
                                                            module Activation = Graph.Neuron.Activation
                                                            module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                            +D (owl.Owl_neural.D)

                                                            Module Owl_neural.D

                                                            include sig ... end
                                                            module Graph : sig ... end
                                                            module Optimise = Graph.Neuron.Optimise
                                                            module Init = Graph.Neuron.Init
                                                            module Activation = Graph.Neuron.Activation
                                                            module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Activation/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Activation/index.html index 219dde7da..dd13ff7e7 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Activation/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Activation/index.html @@ -1,5 +1,5 @@ -Activation (owl.Owl_neural.S.Graph.Neuron.Activation)

                                                            Module Neuron.Activation

                                                            type typ = +Activation (owl.Owl_neural.S.Graph.Neuron.Activation)

                                                            Module Neuron.Activation

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Activation.typ =
                                                            1. | Elu
                                                            2. | Relu
                                                            3. | Sigmoid
                                                            4. | HardSigmoid
                                                            5. | Softmax of int
                                                            6. | Softplus
                                                            7. | Softsign
                                                            8. | Tanh
                                                            9. | Relu6
                                                            10. | LeakyRelu of float
                                                            11. | TRelu of float
                                                            12. | Custom of Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                            13. | None
                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Activation.neuron_typ = diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Add/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Add/index.html index 483b505cd..b5c09b633 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Add/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Add/index.html @@ -1,4 +1,4 @@ -Add (owl.Owl_neural.S.Graph.Neuron.Add)

                                                            Module Neuron.Add

                                                            type neuron_typ = +Add (owl.Owl_neural.S.Graph.Neuron.Add)

                                                            Module Neuron.Add

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Add.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/AlphaDropout/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/AlphaDropout/index.html index b1958bda9..02b2c0b27 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/AlphaDropout/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/AlphaDropout/index.html @@ -1,4 +1,4 @@ -AlphaDropout (owl.Owl_neural.S.Graph.Neuron.AlphaDropout)

                                                            Module Neuron.AlphaDropout

                                                            type neuron_typ = +AlphaDropout (owl.Owl_neural.S.Graph.Neuron.AlphaDropout)

                                                            Module Neuron.AlphaDropout

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.AlphaDropout.neuron_typ = {
                                                            1. mutable rate : float;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : float -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Average/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Average/index.html index cbd1c5822..360fca5b2 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Average/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Average/index.html @@ -1,4 +1,4 @@ -Average (owl.Owl_neural.S.Graph.Neuron.Average)

                                                            Module Neuron.Average

                                                            type neuron_typ = +Average (owl.Owl_neural.S.Graph.Neuron.Average)

                                                            Module Neuron.Average

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Average.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/AvgPool1D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/AvgPool1D/index.html index c492a0619..3a6d98386 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/AvgPool1D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/AvgPool1D/index.html @@ -1,4 +1,4 @@ -AvgPool1D (owl.Owl_neural.S.Graph.Neuron.AvgPool1D)

                                                            Module Neuron.AvgPool1D

                                                            type neuron_typ = +AvgPool1D (owl.Owl_neural.S.Graph.Neuron.AvgPool1D)

                                                            Module Neuron.AvgPool1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.AvgPool1D.neuron_typ = {
                                                            1. mutable padding : Owl_types.padding;
                                                            2. mutable kernel : int array;
                                                            3. mutable stride : int array;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/AvgPool2D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/AvgPool2D/index.html index 7597282cb..bb8eece4b 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/AvgPool2D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/AvgPool2D/index.html @@ -1,4 +1,4 @@ -AvgPool2D (owl.Owl_neural.S.Graph.Neuron.AvgPool2D)

                                                            Module Neuron.AvgPool2D

                                                            type neuron_typ = +AvgPool2D (owl.Owl_neural.S.Graph.Neuron.AvgPool2D)

                                                            Module Neuron.AvgPool2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.AvgPool2D.neuron_typ = {
                                                            1. mutable padding : Owl_types.padding;
                                                            2. mutable kernel : int array;
                                                            3. mutable stride : int array;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Concatenate/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Concatenate/index.html index 707b34c3c..e9ba9ce5f 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Concatenate/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Concatenate/index.html @@ -1,4 +1,4 @@ -Concatenate (owl.Owl_neural.S.Graph.Neuron.Concatenate)

                                                            Module Neuron.Concatenate

                                                            type neuron_typ = +Concatenate (owl.Owl_neural.S.Graph.Neuron.Concatenate)

                                                            Module Neuron.Concatenate

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Concatenate.neuron_typ = {
                                                            1. mutable axis : int;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : int -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Conv1D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Conv1D/index.html index db3e65af0..59f6447de 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Conv1D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Conv1D/index.html @@ -1,5 +1,5 @@ -Conv1D (owl.Owl_neural.S.Graph.Neuron.Conv1D)

                                                            Module Neuron.Conv1D

                                                            type neuron_typ = +Conv1D (owl.Owl_neural.S.Graph.Neuron.Conv1D)

                                                            Module Neuron.Conv1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Conv1D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Conv2D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Conv2D/index.html index 3016894d6..88c2f67b4 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Conv2D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Conv2D/index.html @@ -1,5 +1,5 @@ -Conv2D (owl.Owl_neural.S.Graph.Neuron.Conv2D)

                                                            Module Neuron.Conv2D

                                                            type neuron_typ = +Conv2D (owl.Owl_neural.S.Graph.Neuron.Conv2D)

                                                            Module Neuron.Conv2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Conv2D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Conv3D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Conv3D/index.html index b4056ab6b..faad22b5d 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Conv3D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Conv3D/index.html @@ -1,5 +1,5 @@ -Conv3D (owl.Owl_neural.S.Graph.Neuron.Conv3D)

                                                            Module Neuron.Conv3D

                                                            type neuron_typ = +Conv3D (owl.Owl_neural.S.Graph.Neuron.Conv3D)

                                                            Module Neuron.Conv3D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Conv3D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv1D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv1D/index.html index 1fab9b3d2..fbf768acf 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv1D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv1D/index.html @@ -1,5 +1,5 @@ -DilatedConv1D (owl.Owl_neural.S.Graph.Neuron.DilatedConv1D)

                                                            Module Neuron.DilatedConv1D

                                                            type neuron_typ = +DilatedConv1D (owl.Owl_neural.S.Graph.Neuron.DilatedConv1D)

                                                            Module Neuron.DilatedConv1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.DilatedConv1D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable rate : int array;
                                                            6. mutable padding : Owl_types.padding;
                                                            7. mutable init_typ : Init.typ;
                                                            8. mutable in_shape : int array;
                                                            9. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv2D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv2D/index.html index 935f084ff..5725aba62 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv2D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv2D/index.html @@ -1,5 +1,5 @@ -DilatedConv2D (owl.Owl_neural.S.Graph.Neuron.DilatedConv2D)

                                                            Module Neuron.DilatedConv2D

                                                            type neuron_typ = +DilatedConv2D (owl.Owl_neural.S.Graph.Neuron.DilatedConv2D)

                                                            Module Neuron.DilatedConv2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.DilatedConv2D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable rate : int array;
                                                            6. mutable padding : Owl_types.padding;
                                                            7. mutable init_typ : Init.typ;
                                                            8. mutable in_shape : int array;
                                                            9. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv3D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv3D/index.html index cd9d0dc32..f7c48e059 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv3D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/DilatedConv3D/index.html @@ -1,5 +1,5 @@ -DilatedConv3D (owl.Owl_neural.S.Graph.Neuron.DilatedConv3D)

                                                            Module Neuron.DilatedConv3D

                                                            type neuron_typ = +DilatedConv3D (owl.Owl_neural.S.Graph.Neuron.DilatedConv3D)

                                                            Module Neuron.DilatedConv3D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.DilatedConv3D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable rate : int array;
                                                            6. mutable padding : Owl_types.padding;
                                                            7. mutable init_typ : Init.typ;
                                                            8. mutable in_shape : int array;
                                                            9. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Dot/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Dot/index.html index 6c8c5c904..093d23fb2 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Dot/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Dot/index.html @@ -1,4 +1,4 @@ -Dot (owl.Owl_neural.S.Graph.Neuron.Dot)

                                                            Module Neuron.Dot

                                                            type neuron_typ = +Dot (owl.Owl_neural.S.Graph.Neuron.Dot)

                                                            Module Neuron.Dot

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Dot.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Dropout/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Dropout/index.html index 4db1eaaff..4928ff7b6 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Dropout/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Dropout/index.html @@ -1,4 +1,4 @@ -Dropout (owl.Owl_neural.S.Graph.Neuron.Dropout)

                                                            Module Neuron.Dropout

                                                            type neuron_typ = +Dropout (owl.Owl_neural.S.Graph.Neuron.Dropout)

                                                            Module Neuron.Dropout

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Dropout.neuron_typ = {
                                                            1. mutable rate : float;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : float -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Embedding/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Embedding/index.html index f4fdfeb55..db6e3c3f4 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Embedding/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Embedding/index.html @@ -1,4 +1,4 @@ -Embedding (owl.Owl_neural.S.Graph.Neuron.Embedding)

                                                            Module Neuron.Embedding

                                                            type neuron_typ = +Embedding (owl.Owl_neural.S.Graph.Neuron.Embedding)

                                                            Module Neuron.Embedding

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Embedding.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable init_typ : Init.typ;
                                                            3. mutable in_dim : int;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Flatten/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Flatten/index.html index 29b64db59..e833673df 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Flatten/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Flatten/index.html @@ -1,4 +1,4 @@ -Flatten (owl.Owl_neural.S.Graph.Neuron.Flatten)

                                                            Module Neuron.Flatten

                                                            type neuron_typ = +Flatten (owl.Owl_neural.S.Graph.Neuron.Flatten)

                                                            Module Neuron.Flatten

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Flatten.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/FullyConnected/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/FullyConnected/index.html index 14b8bfce3..ed168ae57 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/FullyConnected/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/FullyConnected/index.html @@ -1,4 +1,4 @@ -FullyConnected (owl.Owl_neural.S.Graph.Neuron.FullyConnected)

                                                            Module Neuron.FullyConnected

                                                            type neuron_typ = +FullyConnected (owl.Owl_neural.S.Graph.Neuron.FullyConnected)

                                                            Module Neuron.FullyConnected

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.FullyConnected.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable init_typ : Init.typ;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/GRU/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/GRU/index.html index 3cf26e8aa..0641d1d7d 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/GRU/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/GRU/index.html @@ -1,4 +1,4 @@ -GRU (owl.Owl_neural.S.Graph.Neuron.GRU)

                                                            Module Neuron.GRU

                                                            type neuron_typ = +GRU (owl.Owl_neural.S.Graph.Neuron.GRU)

                                                            Module Neuron.GRU

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.GRU.neuron_typ = {
                                                            1. mutable wxz : Optimise.Algodiff.t;
                                                            2. mutable whz : Optimise.Algodiff.t;
                                                            3. mutable wxr : Optimise.Algodiff.t;
                                                            4. mutable whr : Optimise.Algodiff.t;
                                                            5. mutable wxh : Optimise.Algodiff.t;
                                                            6. mutable whh : Optimise.Algodiff.t;
                                                            7. mutable bz : Optimise.Algodiff.t;
                                                            8. mutable br : Optimise.Algodiff.t;
                                                            9. mutable bh : Optimise.Algodiff.t;
                                                            10. mutable h : Optimise.Algodiff.t;
                                                            11. mutable init_typ : Init.typ;
                                                            12. mutable in_shape : int array;
                                                            13. mutable out_shape : int array;
                                                            }
                                                            val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/GaussianDropout/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/GaussianDropout/index.html index 7e7e37ef9..264c9ad3b 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/GaussianDropout/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/GaussianDropout/index.html @@ -1,4 +1,4 @@ -GaussianDropout (owl.Owl_neural.S.Graph.Neuron.GaussianDropout)

                                                            Module Neuron.GaussianDropout

                                                            type neuron_typ = +GaussianDropout (owl.Owl_neural.S.Graph.Neuron.GaussianDropout)

                                                            Module Neuron.GaussianDropout

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.GaussianDropout.neuron_typ = {
                                                            1. mutable rate : float;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : float -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/GaussianNoise/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/GaussianNoise/index.html index 8e285f8ef..187d0dff4 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/GaussianNoise/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/GaussianNoise/index.html @@ -1,4 +1,4 @@ -GaussianNoise (owl.Owl_neural.S.Graph.Neuron.GaussianNoise)

                                                            Module Neuron.GaussianNoise

                                                            type neuron_typ = +GaussianNoise (owl.Owl_neural.S.Graph.Neuron.GaussianNoise)

                                                            Module Neuron.GaussianNoise

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.GaussianNoise.neuron_typ = {
                                                            1. mutable sigma : float;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : float -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/GlobalAvgPool1D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/GlobalAvgPool1D/index.html index 9778256fc..be6ed98d2 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/GlobalAvgPool1D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/GlobalAvgPool1D/index.html @@ -1,4 +1,4 @@ -GlobalAvgPool1D (owl.Owl_neural.S.Graph.Neuron.GlobalAvgPool1D)

                                                            Module Neuron.GlobalAvgPool1D

                                                            type neuron_typ = +GlobalAvgPool1D (owl.Owl_neural.S.Graph.Neuron.GlobalAvgPool1D)

                                                            Module Neuron.GlobalAvgPool1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.GlobalAvgPool1D.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/GlobalAvgPool2D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/GlobalAvgPool2D/index.html index 60be2c3b7..dd277f1b2 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/GlobalAvgPool2D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/GlobalAvgPool2D/index.html @@ -1,4 +1,4 @@ -GlobalAvgPool2D (owl.Owl_neural.S.Graph.Neuron.GlobalAvgPool2D)

                                                            Module Neuron.GlobalAvgPool2D

                                                            type neuron_typ = +GlobalAvgPool2D (owl.Owl_neural.S.Graph.Neuron.GlobalAvgPool2D)

                                                            Module Neuron.GlobalAvgPool2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.GlobalAvgPool2D.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/GlobalMaxPool1D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/GlobalMaxPool1D/index.html index 15476be69..c0332495b 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/GlobalMaxPool1D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/GlobalMaxPool1D/index.html @@ -1,4 +1,4 @@ -GlobalMaxPool1D (owl.Owl_neural.S.Graph.Neuron.GlobalMaxPool1D)

                                                            Module Neuron.GlobalMaxPool1D

                                                            type neuron_typ = +GlobalMaxPool1D (owl.Owl_neural.S.Graph.Neuron.GlobalMaxPool1D)

                                                            Module Neuron.GlobalMaxPool1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.GlobalMaxPool1D.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/GlobalMaxPool2D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/GlobalMaxPool2D/index.html index adadb1cfd..7ee7b0e18 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/GlobalMaxPool2D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/GlobalMaxPool2D/index.html @@ -1,4 +1,4 @@ -GlobalMaxPool2D (owl.Owl_neural.S.Graph.Neuron.GlobalMaxPool2D)

                                                            Module Neuron.GlobalMaxPool2D

                                                            type neuron_typ = +GlobalMaxPool2D (owl.Owl_neural.S.Graph.Neuron.GlobalMaxPool2D)

                                                            Module Neuron.GlobalMaxPool2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.GlobalMaxPool2D.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Init/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Init/index.html index 3f627535b..c8ca48413 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Init/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Init/index.html @@ -1,4 +1,4 @@ -Init (owl.Owl_neural.S.Graph.Neuron.Init)

                                                            Module Neuron.Init

                                                            type typ = +Init (owl.Owl_neural.S.Graph.Neuron.Init)

                                                            Module Neuron.Init

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Init.typ =
                                                            1. | Uniform of float * float
                                                            2. | Gaussian of float * float
                                                            3. | Standard
                                                            4. | Tanh
                                                            5. | GlorotNormal
                                                            6. | GlorotUniform
                                                            7. | LecunNormal
                                                            8. | HeNormal
                                                            9. | Custom of int array -> Optimise.Algodiff.t
                                                            val calc_fans : int array -> float * float
                                                            val run : typ -> int array -> Optimise.Algodiff.t -> Optimise.Algodiff.t
                                                            val to_string : typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Input/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Input/index.html index fcc39942c..16203811d 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Input/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Input/index.html @@ -1,4 +1,4 @@ -Input (owl.Owl_neural.S.Graph.Neuron.Input)

                                                            Module Neuron.Input

                                                            type neuron_typ = +Input (owl.Owl_neural.S.Graph.Neuron.Input)

                                                            Module Neuron.Input

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Input.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : int array -> neuron_typ
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/LSTM/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/LSTM/index.html index 683955395..926acc61c 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/LSTM/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/LSTM/index.html @@ -1,4 +1,4 @@ -LSTM (owl.Owl_neural.S.Graph.Neuron.LSTM)

                                                            Module Neuron.LSTM

                                                            type neuron_typ = +LSTM (owl.Owl_neural.S.Graph.Neuron.LSTM)

                                                            Module Neuron.LSTM

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.LSTM.neuron_typ = {
                                                            1. mutable wxi : Optimise.Algodiff.t;
                                                            2. mutable whi : Optimise.Algodiff.t;
                                                            3. mutable wxc : Optimise.Algodiff.t;
                                                            4. mutable whc : Optimise.Algodiff.t;
                                                            5. mutable wxf : Optimise.Algodiff.t;
                                                            6. mutable whf : Optimise.Algodiff.t;
                                                            7. mutable wxo : Optimise.Algodiff.t;
                                                            8. mutable who : Optimise.Algodiff.t;
                                                            9. mutable bi : Optimise.Algodiff.t;
                                                            10. mutable bc : Optimise.Algodiff.t;
                                                            11. mutable bf : Optimise.Algodiff.t;
                                                            12. mutable bo : Optimise.Algodiff.t;
                                                            13. mutable c : Optimise.Algodiff.t;
                                                            14. mutable h : Optimise.Algodiff.t;
                                                            15. mutable init_typ : Init.typ;
                                                            16. mutable in_shape : int array;
                                                            17. mutable out_shape : int array;
                                                            }
                                                            val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Lambda/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Lambda/index.html index 2994a4300..bd5e4f555 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Lambda/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Lambda/index.html @@ -1,5 +1,5 @@ -Lambda (owl.Owl_neural.S.Graph.Neuron.Lambda)

                                                            Module Neuron.Lambda

                                                            type neuron_typ = +Lambda (owl.Owl_neural.S.Graph.Neuron.Lambda)

                                                            Module Neuron.Lambda

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Lambda.neuron_typ = {
                                                            1. mutable lambda : Optimise.Algodiff.t -> Optimise.Algodiff.t;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : ?out_shape:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/LambdaArray/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/LambdaArray/index.html index 60f16cce4..1b7a2e3f2 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/LambdaArray/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/LambdaArray/index.html @@ -1,5 +1,5 @@ -LambdaArray (owl.Owl_neural.S.Graph.Neuron.LambdaArray)

                                                            Module Neuron.LambdaArray

                                                            type neuron_typ = +LambdaArray (owl.Owl_neural.S.Graph.Neuron.LambdaArray)

                                                            Module Neuron.LambdaArray

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.LambdaArray.neuron_typ = {
                                                            1. mutable lambda : Optimise.Algodiff.t array -> Optimise.Algodiff.t;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Linear/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Linear/index.html index f88069037..8c85d5859 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Linear/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Linear/index.html @@ -1,4 +1,4 @@ -Linear (owl.Owl_neural.S.Graph.Neuron.Linear)

                                                            Module Neuron.Linear

                                                            type neuron_typ = +Linear (owl.Owl_neural.S.Graph.Neuron.Linear)

                                                            Module Neuron.Linear

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Linear.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable init_typ : Init.typ;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/LinearNoBias/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/LinearNoBias/index.html index 9e95af8c8..a6ba8227c 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/LinearNoBias/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/LinearNoBias/index.html @@ -1,4 +1,4 @@ -LinearNoBias (owl.Owl_neural.S.Graph.Neuron.LinearNoBias)

                                                            Module Neuron.LinearNoBias

                                                            type neuron_typ = +LinearNoBias (owl.Owl_neural.S.Graph.Neuron.LinearNoBias)

                                                            Module Neuron.LinearNoBias

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.LinearNoBias.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable init_typ : Init.typ;
                                                            3. mutable in_shape : int array;
                                                            4. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int -> int -> Init.typ -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val init : neuron_typ -> unit
                                                            val reset : neuron_typ -> unit
                                                            val mktag : int -> neuron_typ -> unit
                                                            val mkpar : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkpri : neuron_typ -> Optimise.Algodiff.t array
                                                            val mkadj : neuron_typ -> Optimise.Algodiff.t array
                                                            val update : neuron_typ -> Optimise.Algodiff.t array -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Masking/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Masking/index.html index 34c2cfe7a..65749146f 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Masking/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Masking/index.html @@ -1,2 +1,2 @@ -Masking (owl.Owl_neural.S.Graph.Neuron.Masking)

                                                            Module Neuron.Masking

                                                            +Masking (owl.Owl_neural.S.Graph.Neuron.Masking)

                                                            Module Neuron.Masking

                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Max/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Max/index.html index f3bf49b48..e8464d5ff 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Max/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Max/index.html @@ -1,4 +1,4 @@ -Max (owl.Owl_neural.S.Graph.Neuron.Max)

                                                            Module Neuron.Max

                                                            type neuron_typ = +Max (owl.Owl_neural.S.Graph.Neuron.Max)

                                                            Module Neuron.Max

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Max.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/MaxPool1D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/MaxPool1D/index.html index 1c6aa47a2..35ed2cdc7 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/MaxPool1D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/MaxPool1D/index.html @@ -1,4 +1,4 @@ -MaxPool1D (owl.Owl_neural.S.Graph.Neuron.MaxPool1D)

                                                            Module Neuron.MaxPool1D

                                                            type neuron_typ = +MaxPool1D (owl.Owl_neural.S.Graph.Neuron.MaxPool1D)

                                                            Module Neuron.MaxPool1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.MaxPool1D.neuron_typ = {
                                                            1. mutable padding : Owl_types.padding;
                                                            2. mutable kernel : int array;
                                                            3. mutable stride : int array;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/MaxPool2D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/MaxPool2D/index.html index d7fb57003..131371c3f 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/MaxPool2D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/MaxPool2D/index.html @@ -1,4 +1,4 @@ -MaxPool2D (owl.Owl_neural.S.Graph.Neuron.MaxPool2D)

                                                            Module Neuron.MaxPool2D

                                                            type neuron_typ = +MaxPool2D (owl.Owl_neural.S.Graph.Neuron.MaxPool2D)

                                                            Module Neuron.MaxPool2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.MaxPool2D.neuron_typ = {
                                                            1. mutable padding : Owl_types.padding;
                                                            2. mutable kernel : int array;
                                                            3. mutable stride : int array;
                                                            4. mutable in_shape : int array;
                                                            5. mutable out_shape : int array;
                                                            }
                                                            val create : Owl_types.padding -> int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Mul/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Mul/index.html index 3830536d5..beaa39fda 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Mul/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Mul/index.html @@ -1,4 +1,4 @@ -Mul (owl.Owl_neural.S.Graph.Neuron.Mul)

                                                            Module Neuron.Mul

                                                            type neuron_typ = +Mul (owl.Owl_neural.S.Graph.Neuron.Mul)

                                                            Module Neuron.Mul

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Mul.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : unit -> neuron_typ
                                                            val connect : int array array -> neuron_typ -> unit
                                                            val copy : 'a -> neuron_typ
                                                            val run : Optimise.Algodiff.t array -> 'a -> Optimise.Algodiff.t
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Normalisation/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Normalisation/index.html index 8c8301f21..aeb5feeb1 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Normalisation/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Normalisation/index.html @@ -1,5 +1,5 @@ -Normalisation (owl.Owl_neural.S.Graph.Neuron.Normalisation)

                                                            Module Neuron.Normalisation

                                                            type neuron_typ = +Normalisation (owl.Owl_neural.S.Graph.Neuron.Normalisation)

                                                            Module Neuron.Normalisation

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Normalisation.neuron_typ = {
                                                            1. mutable axis : int;
                                                            2. mutable beta : Optimise.Algodiff.t;
                                                            3. mutable gamma : Optimise.Algodiff.t;
                                                            4. mutable mu : Optimise.Algodiff.t;
                                                            5. mutable var : Optimise.Algodiff.t;
                                                            6. mutable decay : Optimise.Algodiff.t;
                                                            7. mutable training : bool;
                                                            8. mutable in_shape : int array;
                                                            9. mutable out_shape : int array;
                                                            }
                                                            val create : ?training:bool -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html index 396fdf370..8266f0118 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html index f07e8fc5a..2426c8df1 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html index a36832c5b..71ca56421 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/index.html index 654b60328..094349c0d 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            type arr = +A (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Arr/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Arr/index.html index c93a017f0..c11d06b89 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Arr/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/index.html index 7d828aca5..8deb41266 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html index 9a1fd583f..7954f3cec 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html index 266846740..2a9b97164 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html index 77d1cdbcd..c50e5359a 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 5cf6908b8..d0f05426e 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html index c6b2789fe..bcdad1399 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html index 3d8616e10..319fe9e17 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Linalg/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Linalg/index.html index 2ac917b69..fe57df18c 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Mat/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Mat/index.html index e07664d36..bada484cd 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Mat/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Maths/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Maths/index.html index 4e3ad7034..80fbb71dd 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Maths/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/NN/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/NN/index.html index 6ac130251..d45c3a6d0 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/NN/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/index.html index eb03567d6..71e4bcaa8 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Algodiff/index.html @@ -1,4 +1,4 @@ -Algodiff (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = +Algodiff (owl.Owl_neural.S.Graph.Neuron.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Algodiff.t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Batch/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Batch/index.html index 58741c46a..64a868f90 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Batch/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Batch/index.html @@ -1,4 +1,4 @@ -Batch (owl.Owl_neural.S.Graph.Neuron.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = +Batch (owl.Owl_neural.S.Graph.Neuron.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Checkpoint/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Checkpoint/index.html index 9006910b5..04ab53d57 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Checkpoint/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl.Owl_neural.S.Graph.Neuron.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = +Checkpoint (owl.Owl_neural.S.Graph.Neuron.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Checkpoint.state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }
                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Checkpoint.typ = diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Clipping/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Clipping/index.html index 4543ba671..cc2a4e115 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Clipping/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Clipping/index.html @@ -1,4 +1,4 @@ -Clipping (owl.Owl_neural.S.Graph.Neuron.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            type typ = +Clipping (owl.Owl_neural.S.Graph.Neuron.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Clipping.typ =
                                                            1. | L2norm of float
                                                            2. | Value of float * float
                                                            3. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Gradient/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Gradient/index.html index 8571774df..69292af03 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Gradient/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_neural.S.Graph.Neuron.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = +Gradient (owl.Owl_neural.S.Graph.Neuron.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Gradient.typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton
                                                            val run : typ -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Learning_Rate/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Learning_Rate/index.html index 72fcbc764..c74721cba 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Learning_Rate/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl.Owl_neural.S.Graph.Neuron.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = +Learning_Rate (owl.Owl_neural.S.Graph.Neuron.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Learning_Rate.typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                            val default : typ -> typ
                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Loss/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Loss/index.html index d7d5b0349..2faf0f396 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Loss/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Loss/index.html @@ -1,4 +1,4 @@ -Loss (owl.Owl_neural.S.Graph.Neuron.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = +Loss (owl.Owl_neural.S.Graph.Neuron.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Momentum/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Momentum/index.html index 72fe655c9..24e21b037 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Momentum/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Momentum/index.html @@ -1,4 +1,4 @@ -Momentum (owl.Owl_neural.S.Graph.Neuron.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            type typ = +Momentum (owl.Owl_neural.S.Graph.Neuron.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Params/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Params/index.html index 960b0f47b..6111056eb 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Params/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_neural.S.Graph.Neuron.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = +Params (owl.Owl_neural.S.Graph.Neuron.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : ?batch:Batch.typ -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Regularisation/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Regularisation/index.html index 84bcdbca1..988d8bd48 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Regularisation/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl.Owl_neural.S.Graph.Neuron.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = +Regularisation (owl.Owl_neural.S.Graph.Neuron.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Optimise.Regularisation.typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Stopping/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Stopping/index.html index 6451b51ac..85f7c7838 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Stopping/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Stopping/index.html @@ -1,4 +1,4 @@ -Stopping (owl.Owl_neural.S.Graph.Neuron.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            type typ = +Stopping (owl.Owl_neural.S.Graph.Neuron.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            val run : typ -> float -> bool
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Utils/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Utils/index.html index 3fef3c405..97f347ea0 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Utils/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_neural.S.Graph.Neuron.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : +Utils (owl.Owl_neural.S.Graph.Neuron.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/index.html index 3a1eb44d1..c74136195 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl.Owl_neural.S.Graph.Neuron.Optimise)

                                                            Module Neuron.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : +Optimise (owl.Owl_neural.S.Graph.Neuron.Optimise)

                                                            Module Neuron.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Padding1D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Padding1D/index.html index b055d2928..414e79112 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Padding1D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Padding1D/index.html @@ -1,2 +1,2 @@ -Padding1D (owl.Owl_neural.S.Graph.Neuron.Padding1D)

                                                            Module Neuron.Padding1D

                                                            +Padding1D (owl.Owl_neural.S.Graph.Neuron.Padding1D)

                                                            Module Neuron.Padding1D

                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Padding2D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Padding2D/index.html index 1672aef94..bf50d2bd6 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Padding2D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Padding2D/index.html @@ -1,4 +1,4 @@ -Padding2D (owl.Owl_neural.S.Graph.Neuron.Padding2D)

                                                            Module Neuron.Padding2D

                                                            type neuron_typ = +Padding2D (owl.Owl_neural.S.Graph.Neuron.Padding2D)

                                                            Module Neuron.Padding2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Padding2D.neuron_typ = {
                                                            1. mutable padding : int array array;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : int array array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Padding3D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Padding3D/index.html index 436c4f729..664fa76c8 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Padding3D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Padding3D/index.html @@ -1,2 +1,2 @@ -Padding3D (owl.Owl_neural.S.Graph.Neuron.Padding3D)

                                                            Module Neuron.Padding3D

                                                            +Padding3D (owl.Owl_neural.S.Graph.Neuron.Padding3D)

                                                            Module Neuron.Padding3D

                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Recurrent/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Recurrent/index.html index 02da02da7..a49f17815 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Recurrent/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Recurrent/index.html @@ -1,5 +1,5 @@ -Recurrent (owl.Owl_neural.S.Graph.Neuron.Recurrent)

                                                            Module Neuron.Recurrent

                                                            type neuron_typ = +Recurrent (owl.Owl_neural.S.Graph.Neuron.Recurrent)

                                                            Module Neuron.Recurrent

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Recurrent.neuron_typ = {
                                                            1. mutable whh : Optimise.Algodiff.t;
                                                            2. mutable wxh : Optimise.Algodiff.t;
                                                            3. mutable why : Optimise.Algodiff.t;
                                                            4. mutable bh : Optimise.Algodiff.t;
                                                            5. mutable by : Optimise.Algodiff.t;
                                                            6. mutable h : Optimise.Algodiff.t;
                                                            7. mutable hiddens : int;
                                                            8. mutable act : Activation.typ;
                                                            9. mutable init_typ : Init.typ;
                                                            10. mutable in_shape : int array;
                                                            11. mutable out_shape : int array;
                                                            }
                                                            val create : ?time_steps:int -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Reshape/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Reshape/index.html index d16016382..cdf97dc79 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Reshape/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Reshape/index.html @@ -1,4 +1,4 @@ -Reshape (owl.Owl_neural.S.Graph.Neuron.Reshape)

                                                            Module Neuron.Reshape

                                                            type neuron_typ = +Reshape (owl.Owl_neural.S.Graph.Neuron.Reshape)

                                                            Module Neuron.Reshape

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Reshape.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/Slice/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/Slice/index.html index 68b77cc16..65433762d 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/Slice/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/Slice/index.html @@ -1,4 +1,4 @@ -Slice (owl.Owl_neural.S.Graph.Neuron.Slice)

                                                            Module Neuron.Slice

                                                            type neuron_typ = +Slice (owl.Owl_neural.S.Graph.Neuron.Slice)

                                                            Module Neuron.Slice

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.Slice.neuron_typ = {
                                                            1. mutable in_shape : int array;
                                                            2. mutable out_shape : int array;
                                                            3. mutable slice : int list list;
                                                            }
                                                            val create : int list list -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv1D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv1D/index.html index 31fc7f2c9..4c014b15d 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv1D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv1D/index.html @@ -1,5 +1,5 @@ -TransposeConv1D (owl.Owl_neural.S.Graph.Neuron.TransposeConv1D)

                                                            Module Neuron.TransposeConv1D

                                                            type neuron_typ = +TransposeConv1D (owl.Owl_neural.S.Graph.Neuron.TransposeConv1D)

                                                            Module Neuron.TransposeConv1D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.TransposeConv1D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv2D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv2D/index.html index 1c4245cfa..d0b77b920 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv2D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv2D/index.html @@ -1,5 +1,5 @@ -TransposeConv2D (owl.Owl_neural.S.Graph.Neuron.TransposeConv2D)

                                                            Module Neuron.TransposeConv2D

                                                            type neuron_typ = +TransposeConv2D (owl.Owl_neural.S.Graph.Neuron.TransposeConv2D)

                                                            Module Neuron.TransposeConv2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.TransposeConv2D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv3D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv3D/index.html index b3edcd197..4738a7c24 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv3D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/TransposeConv3D/index.html @@ -1,5 +1,5 @@ -TransposeConv3D (owl.Owl_neural.S.Graph.Neuron.TransposeConv3D)

                                                            Module Neuron.TransposeConv3D

                                                            type neuron_typ = +TransposeConv3D (owl.Owl_neural.S.Graph.Neuron.TransposeConv3D)

                                                            Module Neuron.TransposeConv3D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.TransposeConv3D.neuron_typ = {
                                                            1. mutable w : Optimise.Algodiff.t;
                                                            2. mutable b : Optimise.Algodiff.t;
                                                            3. mutable kernel : int array;
                                                            4. mutable stride : int array;
                                                            5. mutable padding : Owl_types.padding;
                                                            6. mutable init_typ : Init.typ;
                                                            7. mutable in_shape : int array;
                                                            8. mutable out_shape : int array;
                                                            }
                                                            val create : ?inputs:int array -> diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling1D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling1D/index.html index 6f9611bff..171a15a24 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling1D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling1D/index.html @@ -1,2 +1,2 @@ -UpSampling1D (owl.Owl_neural.S.Graph.Neuron.UpSampling1D)

                                                            Module Neuron.UpSampling1D

                                                            +UpSampling1D (owl.Owl_neural.S.Graph.Neuron.UpSampling1D)

                                                            Module Neuron.UpSampling1D

                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling2D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling2D/index.html index 73ef6d908..26c6a768b 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling2D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling2D/index.html @@ -1,4 +1,4 @@ -UpSampling2D (owl.Owl_neural.S.Graph.Neuron.UpSampling2D)

                                                            Module Neuron.UpSampling2D

                                                            type neuron_typ = +UpSampling2D (owl.Owl_neural.S.Graph.Neuron.UpSampling2D)

                                                            Module Neuron.UpSampling2D

                                                            type neuron_typ = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.UpSampling2D.neuron_typ = {
                                                            1. mutable size : int array;
                                                            2. mutable in_shape : int array;
                                                            3. mutable out_shape : int array;
                                                            }
                                                            val create : int array -> neuron_typ
                                                            val connect : int array -> neuron_typ -> unit
                                                            val copy : neuron_typ -> neuron_typ
                                                            val to_string : neuron_typ -> string
                                                            val to_name : unit -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling3D/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling3D/index.html index 370649ea1..0aff9ed27 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling3D/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/UpSampling3D/index.html @@ -1,2 +1,2 @@ -UpSampling3D (owl.Owl_neural.S.Graph.Neuron.UpSampling3D)

                                                            Module Neuron.UpSampling3D

                                                            +UpSampling3D (owl.Owl_neural.S.Graph.Neuron.UpSampling3D)

                                                            Module Neuron.UpSampling3D

                                                            diff --git a/docs/owl/Owl_neural/S/Graph/Neuron/index.html b/docs/owl/Owl_neural/S/Graph/Neuron/index.html index 7874aa07c..107a10e8e 100644 --- a/docs/owl/Owl_neural/S/Graph/Neuron/index.html +++ b/docs/owl/Owl_neural/S/Graph/Neuron/index.html @@ -1,4 +1,4 @@ -Neuron (owl.Owl_neural.S.Graph.Neuron)

                                                            Module Graph.Neuron

                                                            module Optimise : sig ... end
                                                            module Init : sig ... end
                                                            module Input : sig ... end
                                                            module Activation : sig ... end
                                                            module Linear : sig ... end
                                                            module LinearNoBias : sig ... end
                                                            module Recurrent : sig ... end
                                                            module LSTM : sig ... end
                                                            module GRU : sig ... end
                                                            module Conv1D : sig ... end
                                                            module Conv2D : sig ... end
                                                            module Conv3D : sig ... end
                                                            module DilatedConv1D : sig ... end
                                                            module DilatedConv2D : sig ... end
                                                            module DilatedConv3D : sig ... end
                                                            module TransposeConv1D : sig ... end
                                                            module TransposeConv2D : sig ... end
                                                            module TransposeConv3D : sig ... end
                                                            module FullyConnected : sig ... end
                                                            module MaxPool1D : sig ... end
                                                            module MaxPool2D : sig ... end
                                                            module AvgPool1D : sig ... end
                                                            module AvgPool2D : sig ... end
                                                            module GlobalMaxPool1D : sig ... end
                                                            module GlobalMaxPool2D : sig ... end
                                                            module GlobalAvgPool1D : sig ... end
                                                            module GlobalAvgPool2D : sig ... end
                                                            module UpSampling1D : sig ... end
                                                            module UpSampling2D : sig ... end
                                                            module UpSampling3D : sig ... end
                                                            module Padding1D : sig ... end
                                                            module Padding2D : sig ... end
                                                            module Padding3D : sig ... end
                                                            module Lambda : sig ... end
                                                            module LambdaArray : sig ... end
                                                            module Dropout : sig ... end
                                                            module Reshape : sig ... end
                                                            module Flatten : sig ... end
                                                            module Slice : sig ... end
                                                            module Add : sig ... end
                                                            module Mul : sig ... end
                                                            module Dot : sig ... end
                                                            module Max : sig ... end
                                                            module Average : sig ... end
                                                            module Concatenate : sig ... end
                                                            module Normalisation : sig ... end
                                                            module GaussianNoise : sig ... end
                                                            module GaussianDropout : sig ... end
                                                            module AlphaDropout : sig ... end
                                                            module Embedding : sig ... end
                                                            module Masking : sig ... end
                                                            type neuron = +Neuron (owl.Owl_neural.S.Graph.Neuron)

                                                            Module Graph.Neuron

                                                            module Optimise : sig ... end
                                                            module Init : sig ... end
                                                            module Input : sig ... end
                                                            module Activation : sig ... end
                                                            module Linear : sig ... end
                                                            module LinearNoBias : sig ... end
                                                            module Recurrent : sig ... end
                                                            module LSTM : sig ... end
                                                            module GRU : sig ... end
                                                            module Conv1D : sig ... end
                                                            module Conv2D : sig ... end
                                                            module Conv3D : sig ... end
                                                            module DilatedConv1D : sig ... end
                                                            module DilatedConv2D : sig ... end
                                                            module DilatedConv3D : sig ... end
                                                            module TransposeConv1D : sig ... end
                                                            module TransposeConv2D : sig ... end
                                                            module TransposeConv3D : sig ... end
                                                            module FullyConnected : sig ... end
                                                            module MaxPool1D : sig ... end
                                                            module MaxPool2D : sig ... end
                                                            module AvgPool1D : sig ... end
                                                            module AvgPool2D : sig ... end
                                                            module GlobalMaxPool1D : sig ... end
                                                            module GlobalMaxPool2D : sig ... end
                                                            module GlobalAvgPool1D : sig ... end
                                                            module GlobalAvgPool2D : sig ... end
                                                            module UpSampling1D : sig ... end
                                                            module UpSampling2D : sig ... end
                                                            module UpSampling3D : sig ... end
                                                            module Padding1D : sig ... end
                                                            module Padding2D : sig ... end
                                                            module Padding3D : sig ... end
                                                            module Lambda : sig ... end
                                                            module LambdaArray : sig ... end
                                                            module Dropout : sig ... end
                                                            module Reshape : sig ... end
                                                            module Flatten : sig ... end
                                                            module Slice : sig ... end
                                                            module Add : sig ... end
                                                            module Mul : sig ... end
                                                            module Dot : sig ... end
                                                            module Max : sig ... end
                                                            module Average : sig ... end
                                                            module Concatenate : sig ... end
                                                            module Normalisation : sig ... end
                                                            module GaussianNoise : sig ... end
                                                            module GaussianDropout : sig ... end
                                                            module AlphaDropout : sig ... end
                                                            module Embedding : sig ... end
                                                            module Masking : sig ... end
                                                            type neuron = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).Neuron.neuron =
                                                            1. | Input of Input.neuron_typ
                                                            2. | Linear of Linear.neuron_typ
                                                            3. | LinearNoBias of LinearNoBias.neuron_typ
                                                            4. | Embedding of Embedding.neuron_typ
                                                            5. | LSTM of LSTM.neuron_typ
                                                            6. | GRU of GRU.neuron_typ
                                                            7. | Recurrent of Recurrent.neuron_typ
                                                            8. | Conv1D of Conv1D.neuron_typ
                                                            9. | Conv2D of Conv2D.neuron_typ
                                                            10. | Conv3D of Conv3D.neuron_typ
                                                            11. | DilatedConv1D of DilatedConv1D.neuron_typ
                                                            12. | DilatedConv2D of DilatedConv2D.neuron_typ
                                                            13. | DilatedConv3D of DilatedConv3D.neuron_typ
                                                            14. | TransposeConv1D of TransposeConv1D.neuron_typ
                                                            15. | TransposeConv2D of TransposeConv2D.neuron_typ
                                                            16. | TransposeConv3D of TransposeConv3D.neuron_typ
                                                            17. | FullyConnected of FullyConnected.neuron_typ
                                                            18. | MaxPool1D of MaxPool1D.neuron_typ
                                                            19. | MaxPool2D of MaxPool2D.neuron_typ
                                                            20. | AvgPool1D of AvgPool1D.neuron_typ
                                                            21. | AvgPool2D of AvgPool2D.neuron_typ
                                                            22. | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
                                                            23. | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
                                                            24. | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
                                                            25. | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
                                                            26. | UpSampling2D of UpSampling2D.neuron_typ
                                                            27. | Padding2D of Padding2D.neuron_typ
                                                            28. | Dropout of Dropout.neuron_typ
                                                            29. | Reshape of Reshape.neuron_typ
                                                            30. | Flatten of Flatten.neuron_typ
                                                            31. | Slice of Slice.neuron_typ
                                                            32. | Lambda of Lambda.neuron_typ
                                                            33. | LambdaArray of LambdaArray.neuron_typ
                                                            34. | Activation of Activation.neuron_typ
                                                            35. | GaussianNoise of GaussianNoise.neuron_typ
                                                            36. | GaussianDropout of GaussianDropout.neuron_typ
                                                            37. | AlphaDropout of AlphaDropout.neuron_typ
                                                            38. | Normalisation of Normalisation.neuron_typ
                                                            39. | Add of Add.neuron_typ
                                                            40. | Mul of Mul.neuron_typ
                                                            41. | Dot of Dot.neuron_typ
                                                            42. | Max of Max.neuron_typ
                                                            43. | Average of Average.neuron_typ
                                                            44. | Concatenate of Concatenate.neuron_typ
                                                            val get_in_out_shape : neuron -> int array * int array
                                                            val get_in_shape : neuron -> int array
                                                            val get_out_shape : neuron -> int array
                                                            val connect : int array array -> neuron -> unit
                                                            val init : neuron -> unit
                                                            val reset : neuron -> unit
                                                            val mktag : int -> neuron -> unit
                                                            val mkpar : neuron -> Optimise.Algodiff.t array
                                                            val mkpri : neuron -> Optimise.Algodiff.t array
                                                            val mkadj : neuron -> Optimise.Algodiff.t array
                                                            val update : neuron -> Optimise.Algodiff.t array -> unit
                                                            val load_weights : neuron -> Optimise.Algodiff.t array -> unit
                                                            val save_weights : neuron -> Optimise.Algodiff.t array
                                                            val copy : neuron -> neuron
                                                            val to_string : neuron -> string
                                                            val to_name : neuron -> string
                                                            diff --git a/docs/owl/Owl_neural/S/Graph/index.html b/docs/owl/Owl_neural/S/Graph/index.html index dd3545b79..6b77d2c68 100644 --- a/docs/owl/Owl_neural/S/Graph/index.html +++ b/docs/owl/Owl_neural/S/Graph/index.html @@ -1,5 +1,5 @@ -Graph (owl.Owl_neural.S.Graph)

                                                            Module S.Graph

                                                            module Neuron : sig ... end
                                                            type node = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).node = {
                                                            1. mutable name : string;
                                                            2. mutable prev : node array;
                                                            3. mutable next : node array;
                                                            4. mutable neuron : Neuron.neuron;
                                                            5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                            6. mutable network : network;
                                                            7. mutable train : bool;
                                                            }
                                                            and network = +Graph (owl.Owl_neural.S.Graph)

                                                            Module S.Graph

                                                            module Neuron : sig ... end
                                                            type node = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).node = {
                                                            1. mutable name : string;
                                                            2. mutable prev : node array;
                                                            3. mutable next : node array;
                                                            4. mutable neuron : Neuron.neuron;
                                                            5. mutable output : Neuron.Optimise.Algodiff.t option;
                                                            6. mutable network : network;
                                                            7. mutable train : bool;
                                                            }
                                                            and network = Owl_neural_generic.Make_Embedded(Owl_algodiff_primal_ops.S).network = {
                                                            1. mutable nnid : string;
                                                            2. mutable size : int;
                                                            3. mutable roots : node array;
                                                            4. mutable outputs : node array;
                                                            5. mutable topo : node array;
                                                            }
                                                            val make_network : ?nnid:string -> int -> node array -> node array -> network
                                                            val make_node : ?name:string -> diff --git a/docs/owl/Owl_neural/S/index.html b/docs/owl/Owl_neural/S/index.html index f5cc7a8e0..9491724e8 100644 --- a/docs/owl/Owl_neural/S/index.html +++ b/docs/owl/Owl_neural/S/index.html @@ -1,2 +1,2 @@ -S (owl.Owl_neural.S)

                                                            Module Owl_neural.S

                                                            include sig ... end
                                                            module Graph : sig ... end
                                                            module Optimise = Graph.Neuron.Optimise
                                                            module Init = Graph.Neuron.Init
                                                            module Activation = Graph.Neuron.Activation
                                                            module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                            +S (owl.Owl_neural.S)

                                                            Module Owl_neural.S

                                                            include sig ... end
                                                            module Graph : sig ... end
                                                            module Optimise = Graph.Neuron.Optimise
                                                            module Init = Graph.Neuron.Init
                                                            module Activation = Graph.Neuron.Activation
                                                            module Regularisation = Graph.Neuron.Optimise.Regularisation
                                                            diff --git a/docs/owl/Owl_neural/index.html b/docs/owl/Owl_neural/index.html index a3e816cfd..e8b98e41a 100644 --- a/docs/owl/Owl_neural/index.html +++ b/docs/owl/Owl_neural/index.html @@ -1,2 +1,2 @@ -Owl_neural (owl.Owl_neural)

                                                            Module Owl_neural

                                                            Single precision neural network
                                                            module S : sig ... end
                                                            Double precision neural network
                                                            module D : sig ... end
                                                            +Owl_neural (owl.Owl_neural)

                                                            Module Owl_neural

                                                            Single precision neural network
                                                            module S : sig ... end
                                                            Double precision neural network
                                                            module D : sig ... end
                                                            diff --git a/docs/owl/Owl_neural_parallel/Make/argument-1-M/index.html b/docs/owl/Owl_neural_parallel/Make/argument-1-M/index.html index 52442abee..583f0a624 100644 --- a/docs/owl/Owl_neural_parallel/Make/argument-1-M/index.html +++ b/docs/owl/Owl_neural_parallel/Make/argument-1-M/index.html @@ -1,5 +1,5 @@ -M (owl.Owl_neural_parallel.Make.M)

                                                            Parameter Make.M

                                                            type network
                                                            val mkpar : network -> Owl_algodiff.S.t array array
                                                            val init : network -> unit
                                                            val update : network -> Owl_algodiff.S.t array array -> unit
                                                            val copy : network -> network
                                                            val train_generic : +M (owl.Owl_neural_parallel.Make.M)

                                                            Parameter Make.M

                                                            type network
                                                            val mkpar : network -> Owl_algodiff.S.t array array
                                                            val init : network -> unit
                                                            val update : network -> Owl_algodiff.S.t array array -> unit
                                                            val copy : network -> network
                                                            val train_generic : ?state:Owl_optimise.S.Checkpoint.state -> ?params:Owl_optimise.S.Params.typ -> ?init_model:bool -> diff --git a/docs/owl/Owl_neural_parallel/Make/argument-2-E/index.html b/docs/owl/Owl_neural_parallel/Make/argument-2-E/index.html index d9bf74744..cb3c7d6f2 100644 --- a/docs/owl/Owl_neural_parallel/Make/argument-2-E/index.html +++ b/docs/owl/Owl_neural_parallel/Make/argument-2-E/index.html @@ -1,2 +1,2 @@ -E (owl.Owl_neural_parallel.Make.E)

                                                            Parameter Make.E

                                                            type param_context
                                                            type barrier =
                                                            1. | ASP
                                                            2. | BSP
                                                            3. | SSP
                                                            4. | PSP
                                                            val get : 'a -> 'b * int
                                                            val set : 'a -> 'b -> unit
                                                            val worker_num : unit -> int
                                                            val start : ?barrier:barrier -> string -> string -> unit
                                                            val register_barrier : (param_context Stdlib.ref -> int * string list) -> unit
                                                            val register_schedule : ('a list -> ('a * ('b * 'c) list) list) -> unit
                                                            val register_pull : (('a * 'b) list -> ('a * 'c) list) -> unit
                                                            val register_push : ('a -> ('b * 'c) list -> ('b * 'c) list) -> unit
                                                            val register_stop : (param_context Stdlib.ref -> bool) -> unit
                                                            +E (owl.Owl_neural_parallel.Make.E)

                                                            Parameter Make.E

                                                            type param_context
                                                            type barrier =
                                                            1. | ASP
                                                            2. | BSP
                                                            3. | SSP
                                                            4. | PSP
                                                            val get : 'a -> 'b * int
                                                            val set : 'a -> 'b -> unit
                                                            val worker_num : unit -> int
                                                            val start : ?barrier:barrier -> string -> string -> unit
                                                            val register_barrier : (param_context Stdlib.ref -> int * string list) -> unit
                                                            val register_schedule : ('a list -> ('a * ('b * 'c) list) list) -> unit
                                                            val register_pull : (('a * 'b) list -> ('a * 'c) list) -> unit
                                                            val register_push : ('a -> ('b * 'c) list -> ('b * 'c) list) -> unit
                                                            val register_stop : (param_context Stdlib.ref -> bool) -> unit
                                                            diff --git a/docs/owl/Owl_neural_parallel/Make/index.html b/docs/owl/Owl_neural_parallel/Make/index.html index 02c443b46..2fd45829c 100644 --- a/docs/owl/Owl_neural_parallel/Make/index.html +++ b/docs/owl/Owl_neural_parallel/Make/index.html @@ -1,5 +1,5 @@ -Make (owl.Owl_neural_parallel.Make)

                                                            Module Owl_neural_parallel.Make

                                                            Parameters

                                                            module M : ModelSig
                                                            module E : EngineSig

                                                            Signature

                                                            type task = {
                                                            1. mutable id : int;
                                                            2. mutable state : Owl_optimise.S.Checkpoint.state option;
                                                            3. mutable params : Owl_optimise.S.Params.typ;
                                                            4. mutable model : M.network;
                                                            5. mutable data_x : Owl_algodiff.S.t;
                                                            6. mutable data_y : Owl_algodiff.S.t;
                                                            }
                                                            val make_task : +Make (owl.Owl_neural_parallel.Make)

                                                            Module Owl_neural_parallel.Make

                                                            Parameters

                                                            module M : ModelSig
                                                            module E : EngineSig

                                                            Signature

                                                            type task = {
                                                            1. mutable id : int;
                                                            2. mutable state : Owl_optimise.S.Checkpoint.state option;
                                                            3. mutable params : Owl_optimise.S.Params.typ;
                                                            4. mutable model : M.network;
                                                            5. mutable data_x : Owl_algodiff.S.t;
                                                            6. mutable data_y : Owl_algodiff.S.t;
                                                            }
                                                            val make_task : int -> Owl_optimise.S.Params.typ -> M.network -> diff --git a/docs/owl/Owl_neural_parallel/index.html b/docs/owl/Owl_neural_parallel/index.html index aff2ffddd..0664dce31 100644 --- a/docs/owl/Owl_neural_parallel/index.html +++ b/docs/owl/Owl_neural_parallel/index.html @@ -1,2 +1,2 @@ -Owl_neural_parallel (owl.Owl_neural_parallel)

                                                            Module Owl_neural_parallel

                                                            Neural network: interface of parallel engine

                                                            module type EngineSig = sig ... end
                                                            module type ModelSig = sig ... end
                                                            module Make (M : ModelSig) (E : EngineSig) : sig ... end
                                                            +Owl_neural_parallel (owl.Owl_neural_parallel)

                                                            Module Owl_neural_parallel

                                                            Neural network: interface of parallel engine

                                                            module type EngineSig = sig ... end
                                                            module type ModelSig = sig ... end
                                                            module Make (M : ModelSig) (E : EngineSig) : sig ... end
                                                            diff --git a/docs/owl/Owl_neural_parallel/module-type-EngineSig/index.html b/docs/owl/Owl_neural_parallel/module-type-EngineSig/index.html index ce121f545..6cfd172d8 100644 --- a/docs/owl/Owl_neural_parallel/module-type-EngineSig/index.html +++ b/docs/owl/Owl_neural_parallel/module-type-EngineSig/index.html @@ -1,2 +1,2 @@ -EngineSig (owl.Owl_neural_parallel.EngineSig)

                                                            Module type Owl_neural_parallel.EngineSig

                                                            type param_context
                                                            type barrier =
                                                            1. | ASP
                                                            2. | BSP
                                                            3. | SSP
                                                            4. | PSP
                                                            val get : 'a -> 'b * int
                                                            val set : 'a -> 'b -> unit
                                                            val worker_num : unit -> int
                                                            val start : ?barrier:barrier -> string -> string -> unit
                                                            val register_barrier : (param_context Stdlib.ref -> int * string list) -> unit
                                                            val register_schedule : ('a list -> ('a * ('b * 'c) list) list) -> unit
                                                            val register_pull : (('a * 'b) list -> ('a * 'c) list) -> unit
                                                            val register_push : ('a -> ('b * 'c) list -> ('b * 'c) list) -> unit
                                                            val register_stop : (param_context Stdlib.ref -> bool) -> unit
                                                            +EngineSig (owl.Owl_neural_parallel.EngineSig)

                                                            Module type Owl_neural_parallel.EngineSig

                                                            type param_context
                                                            type barrier =
                                                            1. | ASP
                                                            2. | BSP
                                                            3. | SSP
                                                            4. | PSP
                                                            val get : 'a -> 'b * int
                                                            val set : 'a -> 'b -> unit
                                                            val worker_num : unit -> int
                                                            val start : ?barrier:barrier -> string -> string -> unit
                                                            val register_barrier : (param_context Stdlib.ref -> int * string list) -> unit
                                                            val register_schedule : ('a list -> ('a * ('b * 'c) list) list) -> unit
                                                            val register_pull : (('a * 'b) list -> ('a * 'c) list) -> unit
                                                            val register_push : ('a -> ('b * 'c) list -> ('b * 'c) list) -> unit
                                                            val register_stop : (param_context Stdlib.ref -> bool) -> unit
                                                            diff --git a/docs/owl/Owl_neural_parallel/module-type-ModelSig/index.html b/docs/owl/Owl_neural_parallel/module-type-ModelSig/index.html index ccc5c53e3..8b1c7ba73 100644 --- a/docs/owl/Owl_neural_parallel/module-type-ModelSig/index.html +++ b/docs/owl/Owl_neural_parallel/module-type-ModelSig/index.html @@ -1,5 +1,5 @@ -ModelSig (owl.Owl_neural_parallel.ModelSig)

                                                            Module type Owl_neural_parallel.ModelSig

                                                            type network
                                                            val mkpar : network -> Owl_algodiff.S.t array array
                                                            val init : network -> unit
                                                            val update : network -> Owl_algodiff.S.t array array -> unit
                                                            val copy : network -> network
                                                            val train_generic : +ModelSig (owl.Owl_neural_parallel.ModelSig)

                                                            Module type Owl_neural_parallel.ModelSig

                                                            type network
                                                            val mkpar : network -> Owl_algodiff.S.t array array
                                                            val init : network -> unit
                                                            val update : network -> Owl_algodiff.S.t array array -> unit
                                                            val copy : network -> network
                                                            val train_generic : ?state:Owl_optimise.S.Checkpoint.state -> ?params:Owl_optimise.S.Params.typ -> ?init_model:bool -> diff --git a/docs/owl/Owl_nlp/index.html b/docs/owl/Owl_nlp/index.html index 7c75d80c1..4de1a85c9 100644 --- a/docs/owl/Owl_nlp/index.html +++ b/docs/owl/Owl_nlp/index.html @@ -1,2 +1,2 @@ -Owl_nlp (owl.Owl_nlp)

                                                            Module Owl_nlp

                                                            NLP: module aliases

                                                            module Vocabulary = Owl_nlp_vocabulary
                                                            module Corpus = Owl_nlp_corpus
                                                            module Tfidf = Owl_nlp_tfidf
                                                            module Lda = Owl_nlp_lda
                                                            module Utils = Owl_nlp_utils
                                                            module Similarity = Owl_nlp_similarity
                                                            +Owl_nlp (owl.Owl_nlp)

                                                            Module Owl_nlp

                                                            NLP: module aliases

                                                            module Vocabulary = Owl_nlp_vocabulary
                                                            module Corpus = Owl_nlp_corpus
                                                            module Tfidf = Owl_nlp_tfidf
                                                            module Lda = Owl_nlp_lda
                                                            module Utils = Owl_nlp_utils
                                                            module Similarity = Owl_nlp_similarity
                                                            diff --git a/docs/owl/Owl_nlp_corpus/index.html b/docs/owl/Owl_nlp_corpus/index.html index cd798960d..70025dcab 100644 --- a/docs/owl/Owl_nlp_corpus/index.html +++ b/docs/owl/Owl_nlp_corpus/index.html @@ -1,5 +1,5 @@ -Owl_nlp_corpus (owl.Owl_nlp_corpus)

                                                            Module Owl_nlp_corpus

                                                            NLP: Corpus module

                                                            Type definition
                                                            type t

                                                            Type of a text corpus.

                                                            Query corpus
                                                            val length : t -> int

                                                            Return the size of the corpus, i.e. number of documents.

                                                            val get : t -> int -> string

                                                            Return the ith document in the corpus.

                                                            val get_tok : t -> int -> int array

                                                            Return the ith tokenised document in the corpus.

                                                            val get_uri : t -> string

                                                            Return the path of the corpus.

                                                            val get_bin_uri : t -> string

                                                            Return the path of the binary format of corpus.

                                                            val get_bin_fh : t -> Stdlib.in_channel

                                                            Return the file handle of the binary formation of corpus.

                                                            val get_tok_uri : t -> string

                                                            Return the path of tokenised corpus.

                                                            val get_tok_fh : t -> Stdlib.in_channel

                                                            Return the file handle of the tokenised corpus.

                                                            val get_vocab_uri : t -> string

                                                            Return the path of vocabulary file associated with the corpus.

                                                            val get_vocab : t -> Owl_nlp_vocabulary.t

                                                            Return the vocabulary associated with the corpus.

                                                            val get_docid : t -> int array

                                                            Return a list of document ids which are mapped back to the original file where the corpus is built.

                                                            Iteration functions
                                                            val next : t -> string

                                                            Return the next document in the corpus.

                                                            val next_tok : t -> int array

                                                            Return the next tokenised document in the corpus.

                                                            val iteri : (int -> string -> unit) -> t -> unit

                                                            Iterate all the documents in the corpus, the index (line number) is passed in.

                                                            val iteri_tok : (int -> int array -> unit) -> t -> unit

                                                            Iterate the tokenised documents in the corpus, the index (line number) is passed in.

                                                            val mapi : (int -> string -> 'a) -> t -> 'a array

                                                            Map all the documents in a corpus into another array. The index (line number) is passed in.

                                                            val mapi_tok : (int -> 'a -> 'b) -> t -> 'b array

                                                            Map all the tokenised ocuments in a corpus into another array. The index (line number) is passed in.

                                                            val next_batch : ?size:int -> t -> string array

                                                            Return the next batch of documents in a corpus as a string array. The default size is 100.

                                                            val next_batch_tok : ?size:int -> t -> int array array

                                                            Return the next batch of tokenised documents in a corpus as a string array. The default size is 100.

                                                            val reset_iterators : t -> unit

                                                            Reset the iterator to the beginning of the corpus.

                                                            Core functions
                                                            val build : +Owl_nlp_corpus (owl.Owl_nlp_corpus)

                                                            Module Owl_nlp_corpus

                                                            NLP: Corpus module

                                                            Type definition
                                                            type t

                                                            Type of a text corpus.

                                                            Query corpus
                                                            val length : t -> int

                                                            Return the size of the corpus, i.e. number of documents.

                                                            val get : t -> int -> string

                                                            Return the ith document in the corpus.

                                                            val get_tok : t -> int -> int array

                                                            Return the ith tokenised document in the corpus.

                                                            val get_uri : t -> string

                                                            Return the path of the corpus.

                                                            val get_bin_uri : t -> string

                                                            Return the path of the binary format of corpus.

                                                            val get_bin_fh : t -> Stdlib.in_channel

                                                            Return the file handle of the binary formation of corpus.

                                                            val get_tok_uri : t -> string

                                                            Return the path of tokenised corpus.

                                                            val get_tok_fh : t -> Stdlib.in_channel

                                                            Return the file handle of the tokenised corpus.

                                                            val get_vocab_uri : t -> string

                                                            Return the path of vocabulary file associated with the corpus.

                                                            val get_vocab : t -> Owl_nlp_vocabulary.t

                                                            Return the vocabulary associated with the corpus.

                                                            val get_docid : t -> int array

                                                            Return a list of document ids which are mapped back to the original file where the corpus is built.

                                                            Iteration functions
                                                            val next : t -> string

                                                            Return the next document in the corpus.

                                                            val next_tok : t -> int array

                                                            Return the next tokenised document in the corpus.

                                                            val iteri : (int -> string -> unit) -> t -> unit

                                                            Iterate all the documents in the corpus, the index (line number) is passed in.

                                                            val iteri_tok : (int -> int array -> unit) -> t -> unit

                                                            Iterate the tokenised documents in the corpus, the index (line number) is passed in.

                                                            val mapi : (int -> string -> 'a) -> t -> 'a array

                                                            Map all the documents in a corpus into another array. The index (line number) is passed in.

                                                            val mapi_tok : (int -> 'a -> 'b) -> t -> 'b array

                                                            Map all the tokenised ocuments in a corpus into another array. The index (line number) is passed in.

                                                            val next_batch : ?size:int -> t -> string array

                                                            Return the next batch of documents in a corpus as a string array. The default size is 100.

                                                            val next_batch_tok : ?size:int -> t -> int array array

                                                            Return the next batch of tokenised documents in a corpus as a string array. The default size is 100.

                                                            val reset_iterators : t -> unit

                                                            Reset the iterator to the beginning of the corpus.

                                                            Core functions
                                                            val build : ?docid:int array -> ?stopwords:(string, 'a) Stdlib.Hashtbl.t -> ?lo:float -> diff --git a/docs/owl/Owl_nlp_lda/index.html b/docs/owl/Owl_nlp_lda/index.html index da71e3d9e..7f2381e56 100644 --- a/docs/owl/Owl_nlp_lda/index.html +++ b/docs/owl/Owl_nlp_lda/index.html @@ -1,2 +1,2 @@ -Owl_nlp_lda (owl.Owl_nlp_lda)

                                                            Module Owl_nlp_lda

                                                            NLP: LDA module

                                                            Type definition
                                                            type lda_typ =
                                                            1. | SimpleLDA
                                                            2. | FTreeLDA
                                                            3. | LightLDA
                                                            4. | SparseLDA
                                                              (*

                                                              Type of LDA training algorithms.

                                                              *)
                                                            type model

                                                            Type of LDA model.

                                                            Core functions
                                                            val init : ?iter:int -> int -> Owl_nlp_corpus.t -> model

                                                            init ~iter k v d inits an LDA model for training. The default iteration is 100.

                                                            Parameters: * iter: number of iterations. * k: number of topics. * d: corpus.

                                                            val train : lda_typ -> model -> unit

                                                            After calling init, calling this function starts the training.

                                                            Helper functions
                                                            val show_info : model -> int -> float -> unit

                                                            Function for printing out log information, tailored for LDA training.

                                                            val include_token : model -> int -> int -> int -> unit

                                                            Include a token in model, used in training and you are not supposed to use it.

                                                            val exclude_token : model -> int -> int -> int -> unit

                                                            Exclude a token in model, used in training and you are not supposed to use it.

                                                            +Owl_nlp_lda (owl.Owl_nlp_lda)

                                                            Module Owl_nlp_lda

                                                            NLP: LDA module

                                                            Type definition
                                                            type lda_typ =
                                                            1. | SimpleLDA
                                                            2. | FTreeLDA
                                                            3. | LightLDA
                                                            4. | SparseLDA
                                                              (*

                                                              Type of LDA training algorithms.

                                                              *)
                                                            type model

                                                            Type of LDA model.

                                                            Core functions
                                                            val init : ?iter:int -> int -> Owl_nlp_corpus.t -> model

                                                            init ~iter k v d inits an LDA model for training. The default iteration is 100.

                                                            Parameters: * iter: number of iterations. * k: number of topics. * d: corpus.

                                                            val train : lda_typ -> model -> unit

                                                            After calling init, calling this function starts the training.

                                                            Helper functions
                                                            val show_info : model -> int -> float -> unit

                                                            Function for printing out log information, tailored for LDA training.

                                                            val include_token : model -> int -> int -> int -> unit

                                                            Include a token in model, used in training and you are not supposed to use it.

                                                            val exclude_token : model -> int -> int -> int -> unit

                                                            Exclude a token in model, used in training and you are not supposed to use it.

                                                            diff --git a/docs/owl/Owl_nlp_similarity/index.html b/docs/owl/Owl_nlp_similarity/index.html index 364ab4466..a4c4c9d43 100644 --- a/docs/owl/Owl_nlp_similarity/index.html +++ b/docs/owl/Owl_nlp_similarity/index.html @@ -1,2 +1,2 @@ -Owl_nlp_similarity (owl.Owl_nlp_similarity)

                                                            Module Owl_nlp_similarity

                                                            type t =
                                                            1. | Cosine
                                                            2. | Euclidean
                                                            3. | KL_D
                                                            val to_string : t -> string
                                                            val kl_distance : 'a -> 'b -> float
                                                            val cosine_distance : ('a * float) array -> ('b * float) array -> float
                                                            val inner_product : ('a * float) array -> ('b * float) array -> float
                                                            val inner_product_fast : ('a * float) array -> ('b * float) array -> float
                                                            val euclidean_distance : ('a * float) array -> ('b * float) array -> float
                                                            val distance : t -> ('a * float) array -> ('a * float) array -> float
                                                            +Owl_nlp_similarity (owl.Owl_nlp_similarity)

                                                            Module Owl_nlp_similarity

                                                            type t =
                                                            1. | Cosine
                                                            2. | Euclidean
                                                            3. | KL_D
                                                            val to_string : t -> string
                                                            val kl_distance : 'a -> 'b -> float
                                                            val cosine_distance : ('a * float) array -> ('b * float) array -> float
                                                            val inner_product : ('a * float) array -> ('b * float) array -> float
                                                            val inner_product_fast : ('a * float) array -> ('b * float) array -> float
                                                            val euclidean_distance : ('a * float) array -> ('b * float) array -> float
                                                            val distance : t -> ('a * float) array -> ('a * float) array -> float
                                                            diff --git a/docs/owl/Owl_nlp_tfidf/index.html b/docs/owl/Owl_nlp_tfidf/index.html index b34c801a8..c57cdc6a8 100644 --- a/docs/owl/Owl_nlp_tfidf/index.html +++ b/docs/owl/Owl_nlp_tfidf/index.html @@ -1,5 +1,5 @@ -Owl_nlp_tfidf (owl.Owl_nlp_tfidf)

                                                            Module Owl_nlp_tfidf

                                                            NLP: TFIDF module

                                                            Type definition
                                                            type tf_typ =
                                                            1. | Binary
                                                            2. | Count
                                                            3. | Frequency
                                                            4. | Log_norm
                                                              (*

                                                              Type of term frequency.

                                                              *)
                                                            type df_typ =
                                                            1. | Unary
                                                            2. | Idf
                                                            3. | Idf_Smooth
                                                              (*

                                                              Type of inverse document frequency.

                                                              *)
                                                            type t

                                                            Type of a TFIDF model

                                                            Query model
                                                            val length : t -> int

                                                            Size of Tfidf model, i.e. number of documents contained.

                                                            val term_freq : tf_typ -> float -> float -> float

                                                            term_freq term_count num_words calculates the term frequency weight.

                                                            val doc_freq : df_typ -> float -> float -> float

                                                            doc_freq doc_count num_docs calculates the document frequency weight.

                                                            val get_uri : t -> string

                                                            Return the path of the TFIDF model.

                                                            val get_corpus : t -> Owl_nlp_corpus.t

                                                            Return the corpus contained in TFIDF model

                                                            val vocab_len : t -> int

                                                            Return the size of the vocabulary contained in the TFIDF model.

                                                            val get_handle : t -> Stdlib.in_channel

                                                            Get the file handle associated with TFIDF model.

                                                            val doc_count_of : t -> string -> float

                                                            doc_count_of tfidf w calculate document frequency for a given word w.

                                                            val doc_count : Owl_nlp_vocabulary.t -> string -> float array * int

                                                            doc_count vocab fname count occurrency in all documents contained in the raw text corpus of file fname, for all words

                                                            val term_count : ('a, float) Stdlib.Hashtbl.t -> 'a array -> unit

                                                            term_count count doc counts the term occurrency in a document, and saves the result in count hashtbl.

                                                            val density : t -> float

                                                            Return the percentage of non-zero elements in doc-term matrix.

                                                            val doc_to_vec : +Owl_nlp_tfidf (owl.Owl_nlp_tfidf)

                                                            Module Owl_nlp_tfidf

                                                            NLP: TFIDF module

                                                            Type definition
                                                            type tf_typ =
                                                            1. | Binary
                                                            2. | Count
                                                            3. | Frequency
                                                            4. | Log_norm
                                                              (*

                                                              Type of term frequency.

                                                              *)
                                                            type df_typ =
                                                            1. | Unary
                                                            2. | Idf
                                                            3. | Idf_Smooth
                                                              (*

                                                              Type of inverse document frequency.

                                                              *)
                                                            type t

                                                            Type of a TFIDF model

                                                            Query model
                                                            val length : t -> int

                                                            Size of Tfidf model, i.e. number of documents contained.

                                                            val term_freq : tf_typ -> float -> float -> float

                                                            term_freq term_count num_words calculates the term frequency weight.

                                                            val doc_freq : df_typ -> float -> float -> float

                                                            doc_freq doc_count num_docs calculates the document frequency weight.

                                                            val get_uri : t -> string

                                                            Return the path of the TFIDF model.

                                                            val get_corpus : t -> Owl_nlp_corpus.t

                                                            Return the corpus contained in TFIDF model

                                                            val vocab_len : t -> int

                                                            Return the size of the vocabulary contained in the TFIDF model.

                                                            val get_handle : t -> Stdlib.in_channel

                                                            Get the file handle associated with TFIDF model.

                                                            val doc_count_of : t -> string -> float

                                                            doc_count_of tfidf w calculate document frequency for a given word w.

                                                            val doc_count : Owl_nlp_vocabulary.t -> string -> float array * int

                                                            doc_count vocab fname count occurrency in all documents contained in the raw text corpus of file fname, for all words

                                                            val term_count : ('a, float) Stdlib.Hashtbl.t -> 'a array -> unit

                                                            term_count count doc counts the term occurrency in a document, and saves the result in count hashtbl.

                                                            val density : t -> float

                                                            Return the percentage of non-zero elements in doc-term matrix.

                                                            val doc_to_vec : (float, 'a) Stdlib.Bigarray.kind -> t -> (int * float) array -> diff --git a/docs/owl/Owl_nlp_utils/index.html b/docs/owl/Owl_nlp_utils/index.html index 581756514..73024f468 100644 --- a/docs/owl/Owl_nlp_utils/index.html +++ b/docs/owl/Owl_nlp_utils/index.html @@ -1,5 +1,5 @@ -Owl_nlp_utils (owl.Owl_nlp_utils)

                                                            Module Owl_nlp_utils

                                                            val regexp_split : Str.regexp
                                                            val _allocate_space : 'a array array -> 'a array array
                                                            val load_from_file : +Owl_nlp_utils (owl.Owl_nlp_utils)

                                                            Module Owl_nlp_utils

                                                            val regexp_split : Str.regexp
                                                            val _allocate_space : 'a array array -> 'a array array
                                                            val load_from_file : ?stopwords:(string, 'a) Stdlib.Hashtbl.t -> string -> string array array
                                                            val load_from_string : diff --git a/docs/owl/Owl_nlp_vocabulary/index.html b/docs/owl/Owl_nlp_vocabulary/index.html index 0ec8e9fd8..1748e2a89 100644 --- a/docs/owl/Owl_nlp_vocabulary/index.html +++ b/docs/owl/Owl_nlp_vocabulary/index.html @@ -1,5 +1,5 @@ -Owl_nlp_vocabulary (owl.Owl_nlp_vocabulary)

                                                            Module Owl_nlp_vocabulary

                                                            NLP: Vocabulary module

                                                            Type definition
                                                            type t

                                                            Type of vocabulary (or dictionary).

                                                            Query vocabulary
                                                            val get_w2i : t -> (string, int) Stdlib.Hashtbl.t

                                                            get_w2i v returns word -> index mapping of v.

                                                            val get_i2w : t -> (int, string) Stdlib.Hashtbl.t

                                                            get_i2w v returns index -> word mapping of v.

                                                            val exits_w : t -> string -> bool

                                                            exits_w v w returns true if word w exists in the vocabulary v.

                                                            val exits_i : t -> int -> bool

                                                            exits_i i w returns true if index i exists in the vocabulary v.

                                                            val word2index : t -> string -> int

                                                            word2index v w converts word w to its index using vocabulary v.

                                                            val index2word : t -> int -> string

                                                            index2word v i converts index i to its corresponding word using vocabulary v.

                                                            Obtain properties
                                                            val length : t -> int

                                                            length v returns the size of vocabulary v.

                                                            val freq_i : t -> int -> int

                                                            freq_i v i returns the frequency of word of index i.

                                                            val freq_w : t -> string -> int

                                                            freq_w v w returns the frequency of word w in the vocabulary v.

                                                            val sort_freq : ?inc:bool -> t -> (int * int) array

                                                            sort_freq v returns the vocabulary as a (index, freq) array in increasing or decreasing frequency specified by parameter inc.

                                                            val top : t -> int -> (string * int) array

                                                            top v k returns the top k words in vocabulary v.

                                                            val bottom : t -> int -> (string * int) array

                                                            bottom v k returns the bottom k words in vocabulary v.

                                                            val re_index : t -> t

                                                            re_index v re-indexes the indices of words in vocabulary v.

                                                            Core functions
                                                            val build : +Owl_nlp_vocabulary (owl.Owl_nlp_vocabulary)

                                                            Module Owl_nlp_vocabulary

                                                            NLP: Vocabulary module

                                                            Type definition
                                                            type t

                                                            Type of vocabulary (or dictionary).

                                                            Query vocabulary
                                                            val get_w2i : t -> (string, int) Stdlib.Hashtbl.t

                                                            get_w2i v returns word -> index mapping of v.

                                                            val get_i2w : t -> (int, string) Stdlib.Hashtbl.t

                                                            get_i2w v returns index -> word mapping of v.

                                                            val exits_w : t -> string -> bool

                                                            exits_w v w returns true if word w exists in the vocabulary v.

                                                            val exits_i : t -> int -> bool

                                                            exits_i i w returns true if index i exists in the vocabulary v.

                                                            val word2index : t -> string -> int

                                                            word2index v w converts word w to its index using vocabulary v.

                                                            val index2word : t -> int -> string

                                                            index2word v i converts index i to its corresponding word using vocabulary v.

                                                            Obtain properties
                                                            val length : t -> int

                                                            length v returns the size of vocabulary v.

                                                            val freq_i : t -> int -> int

                                                            freq_i v i returns the frequency of word of index i.

                                                            val freq_w : t -> string -> int

                                                            freq_w v w returns the frequency of word w in the vocabulary v.

                                                            val sort_freq : ?inc:bool -> t -> (int * int) array

                                                            sort_freq v returns the vocabulary as a (index, freq) array in increasing or decreasing frequency specified by parameter inc.

                                                            val top : t -> int -> (string * int) array

                                                            top v k returns the top k words in vocabulary v.

                                                            val bottom : t -> int -> (string * int) array

                                                            bottom v k returns the bottom k words in vocabulary v.

                                                            val re_index : t -> t

                                                            re_index v re-indexes the indices of words in vocabulary v.

                                                            Core functions
                                                            val build : ?lo:float -> ?hi:float -> ?alphabet:bool -> diff --git a/docs/owl/Owl_optimise/D/Algodiff/A/Linalg/index.html b/docs/owl/Owl_optimise/D/Algodiff/A/Linalg/index.html index b0392c670..f8bccac47 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_optimise.D.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_optimise.D.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_optimise/D/Algodiff/A/Mat/index.html b/docs/owl/Owl_optimise/D/Algodiff/A/Mat/index.html index 8d2e521a4..df4c227b3 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_optimise.D.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_optimise.D.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/A/Scalar/index.html b/docs/owl/Owl_optimise/D/Algodiff/A/Scalar/index.html index 78df75da0..36d0d409f 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_optimise.D.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_optimise.D.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/A/index.html b/docs/owl/Owl_optimise/D/Algodiff/A/index.html index b6ae01f20..f36d2c8a9 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/A/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_optimise.D.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_optimise.D.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_optimise/D/Algodiff/Arr/index.html b/docs/owl/Owl_optimise/D/Algodiff/Arr/index.html index d99bf9147..dfc8d11c2 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Arr/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_optimise.D.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_optimise.D.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/Builder/index.html b/docs/owl/Owl_optimise/D/Algodiff/Builder/index.html index 92fd555d6..15f11ac43 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Builder/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_optimise.D.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_optimise.D.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Aiso/index.html index 7a15c784a..db7b8f8dd 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_optimise.D.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_optimise.D.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Piso/index.html index 0a8ac6727..00e105882 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_optimise.D.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_optimise.D.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Siao/index.html index aa33beef7..79726074e 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_optimise.D.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_optimise.D.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Sipo/index.html index ff2844ba9..36c3b9245 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_optimise.D.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_optimise.D.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Siso/index.html index 890ce4563..d10050125 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_optimise.D.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_optimise.D.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Sito/index.html index b93254214..4fe917a83 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_optimise.D.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_optimise.D.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_optimise/D/Algodiff/Linalg/index.html b/docs/owl/Owl_optimise/D/Algodiff/Linalg/index.html index 88e476bf7..64c6c26fb 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_optimise.D.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_optimise.D.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_optimise/D/Algodiff/Mat/index.html b/docs/owl/Owl_optimise/D/Algodiff/Mat/index.html index a56f0e788..755c87d5e 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Mat/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_optimise.D.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_optimise.D.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/Maths/index.html b/docs/owl/Owl_optimise/D/Algodiff/Maths/index.html index 4b690f2c6..45697f1a0 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/Maths/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_optimise.D.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_optimise.D.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_optimise/D/Algodiff/NN/index.html b/docs/owl/Owl_optimise/D/Algodiff/NN/index.html index 8c8bde1f4..e78a075bb 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/NN/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_optimise.D.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_optimise.D.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_optimise/D/Algodiff/index.html b/docs/owl/Owl_optimise/D/Algodiff/index.html index 21d22eed9..9c7bb0edd 100644 --- a/docs/owl/Owl_optimise/D/Algodiff/index.html +++ b/docs/owl/Owl_optimise/D/Algodiff/index.html @@ -1,2 +1,2 @@ -Algodiff (owl.Owl_optimise.D.Algodiff)

                                                            Module D.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            +Algodiff (owl.Owl_optimise.D.Algodiff)

                                                            Module D.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_optimise/D/Batch/index.html b/docs/owl/Owl_optimise/D/Batch/index.html index e201ae713..1047dc256 100644 --- a/docs/owl/Owl_optimise/D/Batch/index.html +++ b/docs/owl/Owl_optimise/D/Batch/index.html @@ -1,4 +1,4 @@ -Batch (owl.Owl_optimise.D.Batch)

                                                            Module D.Batch

                                                            type typ = +Batch (owl.Owl_optimise.D.Batch)

                                                            Module D.Batch

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/D/Checkpoint/index.html b/docs/owl/Owl_optimise/D/Checkpoint/index.html index 516079e28..d41c98c4c 100644 --- a/docs/owl/Owl_optimise/D/Checkpoint/index.html +++ b/docs/owl/Owl_optimise/D/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl.Owl_optimise.D.Checkpoint)

                                                            Module D.Checkpoint

                                                            type state = +Checkpoint (owl.Owl_optimise.D.Checkpoint)

                                                            Module D.Checkpoint

                                                            type state = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Checkpoint.state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }
                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Checkpoint.typ = diff --git a/docs/owl/Owl_optimise/D/Clipping/index.html b/docs/owl/Owl_optimise/D/Clipping/index.html index b242e6c88..514926d3a 100644 --- a/docs/owl/Owl_optimise/D/Clipping/index.html +++ b/docs/owl/Owl_optimise/D/Clipping/index.html @@ -1,4 +1,4 @@ -Clipping (owl.Owl_optimise.D.Clipping)

                                                            Module D.Clipping

                                                            type typ = +Clipping (owl.Owl_optimise.D.Clipping)

                                                            Module D.Clipping

                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/D/Gradient/index.html b/docs/owl/Owl_optimise/D/Gradient/index.html index 3e4c7acd4..78fa1ee7d 100644 --- a/docs/owl/Owl_optimise/D/Gradient/index.html +++ b/docs/owl/Owl_optimise/D/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_optimise.D.Gradient)

                                                            Module D.Gradient

                                                            type typ = +Gradient (owl.Owl_optimise.D.Gradient)

                                                            Module D.Gradient

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Gradient.typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton
                                                            val run : typ -> diff --git a/docs/owl/Owl_optimise/D/Learning_Rate/index.html b/docs/owl/Owl_optimise/D/Learning_Rate/index.html index 245e37786..da9c4452d 100644 --- a/docs/owl/Owl_optimise/D/Learning_Rate/index.html +++ b/docs/owl/Owl_optimise/D/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl.Owl_optimise.D.Learning_Rate)

                                                            Module D.Learning_Rate

                                                            type typ = +Learning_Rate (owl.Owl_optimise.D.Learning_Rate)

                                                            Module D.Learning_Rate

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Learning_Rate.typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                            val default : typ -> typ
                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/D/Loss/index.html b/docs/owl/Owl_optimise/D/Loss/index.html index c85f002c3..d570b2cc8 100644 --- a/docs/owl/Owl_optimise/D/Loss/index.html +++ b/docs/owl/Owl_optimise/D/Loss/index.html @@ -1,4 +1,4 @@ -Loss (owl.Owl_optimise.D.Loss)

                                                            Module D.Loss

                                                            type typ = +Loss (owl.Owl_optimise.D.Loss)

                                                            Module D.Loss

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/D/Momentum/index.html b/docs/owl/Owl_optimise/D/Momentum/index.html index 8fe79e3ef..b34fd92c5 100644 --- a/docs/owl/Owl_optimise/D/Momentum/index.html +++ b/docs/owl/Owl_optimise/D/Momentum/index.html @@ -1,4 +1,4 @@ -Momentum (owl.Owl_optimise.D.Momentum)

                                                            Module D.Momentum

                                                            type typ = +Momentum (owl.Owl_optimise.D.Momentum)

                                                            Module D.Momentum

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/D/Params/index.html b/docs/owl/Owl_optimise/D/Params/index.html index eccf9da7f..72d80235e 100644 --- a/docs/owl/Owl_optimise/D/Params/index.html +++ b/docs/owl/Owl_optimise/D/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_optimise.D.Params)

                                                            Module D.Params

                                                            type typ = +Params (owl.Owl_optimise.D.Params)

                                                            Module D.Params

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : ?batch:Batch.typ -> diff --git a/docs/owl/Owl_optimise/D/Regularisation/index.html b/docs/owl/Owl_optimise/D/Regularisation/index.html index 20103d017..5783c509f 100644 --- a/docs/owl/Owl_optimise/D/Regularisation/index.html +++ b/docs/owl/Owl_optimise/D/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl.Owl_optimise.D.Regularisation)

                                                            Module D.Regularisation

                                                            type typ = +Regularisation (owl.Owl_optimise.D.Regularisation)

                                                            Module D.Regularisation

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Regularisation.typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/D/Stopping/index.html b/docs/owl/Owl_optimise/D/Stopping/index.html index d719dd4dd..4a3c669f4 100644 --- a/docs/owl/Owl_optimise/D/Stopping/index.html +++ b/docs/owl/Owl_optimise/D/Stopping/index.html @@ -1,4 +1,4 @@ -Stopping (owl.Owl_optimise.D.Stopping)

                                                            Module D.Stopping

                                                            type typ = +Stopping (owl.Owl_optimise.D.Stopping)

                                                            Module D.Stopping

                                                            val run : typ -> float -> bool
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/D/Utils/index.html b/docs/owl/Owl_optimise/D/Utils/index.html index 0eb4ae4c6..67265d4c4 100644 --- a/docs/owl/Owl_optimise/D/Utils/index.html +++ b/docs/owl/Owl_optimise/D/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_optimise.D.Utils)

                                                            Module D.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : +Utils (owl.Owl_optimise.D.Utils)

                                                            Module D.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_optimise/D/index.html b/docs/owl/Owl_optimise/D/index.html index 2d7b37ec8..543d74858 100644 --- a/docs/owl/Owl_optimise/D/index.html +++ b/docs/owl/Owl_optimise/D/index.html @@ -1,5 +1,5 @@ -D (owl.Owl_optimise.D)

                                                            Module Owl_optimise.D

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : +D (owl.Owl_optimise.D)

                                                            Module Owl_optimise.D

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Linalg/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Linalg/index.html index 3d2c488f1..df96b8bd1 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_optimise.Make_Embedded.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_optimise.Make_Embedded.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Mat/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Mat/index.html index 1a69db472..5beef64a9 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_optimise.Make_Embedded.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_optimise.Make_Embedded.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Scalar/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Scalar/index.html index e2c19abd4..1d9dca9e2 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_optimise.Make_Embedded.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_optimise.Make_Embedded.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/index.html index 19eea7dcf..cbd82082d 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_optimise.Make_Embedded.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_optimise.Make_Embedded.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Arr/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Arr/index.html index 75ca886c0..60fe9dae2 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Arr/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_optimise.Make_Embedded.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_optimise.Make_Embedded.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/index.html index 00f37c837..cbab1029f 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_optimise.Make_Embedded.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_optimise.Make_Embedded.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Aiso/index.html index 25c9acb82..f8555d7b6 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Piso/index.html index 6471ff471..ee2d6c528 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Siao/index.html index 94cd79fd0..2db8ff7d6 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Sipo/index.html index 4d5756e6d..f4193254f 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Siso/index.html index da1edd017..0284bc1bc 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Sito/index.html index 8057c9f41..9009cf6a4 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_optimise.Make_Embedded.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Linalg/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Linalg/index.html index 177003ba7..45ae54243 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_optimise.Make_Embedded.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_optimise.Make_Embedded.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Mat/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Mat/index.html index f33aaf306..3f2918994 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Mat/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_optimise.Make_Embedded.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_optimise.Make_Embedded.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Maths/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Maths/index.html index 99e452423..83a26d82c 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Maths/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_optimise.Make_Embedded.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_optimise.Make_Embedded.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/NN/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/NN/index.html index 8ecc5266a..e5f8bad2a 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/NN/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_optimise.Make_Embedded.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_optimise.Make_Embedded.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/index.html b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/index.html index 0b646e1f9..e25bfc965 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Algodiff/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Algodiff/index.html @@ -1,2 +1,2 @@ -Algodiff (owl.Owl_optimise.Make_Embedded.Algodiff)

                                                            Module Make_Embedded.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(A).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            +Algodiff (owl.Owl_optimise.Make_Embedded.Algodiff)

                                                            Module Make_Embedded.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(A).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Batch/index.html b/docs/owl/Owl_optimise/Make_Embedded/Batch/index.html index 7f18d5003..e12f2bd36 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Batch/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl.Owl_optimise.Make_Embedded.Batch)

                                                            Module Make_Embedded.Batch

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            +Batch (owl.Owl_optimise.Make_Embedded.Batch)

                                                            Module Make_Embedded.Batch

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Checkpoint/index.html b/docs/owl/Owl_optimise/Make_Embedded/Checkpoint/index.html index 819e560f4..2731c5365 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Checkpoint/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl.Owl_optimise.Make_Embedded.Checkpoint)

                                                            Module Make_Embedded.Checkpoint

                                                            type state = +Checkpoint (owl.Owl_optimise.Make_Embedded.Checkpoint)

                                                            Module Make_Embedded.Checkpoint

                                                            type state = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Checkpoint.state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }
                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Checkpoint.typ = diff --git a/docs/owl/Owl_optimise/Make_Embedded/Clipping/index.html b/docs/owl/Owl_optimise/Make_Embedded/Clipping/index.html index 21f537fd0..21fb69ed7 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Clipping/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Clipping/index.html @@ -1,3 +1,3 @@ -Clipping (owl.Owl_optimise.Make_Embedded.Clipping)

                                                            Module Make_Embedded.Clipping

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Clipping.typ = +Clipping (owl.Owl_optimise.Make_Embedded.Clipping)

                                                            Module Make_Embedded.Clipping

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Clipping.typ =
                                                            1. | L2norm of float
                                                            2. | Value of float * float
                                                            3. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Gradient/index.html b/docs/owl/Owl_optimise/Make_Embedded/Gradient/index.html index 18ec15b00..d33cddb6f 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Gradient/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_optimise.Make_Embedded.Gradient)

                                                            Module Make_Embedded.Gradient

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Gradient.typ = +Gradient (owl.Owl_optimise.Make_Embedded.Gradient)

                                                            Module Make_Embedded.Gradient

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Gradient.typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton
                                                            val run : typ -> (Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/Learning_Rate/index.html b/docs/owl/Owl_optimise/Make_Embedded/Learning_Rate/index.html index 958e6b88a..954ae3bb2 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Learning_Rate/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl.Owl_optimise.Make_Embedded.Learning_Rate)

                                                            Module Make_Embedded.Learning_Rate

                                                            type typ = +Learning_Rate (owl.Owl_optimise.Make_Embedded.Learning_Rate)

                                                            Module Make_Embedded.Learning_Rate

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Learning_Rate.typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                            val default : typ -> typ
                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Loss/index.html b/docs/owl/Owl_optimise/Make_Embedded/Loss/index.html index a958ac335..88da2d8eb 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Loss/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl.Owl_optimise.Make_Embedded.Loss)

                                                            Module Make_Embedded.Loss

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            +Loss (owl.Owl_optimise.Make_Embedded.Loss)

                                                            Module Make_Embedded.Loss

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Momentum/index.html b/docs/owl/Owl_optimise/Make_Embedded/Momentum/index.html index 96274e882..9c412aac4 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Momentum/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Momentum/index.html @@ -1,3 +1,3 @@ -Momentum (owl.Owl_optimise.Make_Embedded.Momentum)

                                                            Module Make_Embedded.Momentum

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Momentum.typ = +Momentum (owl.Owl_optimise.Make_Embedded.Momentum)

                                                            Module Make_Embedded.Momentum

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Momentum.typ =
                                                            1. | Standard of float
                                                            2. | Nesterov of float
                                                            3. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Params/index.html b/docs/owl/Owl_optimise/Make_Embedded/Params/index.html index f79de958b..90a507e22 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Params/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_optimise.Make_Embedded.Params)

                                                            Module Make_Embedded.Params

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : +Params (owl.Owl_optimise.Make_Embedded.Params)

                                                            Module Make_Embedded.Params

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/Regularisation/index.html b/docs/owl/Owl_optimise/Make_Embedded/Regularisation/index.html index efc849e3c..1d21fa41f 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Regularisation/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl.Owl_optimise.Make_Embedded.Regularisation)

                                                            Module Make_Embedded.Regularisation

                                                            type typ = +Regularisation (owl.Owl_optimise.Make_Embedded.Regularisation)

                                                            Module Make_Embedded.Regularisation

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Regularisation.typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Stopping/index.html b/docs/owl/Owl_optimise/Make_Embedded/Stopping/index.html index 7037d6bed..1554e0a8d 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Stopping/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Stopping/index.html @@ -1,3 +1,3 @@ -Stopping (owl.Owl_optimise.Make_Embedded.Stopping)

                                                            Module Make_Embedded.Stopping

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Stopping.typ = +Stopping (owl.Owl_optimise.Make_Embedded.Stopping)

                                                            Module Make_Embedded.Stopping

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Stopping.typ =
                                                            1. | Const of float
                                                            2. | Early of int * int
                                                            3. | None
                                                            val run : typ -> float -> bool
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/Utils/index.html b/docs/owl/Owl_optimise/Make_Embedded/Utils/index.html index f620a2340..500de18d9 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/Utils/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_optimise.Make_Embedded.Utils)

                                                            Module Make_Embedded.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : +Utils (owl.Owl_optimise.Make_Embedded.Utils)

                                                            Module Make_Embedded.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Linalg/index.html b/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Linalg/index.html index d20e1771a..f18a597fe 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Linalg/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_optimise.Make_Embedded.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_optimise.Make_Embedded.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Mat/index.html b/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Mat/index.html index 02aa2bf9e..d29a57693 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Mat/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_optimise.Make_Embedded.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_optimise.Make_Embedded.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Scalar/index.html b/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Scalar/index.html index 73d2d74fb..be2d59afd 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Scalar/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_optimise.Make_Embedded.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_optimise.Make_Embedded.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/index.html b/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/index.html index d9611944f..9e8dd6fd0 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_optimise.Make_Embedded.A)

                                                            Parameter Make_Embedded.A

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_optimise.Make_Embedded.A)

                                                            Parameter Make_Embedded.A

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_optimise/Make_Embedded/index.html b/docs/owl/Owl_optimise/Make_Embedded/index.html index bf97624e3..6da8a2f22 100644 --- a/docs/owl/Owl_optimise/Make_Embedded/index.html +++ b/docs/owl/Owl_optimise/Make_Embedded/index.html @@ -1,5 +1,5 @@ -Make_Embedded (owl.Owl_optimise.Make_Embedded)

                                                            Module Owl_optimise.Make_Embedded

                                                            Parameters

                                                            Signature

                                                            include sig ... end
                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : +Make_Embedded (owl.Owl_optimise.Make_Embedded)

                                                            Module Owl_optimise.Make_Embedded

                                                            Parameters

                                                            Signature

                                                            include sig ... end
                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_optimise/S/Algodiff/A/Linalg/index.html b/docs/owl/Owl_optimise/S/Algodiff/A/Linalg/index.html index 6e4cb815e..fefa90b80 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_optimise.S.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_optimise.S.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_optimise/S/Algodiff/A/Mat/index.html b/docs/owl/Owl_optimise/S/Algodiff/A/Mat/index.html index c5d1195f0..eb171bc06 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_optimise.S.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_optimise.S.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/A/Scalar/index.html b/docs/owl/Owl_optimise/S/Algodiff/A/Scalar/index.html index f95d82324..640ed371d 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_optimise.S.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_optimise.S.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/A/index.html b/docs/owl/Owl_optimise/S/Algodiff/A/index.html index eff271127..7a62d29df 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/A/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_optimise.S.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_optimise.S.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_optimise/S/Algodiff/Arr/index.html b/docs/owl/Owl_optimise/S/Algodiff/Arr/index.html index ab3d41f67..398e262e4 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Arr/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_optimise.S.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_optimise.S.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/Builder/index.html b/docs/owl/Owl_optimise/S/Algodiff/Builder/index.html index 565c54821..145756e60 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Builder/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_optimise.S.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_optimise.S.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Aiso/index.html index fb11cdb5c..2e813038c 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_optimise.S.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_optimise.S.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Piso/index.html index 5cd846b36..61f630a5f 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_optimise.S.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_optimise.S.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Siao/index.html index 53ce5ff0e..5f7a90f9b 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_optimise.S.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_optimise.S.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Sipo/index.html index c1205ec69..a6192efea 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_optimise.S.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_optimise.S.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Siso/index.html index d75a92698..2ce154548 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_optimise.S.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_optimise.S.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Sito/index.html index d631cfdc1..f35373154 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_optimise.S.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_optimise.S.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_optimise/S/Algodiff/Linalg/index.html b/docs/owl/Owl_optimise/S/Algodiff/Linalg/index.html index 3543c8c10..8f3efb2d0 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_optimise.S.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_optimise.S.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_optimise/S/Algodiff/Mat/index.html b/docs/owl/Owl_optimise/S/Algodiff/Mat/index.html index a1a7218c7..64eab1bb9 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Mat/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_optimise.S.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_optimise.S.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/Maths/index.html b/docs/owl/Owl_optimise/S/Algodiff/Maths/index.html index 492db2cce..5c9e19112 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/Maths/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_optimise.S.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_optimise.S.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_optimise/S/Algodiff/NN/index.html b/docs/owl/Owl_optimise/S/Algodiff/NN/index.html index dd053fa01..c9306a8a1 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/NN/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_optimise.S.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_optimise.S.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_optimise/S/Algodiff/index.html b/docs/owl/Owl_optimise/S/Algodiff/index.html index 71443bbf3..e107435b1 100644 --- a/docs/owl/Owl_optimise/S/Algodiff/index.html +++ b/docs/owl/Owl_optimise/S/Algodiff/index.html @@ -1,2 +1,2 @@ -Algodiff (owl.Owl_optimise.S.Algodiff)

                                                            Module S.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            +Algodiff (owl.Owl_optimise.S.Algodiff)

                                                            Module S.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S).t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_optimise/S/Batch/index.html b/docs/owl/Owl_optimise/S/Batch/index.html index deeceb8ed..ca2dec080 100644 --- a/docs/owl/Owl_optimise/S/Batch/index.html +++ b/docs/owl/Owl_optimise/S/Batch/index.html @@ -1,4 +1,4 @@ -Batch (owl.Owl_optimise.S.Batch)

                                                            Module S.Batch

                                                            type typ = +Batch (owl.Owl_optimise.S.Batch)

                                                            Module S.Batch

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/S/Checkpoint/index.html b/docs/owl/Owl_optimise/S/Checkpoint/index.html index a5d2918a2..c7116594a 100644 --- a/docs/owl/Owl_optimise/S/Checkpoint/index.html +++ b/docs/owl/Owl_optimise/S/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl.Owl_optimise.S.Checkpoint)

                                                            Module S.Checkpoint

                                                            type state = +Checkpoint (owl.Owl_optimise.S.Checkpoint)

                                                            Module S.Checkpoint

                                                            type state = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Checkpoint.state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }
                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Checkpoint.typ = diff --git a/docs/owl/Owl_optimise/S/Clipping/index.html b/docs/owl/Owl_optimise/S/Clipping/index.html index 1f7446be7..2124c4713 100644 --- a/docs/owl/Owl_optimise/S/Clipping/index.html +++ b/docs/owl/Owl_optimise/S/Clipping/index.html @@ -1,4 +1,4 @@ -Clipping (owl.Owl_optimise.S.Clipping)

                                                            Module S.Clipping

                                                            type typ = +Clipping (owl.Owl_optimise.S.Clipping)

                                                            Module S.Clipping

                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/S/Gradient/index.html b/docs/owl/Owl_optimise/S/Gradient/index.html index 12bc1c0b5..faa7b8335 100644 --- a/docs/owl/Owl_optimise/S/Gradient/index.html +++ b/docs/owl/Owl_optimise/S/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_optimise.S.Gradient)

                                                            Module S.Gradient

                                                            type typ = +Gradient (owl.Owl_optimise.S.Gradient)

                                                            Module S.Gradient

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Gradient.typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton
                                                            val run : typ -> diff --git a/docs/owl/Owl_optimise/S/Learning_Rate/index.html b/docs/owl/Owl_optimise/S/Learning_Rate/index.html index 2deb4b8b1..0b24fd75a 100644 --- a/docs/owl/Owl_optimise/S/Learning_Rate/index.html +++ b/docs/owl/Owl_optimise/S/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl.Owl_optimise.S.Learning_Rate)

                                                            Module S.Learning_Rate

                                                            type typ = +Learning_Rate (owl.Owl_optimise.S.Learning_Rate)

                                                            Module S.Learning_Rate

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Learning_Rate.typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                            val default : typ -> typ
                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/S/Loss/index.html b/docs/owl/Owl_optimise/S/Loss/index.html index 90f10505a..9f9ae863e 100644 --- a/docs/owl/Owl_optimise/S/Loss/index.html +++ b/docs/owl/Owl_optimise/S/Loss/index.html @@ -1,4 +1,4 @@ -Loss (owl.Owl_optimise.S.Loss)

                                                            Module S.Loss

                                                            type typ = +Loss (owl.Owl_optimise.S.Loss)

                                                            Module S.Loss

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/S/Momentum/index.html b/docs/owl/Owl_optimise/S/Momentum/index.html index 2b0438a7c..4db83e150 100644 --- a/docs/owl/Owl_optimise/S/Momentum/index.html +++ b/docs/owl/Owl_optimise/S/Momentum/index.html @@ -1,4 +1,4 @@ -Momentum (owl.Owl_optimise.S.Momentum)

                                                            Module S.Momentum

                                                            type typ = +Momentum (owl.Owl_optimise.S.Momentum)

                                                            Module S.Momentum

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/S/Params/index.html b/docs/owl/Owl_optimise/S/Params/index.html index e09b9e167..e10acfae3 100644 --- a/docs/owl/Owl_optimise/S/Params/index.html +++ b/docs/owl/Owl_optimise/S/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_optimise.S.Params)

                                                            Module S.Params

                                                            type typ = +Params (owl.Owl_optimise.S.Params)

                                                            Module S.Params

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : ?batch:Batch.typ -> diff --git a/docs/owl/Owl_optimise/S/Regularisation/index.html b/docs/owl/Owl_optimise/S/Regularisation/index.html index f90bd03b1..9258fb6b8 100644 --- a/docs/owl/Owl_optimise/S/Regularisation/index.html +++ b/docs/owl/Owl_optimise/S/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl.Owl_optimise.S.Regularisation)

                                                            Module S.Regularisation

                                                            type typ = +Regularisation (owl.Owl_optimise.S.Regularisation)

                                                            Module S.Regularisation

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Regularisation.typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/S/Stopping/index.html b/docs/owl/Owl_optimise/S/Stopping/index.html index d84e26aea..8e6986315 100644 --- a/docs/owl/Owl_optimise/S/Stopping/index.html +++ b/docs/owl/Owl_optimise/S/Stopping/index.html @@ -1,4 +1,4 @@ -Stopping (owl.Owl_optimise.S.Stopping)

                                                            Module S.Stopping

                                                            type typ = +Stopping (owl.Owl_optimise.S.Stopping)

                                                            Module S.Stopping

                                                            val run : typ -> float -> bool
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_optimise/S/Utils/index.html b/docs/owl/Owl_optimise/S/Utils/index.html index 8ccc9091f..a44d25bd1 100644 --- a/docs/owl/Owl_optimise/S/Utils/index.html +++ b/docs/owl/Owl_optimise/S/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_optimise.S.Utils)

                                                            Module S.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : +Utils (owl.Owl_optimise.S.Utils)

                                                            Module S.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_optimise/S/index.html b/docs/owl/Owl_optimise/S/index.html index 9ec305d96..125ef0e0b 100644 --- a/docs/owl/Owl_optimise/S/index.html +++ b/docs/owl/Owl_optimise/S/index.html @@ -1,5 +1,5 @@ -S (owl.Owl_optimise.S)

                                                            Module Owl_optimise.S

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : +S (owl.Owl_optimise.S)

                                                            Module Owl_optimise.S

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_optimise/index.html b/docs/owl/Owl_optimise/index.html index 6326998dc..0de295fe7 100644 --- a/docs/owl/Owl_optimise/index.html +++ b/docs/owl/Owl_optimise/index.html @@ -1,2 +1,2 @@ -Owl_optimise (owl.Owl_optimise)

                                                            Module Owl_optimise

                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            +Owl_optimise (owl.Owl_optimise)

                                                            Module Owl_optimise

                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Linalg/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Linalg/index.html index 3a21d78f0..e51d2221d 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression.D.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression.D.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Mat/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Mat/index.html index 75014b509..d8585daad 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression.D.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_regression.D.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Scalar/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Scalar/index.html index 509bc82d6..13311dabd 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_regression.D.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_regression.D.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/A/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/A/index.html index 5fb711522..7d9703154 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/A/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_regression.D.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            type arr = +A (owl.Owl_regression.D.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Arr/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Arr/index.html index 50878a69f..55569323b 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Arr/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_regression.D.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_regression.D.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/index.html index 5770019ec..3b7b598d7 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_regression.D.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_regression.D.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Aiso/index.html index ab33ac085..56ebaa4c6 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_regression.D.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_regression.D.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Piso/index.html index 601748432..a0e6459b3 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_regression.D.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_regression.D.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Siao/index.html index c5ec1f16f..69e16a6da 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_regression.D.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_regression.D.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 6c1fa5595..6feffcb67 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_regression.D.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_regression.D.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Siso/index.html index 590a60dd5..5f2970069 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_regression.D.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_regression.D.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Sito/index.html index 738ae7a29..d144bec0c 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_regression.D.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_regression.D.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Linalg/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Linalg/index.html index 48134a25e..5b6252d5a 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression.D.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression.D.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Mat/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Mat/index.html index 68e07f0cd..e2202dcf1 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Mat/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression.D.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_regression.D.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/Maths/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/Maths/index.html index 7dcb0ac81..921cda422 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/Maths/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_regression.D.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_regression.D.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/NN/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/NN/index.html index 5ca093697..8e3723bb7 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/NN/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_regression.D.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_regression.D.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_regression/D/Optimise/Algodiff/index.html b/docs/owl/Owl_regression/D/Optimise/Algodiff/index.html index c7d1975c1..698278102 100644 --- a/docs/owl/Owl_regression/D/Optimise/Algodiff/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Algodiff/index.html @@ -1,4 +1,4 @@ -Algodiff (owl.Owl_regression.D.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = +Algodiff (owl.Owl_regression.D.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Algodiff.t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Batch/index.html b/docs/owl/Owl_regression/D/Optimise/Batch/index.html index a1e4ea92d..4fb89056e 100644 --- a/docs/owl/Owl_regression/D/Optimise/Batch/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Batch/index.html @@ -1,4 +1,4 @@ -Batch (owl.Owl_regression.D.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = +Batch (owl.Owl_regression.D.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Checkpoint/index.html b/docs/owl/Owl_regression/D/Optimise/Checkpoint/index.html index 27937e4d9..0e6fc7cf9 100644 --- a/docs/owl/Owl_regression/D/Optimise/Checkpoint/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl.Owl_regression.D.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = +Checkpoint (owl.Owl_regression.D.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Checkpoint.state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }
                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Checkpoint.typ = diff --git a/docs/owl/Owl_regression/D/Optimise/Clipping/index.html b/docs/owl/Owl_regression/D/Optimise/Clipping/index.html index 3947791f2..325f31d61 100644 --- a/docs/owl/Owl_regression/D/Optimise/Clipping/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Clipping/index.html @@ -1,4 +1,4 @@ -Clipping (owl.Owl_regression.D.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            type typ = +Clipping (owl.Owl_regression.D.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Gradient/index.html b/docs/owl/Owl_regression/D/Optimise/Gradient/index.html index 0c8a0e0f8..192b7e45f 100644 --- a/docs/owl/Owl_regression/D/Optimise/Gradient/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_regression.D.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = +Gradient (owl.Owl_regression.D.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Gradient.typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton
                                                            val run : typ -> diff --git a/docs/owl/Owl_regression/D/Optimise/Learning_Rate/index.html b/docs/owl/Owl_regression/D/Optimise/Learning_Rate/index.html index 804f6535f..e7320c970 100644 --- a/docs/owl/Owl_regression/D/Optimise/Learning_Rate/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl.Owl_regression.D.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = +Learning_Rate (owl.Owl_regression.D.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Learning_Rate.typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                            val default : typ -> typ
                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Loss/index.html b/docs/owl/Owl_regression/D/Optimise/Loss/index.html index b60b5ba4b..55546c670 100644 --- a/docs/owl/Owl_regression/D/Optimise/Loss/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Loss/index.html @@ -1,4 +1,4 @@ -Loss (owl.Owl_regression.D.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = +Loss (owl.Owl_regression.D.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Momentum/index.html b/docs/owl/Owl_regression/D/Optimise/Momentum/index.html index 7159af8ef..b25c8afab 100644 --- a/docs/owl/Owl_regression/D/Optimise/Momentum/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Momentum/index.html @@ -1,4 +1,4 @@ -Momentum (owl.Owl_regression.D.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            type typ = +Momentum (owl.Owl_regression.D.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Params/index.html b/docs/owl/Owl_regression/D/Optimise/Params/index.html index 1045505ba..89e7a1127 100644 --- a/docs/owl/Owl_regression/D/Optimise/Params/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_regression.D.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = +Params (owl.Owl_regression.D.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : ?batch:Batch.typ -> diff --git a/docs/owl/Owl_regression/D/Optimise/Regularisation/index.html b/docs/owl/Owl_regression/D/Optimise/Regularisation/index.html index efa658cea..1ea29e057 100644 --- a/docs/owl/Owl_regression/D/Optimise/Regularisation/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl.Owl_regression.D.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = +Regularisation (owl.Owl_regression.D.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.D)).Regularisation.typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Stopping/index.html b/docs/owl/Owl_regression/D/Optimise/Stopping/index.html index a60738ae4..5551654b5 100644 --- a/docs/owl/Owl_regression/D/Optimise/Stopping/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Stopping/index.html @@ -1,4 +1,4 @@ -Stopping (owl.Owl_regression.D.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            type typ = +Stopping (owl.Owl_regression.D.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            val run : typ -> float -> bool
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/D/Optimise/Utils/index.html b/docs/owl/Owl_regression/D/Optimise/Utils/index.html index 98b4f0d7a..ecd664d52 100644 --- a/docs/owl/Owl_regression/D/Optimise/Utils/index.html +++ b/docs/owl/Owl_regression/D/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_regression.D.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : +Utils (owl.Owl_regression.D.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_regression/D/Optimise/index.html b/docs/owl/Owl_regression/D/Optimise/index.html index d40e795ee..0a56220af 100644 --- a/docs/owl/Owl_regression/D/Optimise/index.html +++ b/docs/owl/Owl_regression/D/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl.Owl_regression.D.Optimise)

                                                            Module D.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : +Optimise (owl.Owl_regression.D.Optimise)

                                                            Module D.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_regression/D/index.html b/docs/owl/Owl_regression/D/index.html index a69e023ea..139a18d7b 100644 --- a/docs/owl/Owl_regression/D/index.html +++ b/docs/owl/Owl_regression/D/index.html @@ -1,5 +1,5 @@ -D (owl.Owl_regression.D)

                                                            Module Owl_regression.D

                                                            module Optimise : sig ... end
                                                            val _linear_reg : +D (owl.Owl_regression.D)

                                                            Module Owl_regression.D

                                                            module Optimise : sig ... end
                                                            val _linear_reg : bool -> Optimise.Params.typ -> Optimise.Algodiff.A.arr -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Linalg/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Linalg/index.html index 4c636f1d9..5f132696b 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Mat/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Mat/index.html index e29c1c5e3..211212bf0 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Scalar/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Scalar/index.html index 904e329ca..5d86df873 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/index.html index 9eda3e67b..04a4fd62a 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            type arr = +A (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Arr/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Arr/index.html index db36cc11e..3ee5b4818 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Arr/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/index.html index 8af51a038..2dd1a6de2 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Aiso/index.html index d006dd08b..112825f19 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Piso/index.html index df88a06ed..85b441c62 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Siao/index.html index 1a03a5d7a..c880d2118 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Sipo/index.html index e5838e007..fe787364a 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Siso/index.html index 55098e23c..e751f4dbc 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Sito/index.html index d95a41252..51ed2dd8d 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Linalg/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Linalg/index.html index fbb58dc6e..ee645eb81 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Mat/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Mat/index.html index 9d51c8808..63f9a4c63 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Mat/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Maths/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Maths/index.html index fbe4355f7..6bc8add59 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Maths/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/NN/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/NN/index.html index 393652f2e..c81b36e93 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/NN/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_regression.Make_Embedded.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/index.html index e104bd284..7f762d855 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Algodiff/index.html @@ -1,2 +1,2 @@ -Algodiff (owl.Owl_regression.Make_Embedded.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Algodiff.t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            +Algodiff (owl.Owl_regression.Make_Embedded.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Algodiff.t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Batch/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Batch/index.html index ff26a4d0c..cfbbe172c 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Batch/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl.Owl_regression.Make_Embedded.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            +Batch (owl.Owl_regression.Make_Embedded.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Checkpoint/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Checkpoint/index.html index 837fa3f71..2e2af2c79 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Checkpoint/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl.Owl_regression.Make_Embedded.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = +Checkpoint (owl.Owl_regression.Make_Embedded.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Checkpoint.state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }
                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Checkpoint.typ = diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Clipping/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Clipping/index.html index dd95d4779..7d75be014 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Clipping/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Clipping/index.html @@ -1,3 +1,3 @@ -Clipping (owl.Owl_regression.Make_Embedded.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Clipping.typ = +Clipping (owl.Owl_regression.Make_Embedded.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Clipping.typ =
                                                            1. | L2norm of float
                                                            2. | Value of float * float
                                                            3. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Gradient/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Gradient/index.html index c8488a7b5..c7cb4623b 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Gradient/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_regression.Make_Embedded.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Gradient.typ = +Gradient (owl.Owl_regression.Make_Embedded.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Gradient.typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton
                                                            val run : typ -> (Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Learning_Rate/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Learning_Rate/index.html index 278ef52a3..50982ba94 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Learning_Rate/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl.Owl_regression.Make_Embedded.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = +Learning_Rate (owl.Owl_regression.Make_Embedded.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Learning_Rate.typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                            val default : typ -> typ
                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Loss/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Loss/index.html index a24d6fdf1..7c05e548c 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Loss/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl.Owl_regression.Make_Embedded.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            +Loss (owl.Owl_regression.Make_Embedded.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Momentum/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Momentum/index.html index 0056ec7a6..6920448be 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Momentum/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Momentum/index.html @@ -1,3 +1,3 @@ -Momentum (owl.Owl_regression.Make_Embedded.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Momentum.typ = +Momentum (owl.Owl_regression.Make_Embedded.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Momentum.typ =
                                                            1. | Standard of float
                                                            2. | Nesterov of float
                                                            3. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Params/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Params/index.html index c0607d6bc..835c3c3e9 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Params/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_regression.Make_Embedded.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : +Params (owl.Owl_regression.Make_Embedded.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Regularisation/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Regularisation/index.html index 1629fc026..4db99ceb2 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Regularisation/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl.Owl_regression.Make_Embedded.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = +Regularisation (owl.Owl_regression.Make_Embedded.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Regularisation.typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Stopping/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Stopping/index.html index 455575de6..e121c5bb3 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Stopping/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Stopping/index.html @@ -1,3 +1,3 @@ -Stopping (owl.Owl_regression.Make_Embedded.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Stopping.typ = +Stopping (owl.Owl_regression.Make_Embedded.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)).Stopping.typ =
                                                            1. | Const of float
                                                            2. | Early of int * int
                                                            3. | None
                                                            val run : typ -> float -> bool
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/Utils/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/Utils/index.html index eba06ab53..eb145a759 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/Utils/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_regression.Make_Embedded.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : +Utils (owl.Owl_regression.Make_Embedded.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_regression/Make_Embedded/Optimise/index.html b/docs/owl/Owl_regression/Make_Embedded/Optimise/index.html index 889e1f1a7..105368bcd 100644 --- a/docs/owl/Owl_regression/Make_Embedded/Optimise/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl.Owl_regression.Make_Embedded.Optimise)

                                                            Module Make_Embedded.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : +Optimise (owl.Owl_regression.Make_Embedded.Optimise)

                                                            Module Make_Embedded.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Linalg/index.html b/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Linalg/index.html index 6b3345d91..f213a4145 100644 --- a/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Linalg/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression.Make_Embedded.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression.Make_Embedded.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Mat/index.html b/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Mat/index.html index 1240b5027..f0b5cffbd 100644 --- a/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Mat/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression.Make_Embedded.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_regression.Make_Embedded.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Scalar/index.html b/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Scalar/index.html index 01e05492f..4d01579a2 100644 --- a/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Scalar/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/argument-1-A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_regression.Make_Embedded.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_regression.Make_Embedded.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_regression/Make_Embedded/argument-1-A/index.html b/docs/owl/Owl_regression/Make_Embedded/argument-1-A/index.html index 17382b0cd..150b65887 100644 --- a/docs/owl/Owl_regression/Make_Embedded/argument-1-A/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/argument-1-A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_regression.Make_Embedded.A)

                                                            Parameter Make_Embedded.A

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_regression.Make_Embedded.A)

                                                            Parameter Make_Embedded.A

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_regression/Make_Embedded/index.html b/docs/owl/Owl_regression/Make_Embedded/index.html index 608f955c3..1447cf0ca 100644 --- a/docs/owl/Owl_regression/Make_Embedded/index.html +++ b/docs/owl/Owl_regression/Make_Embedded/index.html @@ -1,5 +1,5 @@ -Make_Embedded (owl.Owl_regression.Make_Embedded)

                                                            Module Owl_regression.Make_Embedded

                                                            Parameters

                                                            Signature

                                                            include sig ... end
                                                            module Optimise : sig ... end
                                                            val _linear_reg : +Make_Embedded (owl.Owl_regression.Make_Embedded)

                                                            Module Owl_regression.Make_Embedded

                                                            Parameters

                                                            Signature

                                                            include sig ... end
                                                            module Optimise : sig ... end
                                                            val _linear_reg : bool -> Optimise.Params.typ -> Optimise.Algodiff.A.arr -> diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Linalg/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Linalg/index.html index ae9e29263..439c1a9bb 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression.S.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression.S.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Mat/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Mat/index.html index 67a6c6578..f43da8e5e 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression.S.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_regression.S.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Scalar/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Scalar/index.html index f206b4ceb..fa56bd5ed 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_regression.S.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_regression.S.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/A/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/A/index.html index 2ff27df4e..81cc6c516 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/A/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_regression.S.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            type arr = +A (owl.Owl_regression.S.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Arr/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Arr/index.html index af2b8df89..571b7ad1e 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Arr/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_regression.S.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_regression.S.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/index.html index ec41e8590..361856746 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_regression.S.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            +Builder (owl.Owl_regression.S.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t
                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t
                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t
                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array
                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t
                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Aiso/index.html index 5eb0f5dc9..c6f6d5c6b 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_regression.S.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_regression.S.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Piso/index.html index 4b7b198a7..077bb1918 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_regression.S.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_regression.S.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Siao/index.html index e6f15bf45..b445785a0 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_regression.S.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_regression.S.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Sipo/index.html index 666d2b867..664907af6 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_regression.S.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_regression.S.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Siso/index.html index cf1e23fcf..b3980ac41 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_regression.S.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_regression.S.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Sito/index.html index 70537b16a..c293ab662 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_regression.S.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_regression.S.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Linalg/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Linalg/index.html index 6b79e2f3a..35ae795f7 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression.S.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression.S.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t
                                                            val logdet : t -> t
                                                            val chol : ?upper:bool -> t -> t
                                                            val qr : t -> t * t
                                                            val lq : t -> t * t
                                                            val svd : ?thin:bool -> t -> t * t * t
                                                            val sylvester : t -> t -> t -> t
                                                            val lyapunov : t -> t -> t
                                                            val discrete_lyapunov : ?solver:[ `bilinear | `default | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Mat/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Mat/index.html index a27638ebf..084476b34 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Mat/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression.S.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_regression.S.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/Maths/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/Maths/index.html index fb24a06f4..58080c444 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/Maths/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_regression.S.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            +Maths (owl.Owl_regression.S.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t
                                                            val (-) : t -> t -> t
                                                            val (*) : t -> t -> t
                                                            val (/) : t -> t -> t
                                                            val (*@) : t -> t -> t
                                                            val (**) : t -> t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val kron : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val pow : t -> t -> t
                                                            val atan2 : t -> t -> t
                                                            val min2 : t -> t -> t
                                                            val max2 : t -> t -> t
                                                            val cross_entropy : t -> t -> t
                                                            val inv : t -> t
                                                            val neg : t -> t
                                                            val abs : t -> t
                                                            val signum : t -> t
                                                            val floor : t -> t
                                                            val ceil : t -> t
                                                            val round : t -> t
                                                            val sqr : t -> t
                                                            val sqrt : t -> t
                                                            val log : t -> t
                                                            val log2 : t -> t
                                                            val log10 : t -> t
                                                            val exp : t -> t
                                                            val sin : t -> t
                                                            val cos : t -> t
                                                            val tan : t -> t
                                                            val sinh : t -> t
                                                            val cosh : t -> t
                                                            val tanh : t -> t
                                                            val asin : t -> t
                                                            val acos : t -> t
                                                            val atan : t -> t
                                                            val asinh : t -> t
                                                            val acosh : t -> t
                                                            val atanh : t -> t
                                                            val sum' : t -> t
                                                            val log_sum_exp' : t -> t
                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t
                                                            val sum_reduce : ?axis:int array -> t -> t
                                                            val mean : t -> t
                                                            val transpose : ?axis:int array -> t -> t
                                                            val swap : int -> int -> t -> t
                                                            val l1norm' : t -> t
                                                            val l2norm' : t -> t
                                                            val l2norm_sqr' : t -> t
                                                            val sigmoid : t -> t
                                                            val relu : t -> t
                                                            val dawsn : t -> t
                                                            val softplus : t -> t
                                                            val softsign : t -> t
                                                            val softmax : ?axis:int -> t -> t
                                                            val reshape : t -> int array -> t
                                                            val flatten : t -> t
                                                            val get_item : t -> int -> int -> t
                                                            val get_row : t -> int -> t
                                                            val concat : axis:int -> t -> t -> t
                                                            val split : axis:int -> int array -> t -> t array
                                                            val of_arrays : t array array -> t
                                                            val to_arrays : t -> t array array
                                                            val concatenate : axis:int -> t array -> t
                                                            val stack : axis:int -> t array -> t
                                                            val get_slice : int list list -> t -> t
                                                            val set_slice : int list list -> t -> t -> t
                                                            val get_fancy : Owl_types.index list -> t -> t
                                                            val set_fancy : Owl_types.index list -> t -> t -> t
                                                            val diag : ?k:int -> t -> t
                                                            val diagm : ?k:int -> t -> t
                                                            val trace : t -> t
                                                            val triu : ?k:int -> t -> t
                                                            val tril : ?k:int -> t -> t
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/NN/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/NN/index.html index 79c8f5207..962a1bc29 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/NN/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_regression.S.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : +NN (owl.Owl_regression.S.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t
                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t
                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_regression/S/Optimise/Algodiff/index.html b/docs/owl/Owl_regression/S/Optimise/Algodiff/index.html index a7c6c10bd..2ae41947a 100644 --- a/docs/owl/Owl_regression/S/Optimise/Algodiff/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Algodiff/index.html @@ -1,4 +1,4 @@ -Algodiff (owl.Owl_regression.S.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = +Algodiff (owl.Owl_regression.S.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            module A : sig ... end
                                                            type t = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Algodiff.t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            val tag : unit -> int
                                                            val primal : t -> t
                                                            val primal' : t -> t
                                                            val zero : t -> t
                                                            val reset_zero : t -> t
                                                            val tangent : t -> t
                                                            val adjref : t -> t Stdlib.ref
                                                            val adjval : t -> t
                                                            val shape : t -> int array
                                                            val is_float : t -> bool
                                                            val is_arr : t -> bool
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val numel : t -> int
                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t
                                                            val clip_by_l2norm : A.elt -> t -> t
                                                            val copy_primal' : t -> t
                                                            val tile : t -> int array -> t
                                                            val repeat : t -> int array -> t
                                                            val pack_elt : A.elt -> t
                                                            val unpack_elt : t -> A.elt
                                                            val pack_flt : float -> t
                                                            val _f : float -> t
                                                            val unpack_flt : t -> float
                                                            val pack_arr : A.arr -> t
                                                            val unpack_arr : t -> A.arr
                                                            val deep_info : t -> string
                                                            val type_info : t -> string
                                                            val error_binop : string -> t -> t -> 'a
                                                            val error_uniop : string -> t -> 'a
                                                            val make_forward : t -> t -> int -> t
                                                            val make_reverse : t -> int -> t
                                                            val reverse_prop : t -> t -> unit
                                                            val diff : (t -> t) -> t -> t
                                                            val diff' : (t -> t) -> t -> t * t
                                                            val grad : (t -> t) -> t -> t
                                                            val grad' : (t -> t) -> t -> t * t
                                                            val jacobian : (t -> t) -> t -> t
                                                            val jacobian' : (t -> t) -> t -> t * t
                                                            val jacobianv : (t -> t) -> t -> t -> t
                                                            val jacobianv' : (t -> t) -> t -> t -> t * t
                                                            val jacobianTv : (t -> t) -> t -> t -> t
                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t
                                                            val hessian : (t -> t) -> t -> t
                                                            val hessian' : (t -> t) -> t -> t * t
                                                            val hessianv : (t -> t) -> t -> t -> t
                                                            val hessianv' : (t -> t) -> t -> t -> t * t
                                                            val laplacian : (t -> t) -> t -> t
                                                            val laplacian' : (t -> t) -> t -> t * t
                                                            val gradhessian : (t -> t) -> t -> t * t
                                                            val gradhessian' : (t -> t) -> t -> t * t * t
                                                            val gradhessianv : (t -> t) -> t -> t -> t * t
                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t
                                                            module Builder : sig ... end
                                                            module Maths : sig ... end
                                                            module Linalg : sig ... end
                                                            module NN : sig ... end
                                                            module Mat : sig ... end
                                                            module Arr : sig ... end
                                                            val to_trace : t list -> string
                                                            val to_dot : t list -> string
                                                            val pp_num : Stdlib.Format.formatter -> t -> unit
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Batch/index.html b/docs/owl/Owl_regression/S/Optimise/Batch/index.html index e4bae30d8..0f8aa4ec8 100644 --- a/docs/owl/Owl_regression/S/Optimise/Batch/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Batch/index.html @@ -1,4 +1,4 @@ -Batch (owl.Owl_regression.S.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = +Batch (owl.Owl_regression.S.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Batch.typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val batches : typ -> Algodiff.t -> int
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Checkpoint/index.html b/docs/owl/Owl_regression/S/Optimise/Checkpoint/index.html index 92528798a..246201d36 100644 --- a/docs/owl/Owl_regression/S/Optimise/Checkpoint/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Checkpoint/index.html @@ -1,5 +1,5 @@ -Checkpoint (owl.Owl_regression.S.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = +Checkpoint (owl.Owl_regression.S.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            type state = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Checkpoint.state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }
                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Checkpoint.typ = diff --git a/docs/owl/Owl_regression/S/Optimise/Clipping/index.html b/docs/owl/Owl_regression/S/Optimise/Clipping/index.html index cd656007f..d95f45ed2 100644 --- a/docs/owl/Owl_regression/S/Optimise/Clipping/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Clipping/index.html @@ -1,4 +1,4 @@ -Clipping (owl.Owl_regression.S.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            type typ = +Clipping (owl.Owl_regression.S.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Gradient/index.html b/docs/owl/Owl_regression/S/Optimise/Gradient/index.html index 2dfd20503..1faef0936 100644 --- a/docs/owl/Owl_regression/S/Optimise/Gradient/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_regression.S.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = +Gradient (owl.Owl_regression.S.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Gradient.typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton
                                                            val run : typ -> diff --git a/docs/owl/Owl_regression/S/Optimise/Learning_Rate/index.html b/docs/owl/Owl_regression/S/Optimise/Learning_Rate/index.html index ab8c7b65f..3315404cf 100644 --- a/docs/owl/Owl_regression/S/Optimise/Learning_Rate/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Learning_Rate/index.html @@ -1,4 +1,4 @@ -Learning_Rate (owl.Owl_regression.S.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = +Learning_Rate (owl.Owl_regression.S.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Learning_Rate.typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t
                                                            val default : typ -> typ
                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Loss/index.html b/docs/owl/Owl_regression/S/Optimise/Loss/index.html index 79fc38a1b..b685cd4fa 100644 --- a/docs/owl/Owl_regression/S/Optimise/Loss/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Loss/index.html @@ -1,4 +1,4 @@ -Loss (owl.Owl_regression.S.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = +Loss (owl.Owl_regression.S.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Loss.typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Momentum/index.html b/docs/owl/Owl_regression/S/Optimise/Momentum/index.html index 4cc1ed4b4..75d5b1f92 100644 --- a/docs/owl/Owl_regression/S/Optimise/Momentum/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Momentum/index.html @@ -1,4 +1,4 @@ -Momentum (owl.Owl_regression.S.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            type typ = +Momentum (owl.Owl_regression.S.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Params/index.html b/docs/owl/Owl_regression/S/Optimise/Params/index.html index 11957d739..837792566 100644 --- a/docs/owl/Owl_regression/S/Optimise/Params/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_regression.S.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = +Params (owl.Owl_regression.S.Optimise.Params)

                                                            Module Optimise.Params

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Params.typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }
                                                            val default : unit -> typ
                                                            val config : ?batch:Batch.typ -> diff --git a/docs/owl/Owl_regression/S/Optimise/Regularisation/index.html b/docs/owl/Owl_regression/S/Optimise/Regularisation/index.html index f1bb134b8..d502b13f4 100644 --- a/docs/owl/Owl_regression/S/Optimise/Regularisation/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Regularisation/index.html @@ -1,4 +1,4 @@ -Regularisation (owl.Owl_regression.S.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = +Regularisation (owl.Owl_regression.S.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            type typ = Owl_optimise_generic.Make(Owl_algodiff_generic.Make(Owl_algodiff_primal_ops.S)).Regularisation.typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None
                                                            val run : typ -> Algodiff.t -> Algodiff.t
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Stopping/index.html b/docs/owl/Owl_regression/S/Optimise/Stopping/index.html index e06f13161..368df467d 100644 --- a/docs/owl/Owl_regression/S/Optimise/Stopping/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Stopping/index.html @@ -1,4 +1,4 @@ -Stopping (owl.Owl_regression.S.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            type typ = +Stopping (owl.Owl_regression.S.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            val run : typ -> float -> bool
                                                            val default : typ -> typ
                                                            val to_string : typ -> string
                                                            diff --git a/docs/owl/Owl_regression/S/Optimise/Utils/index.html b/docs/owl/Owl_regression/S/Optimise/Utils/index.html index 6e51f9f57..67ca64045 100644 --- a/docs/owl/Owl_regression/S/Optimise/Utils/index.html +++ b/docs/owl/Owl_regression/S/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_regression.S.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : +Utils (owl.Owl_regression.S.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            val sample_num : Algodiff.t -> int
                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t
                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_regression/S/Optimise/index.html b/docs/owl/Owl_regression/S/Optimise/index.html index 1a5ffe7e8..30046183f 100644 --- a/docs/owl/Owl_regression/S/Optimise/index.html +++ b/docs/owl/Owl_regression/S/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl.Owl_regression.S.Optimise)

                                                            Module S.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : +Optimise (owl.Owl_regression.S.Optimise)

                                                            Module S.Optimise

                                                            module Algodiff : sig ... end
                                                            module Utils : sig ... end
                                                            module Learning_Rate : sig ... end
                                                            module Batch : sig ... end
                                                            module Loss : sig ... end
                                                            module Gradient : sig ... end
                                                            module Momentum : sig ... end
                                                            module Regularisation : sig ... end
                                                            module Clipping : sig ... end
                                                            module Stopping : sig ... end
                                                            module Checkpoint : sig ... end
                                                            module Params : sig ... end
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> diff --git a/docs/owl/Owl_regression/S/index.html b/docs/owl/Owl_regression/S/index.html index 97e379be0..1ea73d02e 100644 --- a/docs/owl/Owl_regression/S/index.html +++ b/docs/owl/Owl_regression/S/index.html @@ -1,5 +1,5 @@ -S (owl.Owl_regression.S)

                                                            Module Owl_regression.S

                                                            module Optimise : sig ... end
                                                            val _linear_reg : +S (owl.Owl_regression.S)

                                                            Module Owl_regression.S

                                                            module Optimise : sig ... end
                                                            val _linear_reg : bool -> Optimise.Params.typ -> Optimise.Algodiff.A.arr -> diff --git a/docs/owl/Owl_regression/index.html b/docs/owl/Owl_regression/index.html index 3b2e8add7..e77c22757 100644 --- a/docs/owl/Owl_regression/index.html +++ b/docs/owl/Owl_regression/index.html @@ -1,2 +1,2 @@ -Owl_regression (owl.Owl_regression)

                                                            Module Owl_regression

                                                            Regression Module This module implements a set of regression functions. S module provides supports for single precision float numbers whilst D module provides supports for double precision float numbers.

                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            +Owl_regression (owl.Owl_regression)

                                                            Module Owl_regression

                                                            Regression Module This module implements a set of regression functions. S module provides supports for single precision float numbers whilst D module provides supports for double precision float numbers.

                                                            module S : sig ... end
                                                            module D : sig ... end
                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Linalg/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Linalg/index.html index 89b168f35..2ca9f6a61 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression_generic.Make.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression_generic.Make.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Mat/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Mat/index.html index 38b08da16..cee1f4b6b 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression_generic.Make.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_regression_generic.Make.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Scalar/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Scalar/index.html index 13ed8c223..f81d14ce7 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_regression_generic.Make.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_regression_generic.Make.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/index.html index 34f8dde85..1fa8112a6 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_regression_generic.Make.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_regression_generic.Make.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Arr/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Arr/index.html index 784d115a3..4476fb64e 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Arr/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_regression_generic.Make.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_regression_generic.Make.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/index.html index c930c90db..eb7cdae80 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            Ops Builder
                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t

                                                            build single input single output operations

                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t

                                                            build single input pair outputs operations

                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t

                                                            build single input triple outputs operations

                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array

                                                            build single input array output operations

                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t

                                                            build pair inputs single output operations

                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t

                                                            build array input single output operations

                                                            +Builder (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            Ops Builder
                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t

                                                            build single input single output operations

                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t

                                                            build single input pair outputs operations

                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t

                                                            build single input triple outputs operations

                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array

                                                            build single input array output operations

                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t

                                                            build pair inputs single output operations

                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t

                                                            build array input single output operations

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Aiso/index.html index e7ee34b88..bdd93ae56 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Piso/index.html index ef8a13bd4..fe759b031 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siao/index.html index b8cbf78c2..0efae6b89 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sipo/index.html index d12ab2c6e..a05df958d 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siso/index.html index a25a2d0c8..848bff28c 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sito/index.html index fd5dae567..1e8ec1248 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_regression_generic.Make.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Linalg/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Linalg/index.html index f6f38f84c..fad73e056 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression_generic.Make.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val logdet : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val chol : ?upper:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val qr : t -> t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val lq : t -> t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val svd : ?thin:bool -> t -> t * t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sylvester : t -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val lyapunov : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression_generic.Make.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val logdet : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val chol : ?upper:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val qr : t -> t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val lq : t -> t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val svd : ?thin:bool -> t -> t * t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sylvester : t -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val lyapunov : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Mat/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Mat/index.html index 546cbd963..6f8c5a305 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Mat/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression_generic.Make.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_regression_generic.Make.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Maths/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Maths/index.html index 3bb909075..229adb99e 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Maths/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_regression_generic.Make.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (-) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (*) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (/) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (*@) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (**) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val add : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sub : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val mul : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val div : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val kron : t -> t -> t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val dot : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val pow : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atan2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val min2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val max2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cross_entropy : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val inv : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val neg : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val abs : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val signum : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val floor : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val ceil : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val round : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sqr : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sqrt : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log2 : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log10 : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val exp : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sin : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cos : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tan : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sinh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cosh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tanh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val asin : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val acos : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atan : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val asinh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val acosh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atanh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log_sum_exp' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum_reduce : ?axis:int array -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val mean : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val transpose : ?axis:int array -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val swap : int -> int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l1norm' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l2norm' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l2norm_sqr' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sigmoid : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val relu : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val dawsn : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softplus : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softsign : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softmax : ?axis:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val reshape : t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val flatten : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_item : t -> int -> int -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_row : t -> int -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val concat : axis:int -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val split : axis:int -> int array -> t -> t array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val of_arrays : t array array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val to_arrays : t -> t array array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val concatenate : axis:int -> t array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val stack : axis:int -> t array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_slice : int list list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_slice : int list list -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_fancy : Owl_types.index list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_fancy : Owl_types.index list -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val diag : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val diagm : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val trace : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val triu : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tril : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            +Maths (owl.Owl_regression_generic.Make.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (-) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (*) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (/) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (*@) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (**) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val add : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sub : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val mul : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val div : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val kron : t -> t -> t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val dot : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val pow : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atan2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val min2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val max2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cross_entropy : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val inv : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val neg : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val abs : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val signum : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val floor : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val ceil : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val round : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sqr : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sqrt : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log2 : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log10 : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val exp : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sin : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cos : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tan : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sinh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cosh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tanh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val asin : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val acos : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atan : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val asinh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val acosh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atanh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log_sum_exp' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum_reduce : ?axis:int array -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val mean : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val transpose : ?axis:int array -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val swap : int -> int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l1norm' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l2norm' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l2norm_sqr' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sigmoid : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val relu : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val dawsn : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softplus : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softsign : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softmax : ?axis:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val reshape : t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val flatten : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_item : t -> int -> int -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_row : t -> int -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val concat : axis:int -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val split : axis:int -> int array -> t -> t array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val of_arrays : t array array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val to_arrays : t -> t array array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val concatenate : axis:int -> t array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val stack : axis:int -> t array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_slice : int list list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_slice : int list list -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_fancy : Owl_types.index list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_fancy : Owl_types.index list -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val diag : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val diagm : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val trace : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val triu : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tril : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/NN/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/NN/index.html index 349c44aea..ac7e2bd0f 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/NN/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_regression_generic.Make.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val dilated_conv1d : +NN (owl.Owl_regression_generic.Make.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/index.html index aae7601bd..928b2ebaf 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Algodiff/index.html @@ -1,5 +1,5 @@ -Algodiff (owl.Owl_regression_generic.Make.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            include Owl_algodiff_core_sig.Sig
                                                            Type definition
                                                            include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                            type t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            Core functions
                                                            val tag : unit -> int

                                                            TODO

                                                            val primal : t -> t

                                                            TODO

                                                            val primal' : t -> t

                                                            TODO

                                                            val zero : t -> t

                                                            TODO

                                                            val reset_zero : t -> t

                                                            TODO

                                                            val tangent : t -> t

                                                            TODO

                                                            val adjref : t -> t Stdlib.ref

                                                            TODO

                                                            val adjval : t -> t

                                                            TODO

                                                            val shape : t -> int array

                                                            TODO

                                                            val is_float : t -> bool

                                                            TODO

                                                            val is_arr : t -> bool

                                                            TODO

                                                            val row_num : t -> int

                                                            number of rows

                                                            val col_num : t -> int

                                                            number of columns

                                                            val numel : t -> int

                                                            number of elements

                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                            other functions, without tracking gradient

                                                            val clip_by_l2norm : A.elt -> t -> t

                                                            other functions, without tracking gradient

                                                            val copy_primal' : t -> t

                                                            TODO

                                                            val tile : t -> int array -> t

                                                            TODO

                                                            val repeat : t -> int array -> t

                                                            TODO

                                                            val pack_elt : A.elt -> t

                                                            convert from elt type to t type.

                                                            val unpack_elt : t -> A.elt

                                                            convert from t type to elt type.

                                                            val pack_flt : float -> t

                                                            convert from float type to t type.

                                                            val _f : float -> t

                                                            A shortcut function for F A.(float_to_elt x).

                                                            val unpack_flt : t -> float

                                                            convert from t type to float type.

                                                            val pack_arr : A.arr -> t

                                                            convert from arr type to t type.

                                                            val unpack_arr : t -> A.arr

                                                            convert from t type to arr type.

                                                            val deep_info : t -> string

                                                            TODO

                                                            val type_info : t -> string

                                                            TODO

                                                            val error_binop : string -> t -> t -> 'a

                                                            TODO

                                                            val error_uniop : string -> t -> 'a

                                                            TODO

                                                            val make_forward : t -> t -> int -> t

                                                            TODO

                                                            val make_reverse : t -> int -> t

                                                            TODO

                                                            val reverse_prop : t -> t -> unit

                                                            TODO

                                                            val diff : (t -> t) -> t -> t

                                                            diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                            Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                            val diff' : (t -> t) -> t -> t * t

                                                            similar to diff, but return (f x, diff f x).

                                                            val grad : (t -> t) -> t -> t

                                                            gradient of f : (vector -> scalar) at x, reverse ad.

                                                            val grad' : (t -> t) -> t -> t * t

                                                            similar to grad, but return (f x, grad f x).

                                                            val jacobian : (t -> t) -> t -> t

                                                            jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                            val jacobian' : (t -> t) -> t -> t * t

                                                            similar to jacobian, but return (f x, jacobian f x)

                                                            val jacobianv : (t -> t) -> t -> t -> t

                                                            jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                            val jacobianv' : (t -> t) -> t -> t -> t * t

                                                            similar to jacobianv', but return (f x, jacobianv f x v)

                                                            val jacobianTv : (t -> t) -> t -> t -> t

                                                            transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                            similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                            val hessian : (t -> t) -> t -> t

                                                            hessian of f : (scalar -> scalar) at x.

                                                            val hessian' : (t -> t) -> t -> t * t

                                                            simiarl to hessian, but return (f x, hessian f x)

                                                            val hessianv : (t -> t) -> t -> t -> t

                                                            hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                            val hessianv' : (t -> t) -> t -> t -> t * t

                                                            similar to hessianv, but return (f x, hessianv f x v).

                                                            val laplacian : (t -> t) -> t -> t

                                                            laplacian of f : (scalar -> scalar) at x.

                                                            val laplacian' : (t -> t) -> t -> t * t

                                                            similar to laplacian, but return (f x, laplacian f x).

                                                            val gradhessian : (t -> t) -> t -> t * t

                                                            return (grad f x, hessian f x), f : (scalar -> scalar)

                                                            val gradhessian' : (t -> t) -> t -> t * t * t

                                                            return (f x, grad f x, hessian f x)

                                                            val gradhessianv : (t -> t) -> t -> t -> t * t

                                                            return (grad f x v, hessian f x v)

                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                            return (f x, grad f x v, hessian f x v)

                                                            include Owl_algodiff_ops_sig.Sig +Algodiff (owl.Owl_regression_generic.Make.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            include Owl_algodiff_core_sig.Sig
                                                            Type definition
                                                            include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                            type t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            Core functions
                                                            val tag : unit -> int

                                                            TODO

                                                            val primal : t -> t

                                                            TODO

                                                            val primal' : t -> t

                                                            TODO

                                                            val zero : t -> t

                                                            TODO

                                                            val reset_zero : t -> t

                                                            TODO

                                                            val tangent : t -> t

                                                            TODO

                                                            val adjref : t -> t Stdlib.ref

                                                            TODO

                                                            val adjval : t -> t

                                                            TODO

                                                            val shape : t -> int array

                                                            TODO

                                                            val is_float : t -> bool

                                                            TODO

                                                            val is_arr : t -> bool

                                                            TODO

                                                            val row_num : t -> int

                                                            number of rows

                                                            val col_num : t -> int

                                                            number of columns

                                                            val numel : t -> int

                                                            number of elements

                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                            other functions, without tracking gradient

                                                            val clip_by_l2norm : A.elt -> t -> t

                                                            other functions, without tracking gradient

                                                            val copy_primal' : t -> t

                                                            TODO

                                                            val tile : t -> int array -> t

                                                            TODO

                                                            val repeat : t -> int array -> t

                                                            TODO

                                                            val pack_elt : A.elt -> t

                                                            convert from elt type to t type.

                                                            val unpack_elt : t -> A.elt

                                                            convert from t type to elt type.

                                                            val pack_flt : float -> t

                                                            convert from float type to t type.

                                                            val _f : float -> t

                                                            A shortcut function for F A.(float_to_elt x).

                                                            val unpack_flt : t -> float

                                                            convert from t type to float type.

                                                            val pack_arr : A.arr -> t

                                                            convert from arr type to t type.

                                                            val unpack_arr : t -> A.arr

                                                            convert from t type to arr type.

                                                            val deep_info : t -> string

                                                            TODO

                                                            val type_info : t -> string

                                                            TODO

                                                            val error_binop : string -> t -> t -> 'a

                                                            TODO

                                                            val error_uniop : string -> t -> 'a

                                                            TODO

                                                            val make_forward : t -> t -> int -> t

                                                            TODO

                                                            val make_reverse : t -> int -> t

                                                            TODO

                                                            val reverse_prop : t -> t -> unit

                                                            TODO

                                                            val diff : (t -> t) -> t -> t

                                                            diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                            Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                            val diff' : (t -> t) -> t -> t * t

                                                            similar to diff, but return (f x, diff f x).

                                                            val grad : (t -> t) -> t -> t

                                                            gradient of f : (vector -> scalar) at x, reverse ad.

                                                            val grad' : (t -> t) -> t -> t * t

                                                            similar to grad, but return (f x, grad f x).

                                                            val jacobian : (t -> t) -> t -> t

                                                            jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                            val jacobian' : (t -> t) -> t -> t * t

                                                            similar to jacobian, but return (f x, jacobian f x)

                                                            val jacobianv : (t -> t) -> t -> t -> t

                                                            jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                            val jacobianv' : (t -> t) -> t -> t -> t * t

                                                            similar to jacobianv', but return (f x, jacobianv f x v)

                                                            val jacobianTv : (t -> t) -> t -> t -> t

                                                            transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                            similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                            val hessian : (t -> t) -> t -> t

                                                            hessian of f : (scalar -> scalar) at x.

                                                            val hessian' : (t -> t) -> t -> t * t

                                                            simiarl to hessian, but return (f x, hessian f x)

                                                            val hessianv : (t -> t) -> t -> t -> t

                                                            hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                            val hessianv' : (t -> t) -> t -> t -> t * t

                                                            similar to hessianv, but return (f x, hessianv f x v).

                                                            val laplacian : (t -> t) -> t -> t

                                                            laplacian of f : (scalar -> scalar) at x.

                                                            val laplacian' : (t -> t) -> t -> t * t

                                                            similar to laplacian, but return (f x, laplacian f x).

                                                            val gradhessian : (t -> t) -> t -> t * t

                                                            return (grad f x, hessian f x), f : (scalar -> scalar)

                                                            val gradhessian' : (t -> t) -> t -> t * t * t

                                                            return (f x, grad f x, hessian f x)

                                                            val gradhessianv : (t -> t) -> t -> t -> t * t

                                                            return (grad f x v, hessian f x v)

                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                            return (f x, grad f x v, hessian f x v)

                                                            include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Batch/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Batch/index.html index 1f300bf5b..a73778743 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Batch/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl.Owl_regression_generic.Make.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            Batch module

                                                            type typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic

                                                            Types of batches.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val batches : typ -> Algodiff.t -> int

                                                            Return the total number of batches given a batch typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Batch (owl.Owl_regression_generic.Make.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            Batch module

                                                            type typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic

                                                            Types of batches.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val batches : typ -> Algodiff.t -> int

                                                            Return the total number of batches given a batch typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Checkpoint/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Checkpoint/index.html index 18428003f..2720a8480 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Checkpoint/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl.Owl_regression_generic.Make.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            Checkpoint module

                                                            type state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }

                                                            Type definition of checkpoint

                                                            type typ =
                                                            1. | Batch of int
                                                            2. | Epoch of float
                                                            3. | Custom of state -> unit
                                                            4. | None

                                                            Batch type.

                                                            val init_state : int -> float -> state

                                                            init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                            val default_checkpoint_fun : (string -> 'a) -> 'a

                                                            This function is used for saving intermediate files during optimisation.

                                                            val print_state_info : state -> unit

                                                            Print out the detail information of current state.

                                                            val print_summary : state -> unit

                                                            Print out the summary of current state.

                                                            val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Checkpoint (owl.Owl_regression_generic.Make.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            Checkpoint module

                                                            type state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }

                                                            Type definition of checkpoint

                                                            type typ =
                                                            1. | Batch of int
                                                            2. | Epoch of float
                                                            3. | Custom of state -> unit
                                                            4. | None

                                                            Batch type.

                                                            val init_state : int -> float -> state

                                                            init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                            val default_checkpoint_fun : (string -> 'a) -> 'a

                                                            This function is used for saving intermediate files during optimisation.

                                                            val print_state_info : state -> unit

                                                            Print out the detail information of current state.

                                                            val print_summary : state -> unit

                                                            Print out the summary of current state.

                                                            val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Clipping/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Clipping/index.html index bfdc94020..2b6cd216f 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Clipping/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl.Owl_regression_generic.Make.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            Clipping module

                                                            type typ =
                                                            1. | L2norm of float
                                                            2. | Value of float * float
                                                            3. | None

                                                            Types of clipping functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Clipping (owl.Owl_regression_generic.Make.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            Clipping module

                                                            type typ =
                                                            1. | L2norm of float
                                                            2. | Value of float * float
                                                            3. | None

                                                            Types of clipping functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Gradient/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Gradient/index.html index 5f654c448..696593dda 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Gradient/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_regression_generic.Make.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            Gradient module

                                                            type typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton

                                                            Types of gradient function.

                                                            val run : +Gradient (owl.Owl_regression_generic.Make.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            Gradient module

                                                            type typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton

                                                            Types of gradient function.

                                                            val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Learning_Rate/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Learning_Rate/index.html index 91b2dce0d..60ccf2e2f 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Learning_Rate/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl.Owl_regression_generic.Make.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            Strategies for learning rate update

                                                            type typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array

                                                            Representation of learning rate update strategies. Possible values include:

                                                            • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                            Update the cache of gradients.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Learning_Rate (owl.Owl_regression_generic.Make.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            Strategies for learning rate update

                                                            type typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array

                                                            Representation of learning rate update strategies. Possible values include:

                                                            • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                            Update the cache of gradients.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Loss/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Loss/index.html index f86b2efa7..b01a51c90 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Loss/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl.Owl_regression_generic.Make.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            Loss module

                                                            type typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Types of loss functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Loss (owl.Owl_regression_generic.Make.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            Loss module

                                                            type typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Types of loss functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Momentum/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Momentum/index.html index a6b1bd7e4..5abe40368 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Momentum/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl.Owl_regression_generic.Make.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            Momentum module

                                                            type typ =
                                                            1. | Standard of float
                                                            2. | Nesterov of float
                                                            3. | None

                                                            Types of momentum functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Momentum (owl.Owl_regression_generic.Make.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            Momentum module

                                                            type typ =
                                                            1. | Standard of float
                                                            2. | Nesterov of float
                                                            3. | None

                                                            Types of momentum functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Params/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Params/index.html index a7e813f45..82f26b572 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Params/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_regression_generic.Make.Optimise.Params)

                                                            Module Optimise.Params

                                                            Params module

                                                            type typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }

                                                            Type definition of parameter.

                                                            val default : unit -> typ

                                                            Create module typ with default values.

                                                            val config : +Params (owl.Owl_regression_generic.Make.Optimise.Params)

                                                            Module Optimise.Params

                                                            Params module

                                                            type typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }

                                                            Type definition of parameter.

                                                            val default : unit -> typ

                                                            Create module typ with default values.

                                                            val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Regularisation/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Regularisation/index.html index c13891971..a3b8c08a8 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Regularisation/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl.Owl_regression_generic.Make.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            Regularisation module

                                                            type typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None

                                                            Types of regularisation functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Regularisation (owl.Owl_regression_generic.Make.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            Regularisation module

                                                            type typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None

                                                            Types of regularisation functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Stopping/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Stopping/index.html index 9ad3c96ba..093506bc1 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Stopping/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl.Owl_regression_generic.Make.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            Stopping module

                                                            type typ =
                                                            1. | Const of float
                                                            2. | Early of int * int
                                                            3. | None

                                                            Types of stopping functions.

                                                            val run : typ -> float -> bool

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Stopping (owl.Owl_regression_generic.Make.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            Stopping module

                                                            type typ =
                                                            1. | Const of float
                                                            2. | Early of int * int
                                                            3. | None

                                                            Types of stopping functions.

                                                            val run : typ -> float -> bool

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Utils/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Utils/index.html index 51be193cb..4fa886638 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Utils/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_regression_generic.Make.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            Utils module

                                                            val sample_num : Algodiff.t -> int

                                                            Return the total number of samples in passed in ndarray.

                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                            draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                            val get_chunk : +Utils (owl.Owl_regression_generic.Make.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            Utils module

                                                            val sample_num : Algodiff.t -> int

                                                            Return the total number of samples in passed in ndarray.

                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                            draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/index.html b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/index.html index 84aa1c45d..1ee77a48e 100644 --- a/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/index.html +++ b/docs/owl/Owl_regression_generic/Make/argument-1-Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl.Owl_regression_generic.Make.Optimise)

                                                            Parameter Make.Optimise

                                                            module Utils : sig ... end

                                                            Utils module

                                                            module Learning_Rate : sig ... end

                                                            Strategies for learning rate update

                                                            module Batch : sig ... end

                                                            Batch module

                                                            module Loss : sig ... end

                                                            Loss module

                                                            module Gradient : sig ... end

                                                            Gradient module

                                                            module Momentum : sig ... end

                                                            Momentum module

                                                            module Regularisation : sig ... end

                                                            Regularisation module

                                                            module Clipping : sig ... end

                                                            Clipping module

                                                            module Stopping : sig ... end

                                                            Stopping module

                                                            module Checkpoint : sig ... end

                                                            Checkpoint module

                                                            module Params : sig ... end

                                                            Params module

                                                            Core functions
                                                            val minimise_weight : +Optimise (owl.Owl_regression_generic.Make.Optimise)

                                                            Parameter Make.Optimise

                                                            module Utils : sig ... end

                                                            Utils module

                                                            module Learning_Rate : sig ... end

                                                            Strategies for learning rate update

                                                            module Batch : sig ... end

                                                            Batch module

                                                            module Loss : sig ... end

                                                            Loss module

                                                            module Gradient : sig ... end

                                                            Gradient module

                                                            module Momentum : sig ... end

                                                            Momentum module

                                                            module Regularisation : sig ... end

                                                            Regularisation module

                                                            module Clipping : sig ... end

                                                            Clipping module

                                                            module Stopping : sig ... end

                                                            Stopping module

                                                            module Checkpoint : sig ... end

                                                            Checkpoint module

                                                            module Params : sig ... end

                                                            Params module

                                                            Core functions
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> @@ -28,4 +28,4 @@ (string -> unit) -> Algodiff.t -> Algodiff.t -> - Checkpoint.state

                                                            TODO

                                                            + Checkpoint.state

                                                            This function is minimize the weights in a compiled neural network of graph structure.

                                                            diff --git a/docs/owl/Owl_regression_generic/Make/index.html b/docs/owl/Owl_regression_generic/Make/index.html index 28630a012..e8ec84f09 100644 --- a/docs/owl/Owl_regression_generic/Make/index.html +++ b/docs/owl/Owl_regression_generic/Make/index.html @@ -1,5 +1,5 @@ -Make (owl.Owl_regression_generic.Make)

                                                            Module Owl_regression_generic.Make

                                                            Parameters

                                                            Signature

                                                            module Optimise = Optimise
                                                            val _linear_reg : +Make (owl.Owl_regression_generic.Make)

                                                            Module Owl_regression_generic.Make

                                                            Parameters

                                                            Signature

                                                            module Optimise = Optimise
                                                            val _linear_reg : bool -> Optimise.Params.typ -> Optimise.Algodiff.A.arr -> diff --git a/docs/owl/Owl_regression_generic/index.html b/docs/owl/Owl_regression_generic/index.html index 5fcda9018..bce87a8a4 100644 --- a/docs/owl/Owl_regression_generic/index.html +++ b/docs/owl/Owl_regression_generic/index.html @@ -1,2 +1,2 @@ -Owl_regression_generic (owl.Owl_regression_generic)

                                                            Module Owl_regression_generic

                                                            module Make (Optimise : Owl_optimise_generic_sig.Sig) : sig ... end
                                                            +Owl_regression_generic (owl.Owl_regression_generic)

                                                            Module Owl_regression_generic

                                                            module Make (Optimise : Owl_optimise_generic_sig.Sig) : sig ... end
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/index.html b/docs/owl/Owl_regression_generic_sig/index.html index 3a861468e..c3caef6c1 100644 --- a/docs/owl/Owl_regression_generic_sig/index.html +++ b/docs/owl/Owl_regression_generic_sig/index.html @@ -1,2 +1,2 @@ -Owl_regression_generic_sig (owl.Owl_regression_generic_sig)

                                                            Module Owl_regression_generic_sig

                                                            module type Sig = sig ... end
                                                            +Owl_regression_generic_sig (owl.Owl_regression_generic_sig)

                                                            Module Owl_regression_generic_sig

                                                            module type Sig = sig ... end
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Linalg/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Linalg/index.html index 37da2355f..94cd51026 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Linalg/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.A.Linalg)

                                                            Module A.Linalg

                                                            val inv : arr -> arr
                                                            val logdet : arr -> elt
                                                            val chol : ?upper:bool -> arr -> arr
                                                            val svd : ?thin:bool -> arr -> arr * arr * arr
                                                            val qr : arr -> arr * arr
                                                            val lq : arr -> arr * arr
                                                            val sylvester : arr -> arr -> arr -> arr
                                                            val lyapunov : arr -> arr -> arr
                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> arr -> arr -> diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Mat/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Mat/index.html index b4a56a283..bcc375d3e 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Mat/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            +Mat (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.A.Mat)

                                                            Module A.Mat

                                                            val diagm : ?k:int -> arr -> arr
                                                            val triu : ?k:int -> arr -> arr
                                                            val tril : ?k:int -> arr -> arr
                                                            val eye : int -> arr
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Scalar/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Scalar/index.html index adc87fc3d..0842164ad 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Scalar/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/Scalar/index.html @@ -1,2 +1,2 @@ -Scalar (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            +Scalar (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.A.Scalar)

                                                            Module A.Scalar

                                                            val add : elt -> elt -> elt
                                                            val sub : elt -> elt -> elt
                                                            val mul : elt -> elt -> elt
                                                            val div : elt -> elt -> elt
                                                            val pow : elt -> elt -> elt
                                                            val atan2 : elt -> elt -> elt
                                                            val abs : elt -> elt
                                                            val neg : elt -> elt
                                                            val sqr : elt -> elt
                                                            val sqrt : elt -> elt
                                                            val exp : elt -> elt
                                                            val log : elt -> elt
                                                            val log2 : elt -> elt
                                                            val log10 : elt -> elt
                                                            val signum : elt -> elt
                                                            val floor : elt -> elt
                                                            val ceil : elt -> elt
                                                            val round : elt -> elt
                                                            val sin : elt -> elt
                                                            val cos : elt -> elt
                                                            val tan : elt -> elt
                                                            val sinh : elt -> elt
                                                            val cosh : elt -> elt
                                                            val tanh : elt -> elt
                                                            val asin : elt -> elt
                                                            val acos : elt -> elt
                                                            val atan : elt -> elt
                                                            val asinh : elt -> elt
                                                            val acosh : elt -> elt
                                                            val atanh : elt -> elt
                                                            val relu : elt -> elt
                                                            val dawsn : elt -> elt
                                                            val sigmoid : elt -> elt
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/index.html index b3a1dd0be..ea461cbe7 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/A/index.html @@ -1,5 +1,5 @@ -A (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : +A (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.A)

                                                            Module Algodiff.A

                                                            include Owl_types_ndarray_eltcmp.Sig
                                                            include Owl_types_ndarray_basic.Sig
                                                            type arr
                                                            type elt
                                                            val empty : int array -> arr
                                                            val zeros : int array -> arr
                                                            val ones : int array -> arr
                                                            val create : int array -> elt -> arr
                                                            val sequential : ?a:elt -> ?step:elt -> int array -> arr
                                                            val uniform : ?a:elt -> ?b:elt -> int array -> arr
                                                            val gaussian : ?mu:elt -> ?sigma:elt -> int array -> arr
                                                            val bernoulli : ?p:elt -> int array -> arr
                                                            val init : int array -> (int -> elt) -> arr
                                                            val init_nd : int array -> (int array -> elt) -> arr
                                                            val shape : arr -> int array
                                                            val numel : arr -> int
                                                            val get : arr -> int array -> elt
                                                            val set : arr -> int array -> elt -> unit
                                                            val get_slice : int list list -> arr -> arr
                                                            val set_slice : int list list -> arr -> arr -> unit
                                                            val get_fancy : Owl_types_common.index list -> arr -> arr
                                                            val set_fancy : Owl_types_common.index list -> arr -> arr -> unit
                                                            val copy : arr -> arr
                                                            val copy_ : out:arr -> arr -> unit
                                                            val reset : arr -> unit
                                                            val reshape : arr -> int array -> arr
                                                            val reverse : arr -> arr
                                                            val tile : arr -> int array -> arr
                                                            val repeat : arr -> int array -> arr
                                                            val concatenate : ?axis:int -> arr array -> arr
                                                            val stack : ?axis:int -> arr array -> arr
                                                            val split : ?axis:int -> int array -> arr -> arr array
                                                            val expand : ?hi:bool -> arr -> int -> arr
                                                            val squeeze : ?axis:int array -> arr -> arr
                                                            val draw : ?axis:int -> arr -> int -> arr * int array
                                                            val map : (elt -> elt) -> arr -> arr
                                                            val fold : ?axis:int -> (elt -> elt -> elt) -> elt -> arr -> arr
                                                            val scan : ?axis:int -> (elt -> elt -> elt) -> arr -> arr
                                                            val one_hot : int -> arr -> arr
                                                            val pad : ?v:elt -> int list list -> arr -> arr
                                                            val print : ?max_row:int -> ?max_col:int -> ?header:bool -> diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Arr/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Arr/index.html index 4c1b2046c..3a4333ae6 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Arr/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Arr/index.html @@ -1,2 +1,2 @@ -Arr (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            +Arr (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Arr)

                                                            Module Algodiff.Arr

                                                            val empty : int array -> t
                                                            val zeros : int array -> t
                                                            val ones : int array -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int array -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int array -> t
                                                            val shape : t -> int array
                                                            val numel : t -> int
                                                            val reset : t -> unit
                                                            val reshape : t -> int array -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/index.html index dd80408f2..12f4b0e98 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/index.html @@ -1,2 +1,2 @@ -Builder (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            Ops Builder
                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t

                                                            build single input single output operations

                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t

                                                            build single input pair outputs operations

                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t

                                                            build single input triple outputs operations

                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array

                                                            build single input array output operations

                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t

                                                            build pair inputs single output operations

                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t

                                                            build array input single output operations

                                                            +Builder (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder)

                                                            Module Algodiff.Builder

                                                            Ops Builder
                                                            module type Siso = sig ... end
                                                            val build_siso : (module Siso) -> t -> t

                                                            build single input single output operations

                                                            module type Sipo = sig ... end
                                                            val build_sipo : (module Sipo) -> t -> t * t

                                                            build single input pair outputs operations

                                                            module type Sito = sig ... end
                                                            val build_sito : (module Sito) -> t -> t * t * t

                                                            build single input triple outputs operations

                                                            module type Siao = sig ... end
                                                            val build_siao : (module Siao) -> t -> t array

                                                            build single input array output operations

                                                            module type Piso = sig ... end
                                                            val build_piso : (module Piso) -> t -> t -> t

                                                            build pair inputs single output operations

                                                            module type Aiso = sig ... end
                                                            val build_aiso : (module Aiso) -> t array -> t

                                                            build array input single output operations

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Aiso/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Aiso/index.html index 8ead8ebb8..d314f8c8f 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Aiso/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Aiso/index.html @@ -1,2 +1,2 @@ -Aiso (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            +Aiso (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Aiso)

                                                            Module type Builder.Aiso

                                                            val label : string
                                                            val ff : t array -> t
                                                            val df : int list -> t -> t array -> t array -> t
                                                            val dr : int list -> t array -> t -> t Stdlib.ref -> t list
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Piso/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Piso/index.html index f0a23696f..aeefce6c7 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Piso/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Piso/index.html @@ -1,2 +1,2 @@ -Piso (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            +Piso (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Piso)

                                                            Module type Builder.Piso

                                                            val label : string
                                                            val ff_aa : A.elt -> A.elt -> t
                                                            val ff_ab : A.elt -> A.arr -> t
                                                            val ff_ba : A.arr -> A.elt -> t
                                                            val ff_bb : A.arr -> A.arr -> t
                                                            val df_da : t -> t -> t -> t -> t
                                                            val df_db : t -> t -> t -> t -> t
                                                            val df_dab : t -> t -> t -> t -> t -> t
                                                            val dr_ab : t -> t -> t -> t Stdlib.ref -> t * t
                                                            val dr_a : t -> t -> t -> t Stdlib.ref -> t
                                                            val dr_b : t -> t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siao/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siao/index.html index 63e492596..72c0c8dee 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siao/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siao/index.html @@ -1,2 +1,2 @@ -Siao (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            +Siao (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Siao)

                                                            Module type Builder.Siao

                                                            val label : string
                                                            val ff_f : A.elt -> t array
                                                            val ff_arr : A.arr -> t array
                                                            val df : t array -> t -> t -> t array
                                                            val dr : t -> t -> t Stdlib.ref array -> t Stdlib.ref array -> t
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sipo/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sipo/index.html index adc14fc02..f30a4d929 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sipo/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sipo/index.html @@ -1,5 +1,5 @@ -Sipo (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sipo (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Sipo)

                                                            Module type Builder.Sipo

                                                            val label : string
                                                            val ff_f : A.elt -> t * t
                                                            val ff_arr : A.arr -> t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siso/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siso/index.html index 780d12c62..e90d3256d 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siso/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Siso/index.html @@ -1,2 +1,2 @@ -Siso (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            +Siso (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Siso)

                                                            Module type Builder.Siso

                                                            val label : string
                                                            val ff_f : A.elt -> t
                                                            val ff_arr : A.arr -> t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> t Stdlib.ref -> t
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sito/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sito/index.html index 230de8c7d..7e9de286b 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sito/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Builder/module-type-Sito/index.html @@ -1,5 +1,5 @@ -Sito (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : +Sito (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Builder.Sito)

                                                            Module type Builder.Sito

                                                            val label : string
                                                            val ff_f : A.elt -> t * t * t
                                                            val ff_arr : A.arr -> t * t * t
                                                            val df : t -> t -> t -> t
                                                            val dr : t -> t -> (t Stdlib.ref * t Stdlib.ref * t Stdlib.ref) -> diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Linalg/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Linalg/index.html index 5250abd70..2c6ec34b4 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Linalg/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Linalg/index.html @@ -1,5 +1,5 @@ -Linalg (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val logdet : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val chol : ?upper:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val qr : t -> t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val lq : t -> t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val svd : ?thin:bool -> t -> t * t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sylvester : t -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val lyapunov : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val discrete_lyapunov : +Linalg (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Linalg)

                                                            Module Algodiff.Linalg

                                                            val inv : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val logdet : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val chol : ?upper:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val qr : t -> t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val lq : t -> t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val svd : ?thin:bool -> t -> t * t * t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sylvester : t -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val lyapunov : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val discrete_lyapunov : ?solver:[ `default | `bilinear | `direct ] -> t -> t -> diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Mat/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Mat/index.html index cde11f83f..4e45eb0a9 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Mat/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Mat/index.html @@ -1,2 +1,2 @@ -Mat (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            +Mat (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Mat)

                                                            Module Algodiff.Mat

                                                            val empty : int -> int -> t
                                                            val zeros : int -> int -> t
                                                            val eye : int -> t
                                                            val ones : int -> int -> t
                                                            val uniform : ?a:A.elt -> ?b:A.elt -> int -> int -> t
                                                            val gaussian : ?mu:A.elt -> ?sigma:A.elt -> int -> int -> t
                                                            val shape : t -> int * int
                                                            val numel : t -> int
                                                            val row_num : t -> int
                                                            val col_num : t -> int
                                                            val reset : t -> unit
                                                            val reshape : int -> int -> t -> t
                                                            val get : t -> int -> int -> t
                                                            val set : t -> int -> int -> t -> t
                                                            val row : t -> int -> t
                                                            val mean : t -> t
                                                            val add : t -> t -> t
                                                            val sub : t -> t -> t
                                                            val mul : t -> t -> t
                                                            val div : t -> t -> t
                                                            val dot : t -> t -> t
                                                            val map_by_row : (t -> t) -> t -> t
                                                            val of_arrays : A.elt array array -> t
                                                            val init_2d : int -> int -> (int -> int -> t) -> t
                                                            val print : t -> unit
                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Maths/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Maths/index.html index 49a8cce8b..e0871efc3 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Maths/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/Maths/index.html @@ -1,2 +1,2 @@ -Maths (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (-) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (*) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (/) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (*@) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (**) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val add : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sub : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val mul : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val div : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val kron : t -> t -> t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val dot : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val pow : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atan2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val min2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val max2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cross_entropy : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val inv : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val neg : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val abs : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val signum : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val floor : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val ceil : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val round : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sqr : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sqrt : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log2 : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log10 : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val exp : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sin : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cos : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tan : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sinh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cosh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tanh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val asin : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val acos : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atan : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val asinh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val acosh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atanh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log_sum_exp' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum_reduce : ?axis:int array -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val mean : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val transpose : ?axis:int array -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val swap : int -> int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l1norm' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l2norm' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l2norm_sqr' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sigmoid : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val relu : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val dawsn : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softplus : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softsign : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softmax : ?axis:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val reshape : t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val flatten : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_item : t -> int -> int -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_row : t -> int -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val concat : axis:int -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val split : axis:int -> int array -> t -> t array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val of_arrays : t array array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val to_arrays : t -> t array array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val concatenate : axis:int -> t array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val stack : axis:int -> t array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_slice : int list list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_slice : int list list -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_fancy : Owl_types.index list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_fancy : Owl_types.index list -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val diag : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val diagm : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val trace : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val triu : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tril : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            +Maths (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.Maths)

                                                            Module Algodiff.Maths

                                                            val (+) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (-) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (*) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (/) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (*@) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val (**) : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val add : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sub : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val mul : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val div : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val kron : t -> t -> t

                                                            Refer to :doc:`owl_dense_matrix_generic`

                                                            val dot : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val pow : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atan2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val min2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val max2 : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cross_entropy : t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val inv : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val neg : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val abs : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val signum : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val floor : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val ceil : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val round : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sqr : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sqrt : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log2 : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log10 : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val exp : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sin : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cos : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tan : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sinh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val cosh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tanh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val asin : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val acos : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atan : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val asinh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val acosh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val atanh : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log_sum_exp' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val log_sum_exp : ?axis:int -> ?keep_dims:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum : ?axis:int -> ?keep_dims:bool -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sum_reduce : ?axis:int array -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val mean : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val transpose : ?axis:int array -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val swap : int -> int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l1norm' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l2norm' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val l2norm_sqr' : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val sigmoid : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val relu : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val dawsn : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softplus : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softsign : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val softmax : ?axis:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val reshape : t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val flatten : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_item : t -> int -> int -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_row : t -> int -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val concat : axis:int -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val split : axis:int -> int array -> t -> t array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val of_arrays : t array array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val to_arrays : t -> t array array

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val concatenate : axis:int -> t array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val stack : axis:int -> t array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_slice : int list list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_slice : int list list -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val get_fancy : Owl_types.index list -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val set_fancy : Owl_types.index list -> t -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val diag : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val diagm : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val trace : t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val triu : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val tril : ?k:int -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/NN/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/NN/index.html index b18e0230f..9a20552cb 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/NN/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/NN/index.html @@ -1,5 +1,5 @@ -NN (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val dilated_conv1d : +NN (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff.NN)

                                                            Module Algodiff.NN

                                                            val dropout : ?rate:float -> t -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv1d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv2d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val conv3d : ?padding:Owl_types.padding -> t -> t -> int array -> t

                                                            Refer to :doc:`owl_dense_ndarray_generic`

                                                            val dilated_conv1d : ?padding:Owl_types.padding -> t -> t -> diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/index.html index 56e045db3..cf5701dde 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Algodiff/index.html @@ -1,5 +1,5 @@ -Algodiff (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            include Owl_algodiff_core_sig.Sig
                                                            Type definition
                                                            include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                            type t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            Core functions
                                                            val tag : unit -> int

                                                            TODO

                                                            val primal : t -> t

                                                            TODO

                                                            val primal' : t -> t

                                                            TODO

                                                            val zero : t -> t

                                                            TODO

                                                            val reset_zero : t -> t

                                                            TODO

                                                            val tangent : t -> t

                                                            TODO

                                                            val adjref : t -> t Stdlib.ref

                                                            TODO

                                                            val adjval : t -> t

                                                            TODO

                                                            val shape : t -> int array

                                                            TODO

                                                            val is_float : t -> bool

                                                            TODO

                                                            val is_arr : t -> bool

                                                            TODO

                                                            val row_num : t -> int

                                                            number of rows

                                                            val col_num : t -> int

                                                            number of columns

                                                            val numel : t -> int

                                                            number of elements

                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                            other functions, without tracking gradient

                                                            val clip_by_l2norm : A.elt -> t -> t

                                                            other functions, without tracking gradient

                                                            val copy_primal' : t -> t

                                                            TODO

                                                            val tile : t -> int array -> t

                                                            TODO

                                                            val repeat : t -> int array -> t

                                                            TODO

                                                            val pack_elt : A.elt -> t

                                                            convert from elt type to t type.

                                                            val unpack_elt : t -> A.elt

                                                            convert from t type to elt type.

                                                            val pack_flt : float -> t

                                                            convert from float type to t type.

                                                            val _f : float -> t

                                                            A shortcut function for F A.(float_to_elt x).

                                                            val unpack_flt : t -> float

                                                            convert from t type to float type.

                                                            val pack_arr : A.arr -> t

                                                            convert from arr type to t type.

                                                            val unpack_arr : t -> A.arr

                                                            convert from t type to arr type.

                                                            val deep_info : t -> string

                                                            TODO

                                                            val type_info : t -> string

                                                            TODO

                                                            val error_binop : string -> t -> t -> 'a

                                                            TODO

                                                            val error_uniop : string -> t -> 'a

                                                            TODO

                                                            val make_forward : t -> t -> int -> t

                                                            TODO

                                                            val make_reverse : t -> int -> t

                                                            TODO

                                                            val reverse_prop : t -> t -> unit

                                                            TODO

                                                            val diff : (t -> t) -> t -> t

                                                            diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                            Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                            val diff' : (t -> t) -> t -> t * t

                                                            similar to diff, but return (f x, diff f x).

                                                            val grad : (t -> t) -> t -> t

                                                            gradient of f : (vector -> scalar) at x, reverse ad.

                                                            val grad' : (t -> t) -> t -> t * t

                                                            similar to grad, but return (f x, grad f x).

                                                            val jacobian : (t -> t) -> t -> t

                                                            jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                            val jacobian' : (t -> t) -> t -> t * t

                                                            similar to jacobian, but return (f x, jacobian f x)

                                                            val jacobianv : (t -> t) -> t -> t -> t

                                                            jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                            val jacobianv' : (t -> t) -> t -> t -> t * t

                                                            similar to jacobianv', but return (f x, jacobianv f x v)

                                                            val jacobianTv : (t -> t) -> t -> t -> t

                                                            transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                            similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                            val hessian : (t -> t) -> t -> t

                                                            hessian of f : (scalar -> scalar) at x.

                                                            val hessian' : (t -> t) -> t -> t * t

                                                            simiarl to hessian, but return (f x, hessian f x)

                                                            val hessianv : (t -> t) -> t -> t -> t

                                                            hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                            val hessianv' : (t -> t) -> t -> t -> t * t

                                                            similar to hessianv, but return (f x, hessianv f x v).

                                                            val laplacian : (t -> t) -> t -> t

                                                            laplacian of f : (scalar -> scalar) at x.

                                                            val laplacian' : (t -> t) -> t -> t * t

                                                            similar to laplacian, but return (f x, laplacian f x).

                                                            val gradhessian : (t -> t) -> t -> t * t

                                                            return (grad f x, hessian f x), f : (scalar -> scalar)

                                                            val gradhessian' : (t -> t) -> t -> t * t * t

                                                            return (f x, grad f x, hessian f x)

                                                            val gradhessianv : (t -> t) -> t -> t -> t * t

                                                            return (grad f x v, hessian f x v)

                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                            return (f x, grad f x v, hessian f x v)

                                                            include Owl_algodiff_ops_sig.Sig +Algodiff (owl.Owl_regression_generic_sig.Sig.Optimise.Algodiff)

                                                            Module Optimise.Algodiff

                                                            include Owl_algodiff_core_sig.Sig
                                                            Type definition
                                                            include Owl_algodiff_types_sig.Sig with type elt := A.elt and type arr := A.arr
                                                            type t =
                                                            1. | F of A.elt
                                                            2. | Arr of A.arr
                                                            3. | DF of t * t * int
                                                            4. | DR of t * t Stdlib.ref * op * int Stdlib.ref * int * int Stdlib.ref
                                                            and adjoint = t -> t Stdlib.ref -> (t * t) list -> (t * t) list
                                                            and register = t list -> t list
                                                            and label = string * t list
                                                            and op = adjoint * register * label
                                                            Core functions
                                                            val tag : unit -> int

                                                            TODO

                                                            val primal : t -> t

                                                            TODO

                                                            val primal' : t -> t

                                                            TODO

                                                            val zero : t -> t

                                                            TODO

                                                            val reset_zero : t -> t

                                                            TODO

                                                            val tangent : t -> t

                                                            TODO

                                                            val adjref : t -> t Stdlib.ref

                                                            TODO

                                                            val adjval : t -> t

                                                            TODO

                                                            val shape : t -> int array

                                                            TODO

                                                            val is_float : t -> bool

                                                            TODO

                                                            val is_arr : t -> bool

                                                            TODO

                                                            val row_num : t -> int

                                                            number of rows

                                                            val col_num : t -> int

                                                            number of columns

                                                            val numel : t -> int

                                                            number of elements

                                                            val clip_by_value : amin:A.elt -> amax:A.elt -> t -> t

                                                            other functions, without tracking gradient

                                                            val clip_by_l2norm : A.elt -> t -> t

                                                            other functions, without tracking gradient

                                                            val copy_primal' : t -> t

                                                            TODO

                                                            val tile : t -> int array -> t

                                                            TODO

                                                            val repeat : t -> int array -> t

                                                            TODO

                                                            val pack_elt : A.elt -> t

                                                            convert from elt type to t type.

                                                            val unpack_elt : t -> A.elt

                                                            convert from t type to elt type.

                                                            val pack_flt : float -> t

                                                            convert from float type to t type.

                                                            val _f : float -> t

                                                            A shortcut function for F A.(float_to_elt x).

                                                            val unpack_flt : t -> float

                                                            convert from t type to float type.

                                                            val pack_arr : A.arr -> t

                                                            convert from arr type to t type.

                                                            val unpack_arr : t -> A.arr

                                                            convert from t type to arr type.

                                                            val deep_info : t -> string

                                                            TODO

                                                            val type_info : t -> string

                                                            TODO

                                                            val error_binop : string -> t -> t -> 'a

                                                            TODO

                                                            val error_uniop : string -> t -> 'a

                                                            TODO

                                                            val make_forward : t -> t -> int -> t

                                                            TODO

                                                            val make_reverse : t -> int -> t

                                                            TODO

                                                            val reverse_prop : t -> t -> unit

                                                            TODO

                                                            val diff : (t -> t) -> t -> t

                                                            diff f x returns the exat derivative of a function f : scalar -> scalar at point x. Simply calling diff f will return its derivative function g of the same type, i.e. g : scalar -> scalar.

                                                            Keep calling this function will give you higher-order derivatives of f, i.e. f |> diff |> diff |> diff |> ...

                                                            val diff' : (t -> t) -> t -> t * t

                                                            similar to diff, but return (f x, diff f x).

                                                            val grad : (t -> t) -> t -> t

                                                            gradient of f : (vector -> scalar) at x, reverse ad.

                                                            val grad' : (t -> t) -> t -> t * t

                                                            similar to grad, but return (f x, grad f x).

                                                            val jacobian : (t -> t) -> t -> t

                                                            jacobian of f : (vector -> vector) at x, both x and y are row vectors.

                                                            val jacobian' : (t -> t) -> t -> t * t

                                                            similar to jacobian, but return (f x, jacobian f x)

                                                            val jacobianv : (t -> t) -> t -> t -> t

                                                            jacobian vector product of f : (vector -> vector) at x along v, forward ad. Namely, it calcultes (jacobian x) v

                                                            val jacobianv' : (t -> t) -> t -> t -> t * t

                                                            similar to jacobianv', but return (f x, jacobianv f x v)

                                                            val jacobianTv : (t -> t) -> t -> t -> t

                                                            transposed jacobian vector product of f : (vector -> vector) at x along v, backward ad. Namely, it calculates transpose ((jacobianv f x v)).

                                                            val jacobianTv' : (t -> t) -> t -> t -> t * t

                                                            similar to jacobianTv, but return (f x, transpose (jacobianv f x v))

                                                            val hessian : (t -> t) -> t -> t

                                                            hessian of f : (scalar -> scalar) at x.

                                                            val hessian' : (t -> t) -> t -> t * t

                                                            simiarl to hessian, but return (f x, hessian f x)

                                                            val hessianv : (t -> t) -> t -> t -> t

                                                            hessian vector product of f : (scalar -> scalar) at x along v. Namely, it calculates (hessian x) v.

                                                            val hessianv' : (t -> t) -> t -> t -> t * t

                                                            similar to hessianv, but return (f x, hessianv f x v).

                                                            val laplacian : (t -> t) -> t -> t

                                                            laplacian of f : (scalar -> scalar) at x.

                                                            val laplacian' : (t -> t) -> t -> t * t

                                                            similar to laplacian, but return (f x, laplacian f x).

                                                            val gradhessian : (t -> t) -> t -> t * t

                                                            return (grad f x, hessian f x), f : (scalar -> scalar)

                                                            val gradhessian' : (t -> t) -> t -> t * t * t

                                                            return (f x, grad f x, hessian f x)

                                                            val gradhessianv : (t -> t) -> t -> t -> t * t

                                                            return (grad f x v, hessian f x v)

                                                            val gradhessianv' : (t -> t) -> t -> t -> t * t * t

                                                            return (f x, grad f x v, hessian f x v)

                                                            include Owl_algodiff_ops_sig.Sig with type t := t and type elt := A.elt and type arr := A.arr diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Batch/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Batch/index.html index 7f41300e4..184c9f3b2 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Batch/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Batch/index.html @@ -1,2 +1,2 @@ -Batch (owl.Owl_regression_generic_sig.Sig.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            Batch module

                                                            type typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic

                                                            Types of batches.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val batches : typ -> Algodiff.t -> int

                                                            Return the total number of batches given a batch typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Batch (owl.Owl_regression_generic_sig.Sig.Optimise.Batch)

                                                            Module Optimise.Batch

                                                            Batch module

                                                            type typ =
                                                            1. | Full
                                                            2. | Mini of int
                                                            3. | Sample of int
                                                            4. | Stochastic

                                                            Types of batches.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val batches : typ -> Algodiff.t -> int

                                                            Return the total number of batches given a batch typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Checkpoint/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Checkpoint/index.html index 0bfc99f26..75507acfd 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Checkpoint/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Checkpoint/index.html @@ -1,2 +1,2 @@ -Checkpoint (owl.Owl_regression_generic_sig.Sig.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            Checkpoint module

                                                            type state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }

                                                            Type definition of checkpoint

                                                            type typ =
                                                            1. | Batch of int
                                                            2. | Epoch of float
                                                            3. | Custom of state -> unit
                                                            4. | None

                                                            Batch type.

                                                            val init_state : int -> float -> state

                                                            init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                            val default_checkpoint_fun : (string -> 'a) -> 'a

                                                            This function is used for saving intermediate files during optimisation.

                                                            val print_state_info : state -> unit

                                                            Print out the detail information of current state.

                                                            val print_summary : state -> unit

                                                            Print out the summary of current state.

                                                            val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Checkpoint (owl.Owl_regression_generic_sig.Sig.Optimise.Checkpoint)

                                                            Module Optimise.Checkpoint

                                                            Checkpoint module

                                                            type state = {
                                                            1. mutable current_batch : int;
                                                            2. mutable batches_per_epoch : int;
                                                            3. mutable epochs : float;
                                                            4. mutable batches : int;
                                                            5. mutable loss : Algodiff.t array;
                                                            6. mutable start_at : float;
                                                            7. mutable stop : bool;
                                                            8. mutable gs : Algodiff.t array array;
                                                            9. mutable ps : Algodiff.t array array;
                                                            10. mutable us : Algodiff.t array array;
                                                            11. mutable ch : Algodiff.t array array array;
                                                            }

                                                            Type definition of checkpoint

                                                            type typ =
                                                            1. | Batch of int
                                                            2. | Epoch of float
                                                            3. | Custom of state -> unit
                                                            4. | None

                                                            Batch type.

                                                            val init_state : int -> float -> state

                                                            init_state batches_per_epoch epochs initialises a state by specifying the number of batches per epoch and the number of epochs in total.

                                                            val default_checkpoint_fun : (string -> 'a) -> 'a

                                                            This function is used for saving intermediate files during optimisation.

                                                            val print_state_info : state -> unit

                                                            Print out the detail information of current state.

                                                            val print_summary : state -> unit

                                                            Print out the summary of current state.

                                                            val run : typ -> (string -> unit) -> int -> Algodiff.t -> state -> unit

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Clipping/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Clipping/index.html index f776f780f..ddd1cad45 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Clipping/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Clipping/index.html @@ -1,2 +1,2 @@ -Clipping (owl.Owl_regression_generic_sig.Sig.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            Clipping module

                                                            type typ =
                                                            1. | L2norm of float
                                                            2. | Value of float * float
                                                            3. | None

                                                            Types of clipping functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Clipping (owl.Owl_regression_generic_sig.Sig.Optimise.Clipping)

                                                            Module Optimise.Clipping

                                                            Clipping module

                                                            type typ =
                                                            1. | L2norm of float
                                                            2. | Value of float * float
                                                            3. | None

                                                            Types of clipping functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Gradient/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Gradient/index.html index 472b68632..e5d28466d 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Gradient/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Gradient/index.html @@ -1,5 +1,5 @@ -Gradient (owl.Owl_regression_generic_sig.Sig.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            Gradient module

                                                            type typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton

                                                            Types of gradient function.

                                                            val run : +Gradient (owl.Owl_regression_generic_sig.Sig.Optimise.Gradient)

                                                            Module Optimise.Gradient

                                                            Gradient module

                                                            type typ =
                                                            1. | GD
                                                            2. | CG
                                                            3. | CD
                                                            4. | NonlinearCG
                                                            5. | DaiYuanCG
                                                            6. | NewtonCG
                                                            7. | Newton

                                                            Types of gradient function.

                                                            val run : typ -> (Algodiff.t -> Algodiff.t) -> Algodiff.t -> diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Learning_Rate/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Learning_Rate/index.html index 0f8af4182..6a8f95661 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Learning_Rate/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Learning_Rate/index.html @@ -1,2 +1,2 @@ -Learning_Rate (owl.Owl_regression_generic_sig.Sig.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            Strategies for learning rate update

                                                            type typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array

                                                            Representation of learning rate update strategies. Possible values include:

                                                            • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                            Update the cache of gradients.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Learning_Rate (owl.Owl_regression_generic_sig.Sig.Optimise.Learning_Rate)

                                                            Module Optimise.Learning_Rate

                                                            Strategies for learning rate update

                                                            type typ =
                                                            1. | Adagrad of float
                                                            2. | Const of float
                                                            3. | Decay of float * float
                                                            4. | Exp_decay of float * float
                                                            5. | RMSprop of float * float
                                                            6. | Adam of float * float * float
                                                            7. | Schedule of float array

                                                            Representation of learning rate update strategies. Possible values include:

                                                            • Adam (alpha, beta1, beta2), see ref for parameter meaning
                                                            val run : typ -> int -> Algodiff.t -> Algodiff.t array -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val update_ch : typ -> Algodiff.t -> Algodiff.t array -> Algodiff.t array

                                                            Update the cache of gradients.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Loss/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Loss/index.html index 0dca1a254..49ce9d0fb 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Loss/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Loss/index.html @@ -1,2 +1,2 @@ -Loss (owl.Owl_regression_generic_sig.Sig.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            Loss module

                                                            type typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Types of loss functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Loss (owl.Owl_regression_generic_sig.Sig.Optimise.Loss)

                                                            Module Optimise.Loss

                                                            Loss module

                                                            type typ =
                                                            1. | Hinge
                                                            2. | L1norm
                                                            3. | L2norm
                                                            4. | Quadratic
                                                            5. | Cross_entropy
                                                            6. | Custom of Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Types of loss functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Momentum/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Momentum/index.html index 435ff0cf6..09e989af2 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Momentum/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Momentum/index.html @@ -1,2 +1,2 @@ -Momentum (owl.Owl_regression_generic_sig.Sig.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            Momentum module

                                                            type typ =
                                                            1. | Standard of float
                                                            2. | Nesterov of float
                                                            3. | None

                                                            Types of momentum functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Momentum (owl.Owl_regression_generic_sig.Sig.Optimise.Momentum)

                                                            Module Optimise.Momentum

                                                            Momentum module

                                                            type typ =
                                                            1. | Standard of float
                                                            2. | Nesterov of float
                                                            3. | None

                                                            Types of momentum functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Params/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Params/index.html index 40f3c3403..253acbf5c 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Params/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Params/index.html @@ -1,5 +1,5 @@ -Params (owl.Owl_regression_generic_sig.Sig.Optimise.Params)

                                                            Module Optimise.Params

                                                            Params module

                                                            type typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }

                                                            Type definition of parameter.

                                                            val default : unit -> typ

                                                            Create module typ with default values.

                                                            val config : +Params (owl.Owl_regression_generic_sig.Sig.Optimise.Params)

                                                            Module Optimise.Params

                                                            Params module

                                                            type typ = {
                                                            1. mutable epochs : float;
                                                            2. mutable batch : Batch.typ;
                                                            3. mutable gradient : Gradient.typ;
                                                            4. mutable loss : Loss.typ;
                                                            5. mutable learning_rate : Learning_Rate.typ;
                                                            6. mutable regularisation : Regularisation.typ;
                                                            7. mutable momentum : Momentum.typ;
                                                            8. mutable clipping : Clipping.typ;
                                                            9. mutable stopping : Stopping.typ;
                                                            10. mutable checkpoint : Checkpoint.typ;
                                                            11. mutable verbosity : bool;
                                                            }

                                                            Type definition of parameter.

                                                            val default : unit -> typ

                                                            Create module typ with default values.

                                                            val config : ?batch:Batch.typ -> ?gradient:Gradient.typ -> ?loss:Loss.typ -> diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Regularisation/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Regularisation/index.html index 93121701b..bab185c6b 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Regularisation/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Regularisation/index.html @@ -1,2 +1,2 @@ -Regularisation (owl.Owl_regression_generic_sig.Sig.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            Regularisation module

                                                            type typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None

                                                            Types of regularisation functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Regularisation (owl.Owl_regression_generic_sig.Sig.Optimise.Regularisation)

                                                            Module Optimise.Regularisation

                                                            Regularisation module

                                                            type typ =
                                                            1. | L1norm of float
                                                            2. | L2norm of float
                                                            3. | Elastic_net of float * float
                                                            4. | None

                                                            Types of regularisation functions.

                                                            val run : typ -> Algodiff.t -> Algodiff.t

                                                            Execute the computations defined in module typ.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Stopping/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Stopping/index.html index 28f059574..fcffca81f 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Stopping/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Stopping/index.html @@ -1,2 +1,2 @@ -Stopping (owl.Owl_regression_generic_sig.Sig.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            Stopping module

                                                            type typ =
                                                            1. | Const of float
                                                            2. | Early of int * int
                                                            3. | None

                                                            Types of stopping functions.

                                                            val run : typ -> float -> bool

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            +Stopping (owl.Owl_regression_generic_sig.Sig.Optimise.Stopping)

                                                            Module Optimise.Stopping

                                                            Stopping module

                                                            type typ =
                                                            1. | Const of float
                                                            2. | Early of int * int
                                                            3. | None

                                                            Types of stopping functions.

                                                            val run : typ -> float -> bool

                                                            Execute the computations defined in module typ.

                                                            val default : typ -> typ

                                                            Create module typ with default values.

                                                            val to_string : typ -> string

                                                            Convert the module typ to its string representation.

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Utils/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Utils/index.html index df0e1f763..29c79ec76 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Utils/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/Utils/index.html @@ -1,5 +1,5 @@ -Utils (owl.Owl_regression_generic_sig.Sig.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            Utils module

                                                            val sample_num : Algodiff.t -> int

                                                            Return the total number of samples in passed in ndarray.

                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                            draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                            val get_chunk : +Utils (owl.Owl_regression_generic_sig.Sig.Optimise.Utils)

                                                            Module Optimise.Utils

                                                            Utils module

                                                            val sample_num : Algodiff.t -> int

                                                            Return the total number of samples in passed in ndarray.

                                                            val draw_samples : Algodiff.t -> Algodiff.t -> int -> Algodiff.t * Algodiff.t

                                                            draw_samples x y draws samples from both x (observations) and y (labels). The samples will be drew along axis 0, so x and y must agree along axis 0.

                                                            val get_chunk : Algodiff.t -> Algodiff.t -> int -> diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/index.html index 503f450fa..2986e09ed 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/Optimise/index.html @@ -1,5 +1,5 @@ -Optimise (owl.Owl_regression_generic_sig.Sig.Optimise)

                                                            Module Sig.Optimise

                                                            module Utils : sig ... end

                                                            Utils module

                                                            module Learning_Rate : sig ... end

                                                            Strategies for learning rate update

                                                            module Batch : sig ... end

                                                            Batch module

                                                            module Loss : sig ... end

                                                            Loss module

                                                            module Gradient : sig ... end

                                                            Gradient module

                                                            module Momentum : sig ... end

                                                            Momentum module

                                                            module Regularisation : sig ... end

                                                            Regularisation module

                                                            module Clipping : sig ... end

                                                            Clipping module

                                                            module Stopping : sig ... end

                                                            Stopping module

                                                            module Checkpoint : sig ... end

                                                            Checkpoint module

                                                            module Params : sig ... end

                                                            Params module

                                                            Core functions
                                                            val minimise_weight : +Optimise (owl.Owl_regression_generic_sig.Sig.Optimise)

                                                            Module Sig.Optimise

                                                            module Utils : sig ... end

                                                            Utils module

                                                            module Learning_Rate : sig ... end

                                                            Strategies for learning rate update

                                                            module Batch : sig ... end

                                                            Batch module

                                                            module Loss : sig ... end

                                                            Loss module

                                                            module Gradient : sig ... end

                                                            Gradient module

                                                            module Momentum : sig ... end

                                                            Momentum module

                                                            module Regularisation : sig ... end

                                                            Regularisation module

                                                            module Clipping : sig ... end

                                                            Clipping module

                                                            module Stopping : sig ... end

                                                            Stopping module

                                                            module Checkpoint : sig ... end

                                                            Checkpoint module

                                                            module Params : sig ... end

                                                            Params module

                                                            Core functions
                                                            val minimise_weight : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> @@ -28,4 +28,4 @@ (string -> unit) -> Algodiff.t -> Algodiff.t -> - Checkpoint.state

                                                            TODO

                                                            + Checkpoint.state

                                                            This function is minimize the weights in a compiled neural network of graph structure.

                                                            diff --git a/docs/owl/Owl_regression_generic_sig/module-type-Sig/index.html b/docs/owl/Owl_regression_generic_sig/module-type-Sig/index.html index 1dab85994..00ce689be 100644 --- a/docs/owl/Owl_regression_generic_sig/module-type-Sig/index.html +++ b/docs/owl/Owl_regression_generic_sig/module-type-Sig/index.html @@ -1,8 +1,8 @@ -Sig (owl.Owl_regression_generic_sig.Sig)

                                                            Module type Owl_regression_generic_sig.Sig

                                                            Type definition

                                                            Type of ndarray values.

                                                            Type of scalar values.

                                                            Regression models
                                                            val ols : ?i:bool -> arr -> arr -> arr array

                                                            TODO

                                                            val ridge : ?i:bool -> ?alpha:float -> arr -> arr -> arr array

                                                            TODO

                                                            val lasso : ?i:bool -> ?alpha:float -> arr -> arr -> arr array

                                                            TODO

                                                            val elastic_net : +Sig (owl.Owl_regression_generic_sig.Sig)

                                                            Module type Owl_regression_generic_sig.Sig

                                                            Type definition

                                                            Type of ndarray values.

                                                            Type of scalar values.

                                                            val ols : ?i:bool -> arr -> arr -> arr array

                                                            Regression models

                                                            ols ?i x y performs Ordinary Least Squares (OLS) regression on the data x and y.

                                                            • i is an optional parameter indicating whether to include an intercept in the model. The default is true.
                                                            • x is the matrix of input features.
                                                            • y is the vector of output values. Returns an array of coefficients for the linear model.
                                                            val ridge : ?i:bool -> ?alpha:float -> arr -> arr -> arr array

                                                            ridge ?i ?alpha x y performs Ridge regression on the data x and y.

                                                            • i is an optional parameter indicating whether to include an intercept in the model. The default is true.
                                                            • alpha is the regularization strength parameter. The default value is 1.0.
                                                            • x is the matrix of input features.
                                                            • y is the vector of output values. Returns an array of coefficients for the linear model.
                                                            val lasso : ?i:bool -> ?alpha:float -> arr -> arr -> arr array

                                                            lasso ?i ?alpha x y performs Lasso regression on the data x and y.

                                                            • i is an optional parameter indicating whether to include an intercept in the model. The default is true.
                                                            • alpha is the regularization strength parameter. The default value is 1.0.
                                                            • x is the matrix of input features.
                                                            • y is the vector of output values. Returns an array of coefficients for the linear model.
                                                            val elastic_net : ?i:bool -> ?alpha:float -> ?l1_ratio:float -> arr -> arr -> - arr array

                                                            TODO

                                                            val svm : ?i:bool -> ?a:float -> arr -> arr -> arr array

                                                            TODO

                                                            val logistic : ?i:bool -> arr -> arr -> arr array

                                                            TODO

                                                            val exponential : ?i:bool -> arr -> arr -> elt * elt * elt

                                                            TODO

                                                            val poly : arr -> arr -> int -> arr

                                                            TODO

                                                            + arr array

                                                            elastic_net ?i ?alpha ?l1_ratio x y performs Elastic Net regression on the data x and y.

                                                            • i is an optional parameter indicating whether to include an intercept in the model. The default is true.
                                                            • alpha is the regularization strength parameter. The default value is 1.0.
                                                            • l1_ratio is the ratio between L1 and L2 regularization terms. The default value is 0.5.
                                                            • x is the matrix of input features.
                                                            • y is the vector of output values. Returns an array of coefficients for the linear model.
                                                            val svm : ?i:bool -> ?a:float -> arr -> arr -> arr array

                                                            svm ?i ?a x y performs Support Vector Machine (SVM) classification on the data x and y.

                                                            • i is an optional parameter indicating whether to include an intercept in the model. The default is true.
                                                            • a is an optional parameter for the regularization parameter (commonly denoted as C). The default value is 1.0.
                                                            • x is the matrix of input features.
                                                            • y is the vector of output values. Returns an array of support vectors and coefficients.
                                                            val logistic : ?i:bool -> arr -> arr -> arr array

                                                            logistic ?i x y performs logistic regression on the data x and y.

                                                            • i is an optional parameter indicating whether to include an intercept in the model. The default is true.
                                                            • x is the matrix of input features.
                                                            • y is the vector of output values. Returns an array of coefficients for the logistic model.
                                                            val exponential : ?i:bool -> arr -> arr -> elt * elt * elt

                                                            exponential ?i x y fits an exponential model to the data x and y.

                                                            • i is an optional parameter indicating whether to include an intercept in the model. The default is true.
                                                            • x is the vector of input values.
                                                            • y is the vector of output values. Returns a tuple containing the coefficients of the exponential model.
                                                            val poly : arr -> arr -> int -> arr

                                                            poly x y degree fits a polynomial model of the specified degree to the data x and y.

                                                            • x is the vector of input values.
                                                            • y is the vector of output values.
                                                            • degree specifies the degree of the polynomial. Returns the coefficients of the polynomial model.
                                                            diff --git a/docs/owl/Owl_signal/index.html b/docs/owl/Owl_signal/index.html index c95b0b370..77bd12e28 100644 --- a/docs/owl/Owl_signal/index.html +++ b/docs/owl/Owl_signal/index.html @@ -1,5 +1,5 @@ -Owl_signal (owl.Owl_signal)

                                                            Module Owl_signal

                                                            Signal: Fundamental Signal Processing functions.

                                                            Basic window functions

                                                            val blackman : int -> Owl_dense.Ndarray.D.arr

                                                            Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. blackman m returns a blackman window.

                                                            val hamming : int -> Owl_dense.Ndarray.D.arr

                                                            Hamming window is a taper formed by using a raised cosine with non-zero endpoints, optimized to minimize the nearest side lobe. hamming m returns a hamming window.

                                                            val hann : int -> Owl_dense.Ndarray.D.arr

                                                            Hann window is a taper formed by using a raised cosine or sine-squared with ends that touch zero. hann m returns a hann window.

                                                            Filter response function

                                                            val freqz : +Owl_signal (owl.Owl_signal)

                                                            Module Owl_signal

                                                            Signal: Fundamental Signal Processing functions.

                                                            Basic window functions

                                                            val blackman : int -> Owl_dense.Ndarray.D.arr

                                                            Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. blackman m returns a blackman window.

                                                            val hamming : int -> Owl_dense.Ndarray.D.arr

                                                            Hamming window is a taper formed by using a raised cosine with non-zero endpoints, optimized to minimize the nearest side lobe. hamming m returns a hamming window.

                                                            val hann : int -> Owl_dense.Ndarray.D.arr

                                                            Hann window is a taper formed by using a raised cosine or sine-squared with ends that touch zero. hann m returns a hann window.

                                                            Filter response function

                                                            val freqz : ?n:int -> ?whole:bool -> float array -> diff --git a/docs/owl/Owl_slicing/index.html b/docs/owl/Owl_slicing/index.html index 6265992a0..621444118 100644 --- a/docs/owl/Owl_slicing/index.html +++ b/docs/owl/Owl_slicing/index.html @@ -1,5 +1,5 @@ -Owl_slicing (owl.Owl_slicing)

                                                            Module Owl_slicing

                                                            include module type of struct include Owl_base_slicing end
                                                            val sdlist_to_sdarray : Owl_types.index list -> Owl_types.index_ array
                                                            val sdarray_to_sdarray : Owl_types.index array -> Owl_types.index_ array
                                                            val is_basic_slicing : Owl_types.index_ array -> bool
                                                            val check_slice_definition : +Owl_slicing (owl.Owl_slicing)

                                                            Module Owl_slicing

                                                            include module type of struct include Owl_base_slicing end
                                                            val sdlist_to_sdarray : Owl_types.index list -> Owl_types.index_ array
                                                            val sdarray_to_sdarray : Owl_types.index array -> Owl_types.index_ array
                                                            val is_basic_slicing : Owl_types.index_ array -> bool
                                                            val check_slice_definition : Owl_types.index_ array -> int array -> Owl_types.index_ array
                                                            val calc_continuous_blksz : Owl_types.index_ array -> int array -> int * int
                                                            val calc_slice_shape : Owl_types.index_ array -> int array
                                                            val __foreach_continuous_blk : diff --git a/docs/owl/Owl_slicing_basic/index.html b/docs/owl/Owl_slicing_basic/index.html index 3545f5d66..de1d712fa 100644 --- a/docs/owl/Owl_slicing_basic/index.html +++ b/docs/owl/Owl_slicing_basic/index.html @@ -1,5 +1,5 @@ -Owl_slicing_basic (owl.Owl_slicing_basic)

                                                            Module Owl_slicing_basic

                                                            val owl_float32_ndarray_get_slice : +Owl_slicing_basic (owl.Owl_slicing_basic)

                                                            Module Owl_slicing_basic

                                                            val owl_float32_ndarray_get_slice : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> (int64, 'c) Owl_core_types.owl_arr -> diff --git a/docs/owl/Owl_slicing_fancy/index.html b/docs/owl/Owl_slicing_fancy/index.html index 44b904c51..ab5c5b1f2 100644 --- a/docs/owl/Owl_slicing_fancy/index.html +++ b/docs/owl/Owl_slicing_fancy/index.html @@ -1,5 +1,5 @@ -Owl_slicing_fancy (owl.Owl_slicing_fancy)

                                                            Module Owl_slicing_fancy

                                                            val owl_float32_ndarray_get_fancy : +Owl_slicing_fancy (owl.Owl_slicing_fancy)

                                                            Module Owl_slicing_fancy

                                                            val owl_float32_ndarray_get_fancy : ('a, 'b) Owl_core_types.owl_arr -> ('a, 'b) Owl_core_types.owl_arr -> (int64, 'c) Owl_core_types.owl_arr -> diff --git a/docs/owl/Owl_stats/index.html b/docs/owl/Owl_stats/index.html index b687526a7..099cb9a63 100644 --- a/docs/owl/Owl_stats/index.html +++ b/docs/owl/Owl_stats/index.html @@ -1,5 +1,5 @@ -Owl_stats (owl.Owl_stats)

                                                            Module Owl_stats_dist

                                                            val std_uniform_rvs : unit -> float
                                                            val uniform_int_rvs : a:int -> b:int -> int
                                                            val uniform_rvs : a:float -> b:float -> float
                                                            val uniform_pdf : float -> a:float -> b:float -> float
                                                            val uniform_logpdf : float -> a:float -> b:float -> float
                                                            val uniform_cdf : float -> a:float -> b:float -> float
                                                            val uniform_logcdf : float -> a:float -> b:float -> float
                                                            val uniform_ppf : float -> a:float -> b:float -> float
                                                            val uniform_sf : float -> a:float -> b:float -> float
                                                            val uniform_logsf : float -> a:float -> b:float -> float
                                                            val uniform_isf : float -> a:float -> b:float -> float
                                                            val exponential_rvs : lambda:float -> float
                                                            val exponential_pdf : float -> lambda:float -> float
                                                            val exponential_logpdf : float -> lambda:float -> float
                                                            val exponential_cdf : float -> lambda:float -> float
                                                            val exponential_logcdf : float -> lambda:float -> float
                                                            val exponential_ppf : float -> lambda:float -> float
                                                            val exponential_sf : float -> lambda:float -> float
                                                            val exponential_logsf : float -> lambda:float -> float
                                                            val exponential_isf : float -> lambda:float -> float
                                                            val exponpow_rvs : a:float -> b:float -> float
                                                            val exponpow_pdf : float -> a:float -> b:float -> float
                                                            val exponpow_logpdf : float -> a:float -> b:float -> float
                                                            val exponpow_cdf : float -> a:float -> b:float -> float
                                                            val exponpow_logcdf : float -> a:float -> b:float -> float
                                                            val exponpow_sf : float -> a:float -> b:float -> float
                                                            val exponpow_logsf : float -> a:float -> b:float -> float
                                                            val gaussian_rvs : mu:float -> sigma:float -> float
                                                            val gaussian_pdf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_logpdf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_cdf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_logcdf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_ppf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_sf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_logsf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_isf : float -> mu:float -> sigma:float -> float
                                                            val gamma_rvs : shape:float -> scale:float -> float
                                                            val gamma_pdf : float -> shape:float -> scale:float -> float
                                                            val gamma_logpdf : float -> shape:float -> scale:float -> float
                                                            val gamma_cdf : float -> shape:float -> scale:float -> float
                                                            val gamma_logcdf : float -> shape:float -> scale:float -> float
                                                            val gamma_ppf : float -> shape:float -> scale:float -> float
                                                            val gamma_sf : float -> shape:float -> scale:float -> float
                                                            val gamma_logsf : float -> shape:float -> scale:float -> float
                                                            val gamma_isf : float -> shape:float -> scale:float -> float
                                                            val beta_rvs : a:float -> b:float -> float
                                                            val beta_pdf : float -> a:float -> b:float -> float
                                                            val beta_logpdf : float -> a:float -> b:float -> float
                                                            val beta_cdf : float -> a:float -> b:float -> float
                                                            val beta_logcdf : float -> a:float -> b:float -> float
                                                            val beta_ppf : float -> a:float -> b:float -> float
                                                            val beta_sf : float -> a:float -> b:float -> float
                                                            val beta_logsf : float -> a:float -> b:float -> float
                                                            val beta_isf : float -> a:float -> b:float -> float
                                                            val chi2_rvs : df:float -> float
                                                            val chi2_pdf : float -> df:float -> float
                                                            val chi2_logpdf : float -> df:float -> float
                                                            val chi2_cdf : float -> df:float -> float
                                                            val chi2_logcdf : float -> df:float -> float
                                                            val chi2_ppf : float -> df:float -> float
                                                            val chi2_sf : float -> df:float -> float
                                                            val chi2_logsf : float -> df:float -> float
                                                            val chi2_isf : float -> df:float -> float
                                                            val f_rvs : dfnum:float -> dfden:float -> float
                                                            val f_pdf : float -> dfnum:float -> dfden:float -> float
                                                            val f_logpdf : float -> dfnum:float -> dfden:float -> float
                                                            val f_cdf : float -> dfnum:float -> dfden:float -> float
                                                            val f_logcdf : float -> dfnum:float -> dfden:float -> float
                                                            val f_ppf : float -> dfnum:float -> dfden:float -> float
                                                            val f_sf : float -> dfnum:float -> dfden:float -> float
                                                            val f_logsf : float -> dfnum:float -> dfden:float -> float
                                                            val f_isf : float -> dfnum:float -> dfden:float -> float
                                                            val cauchy_rvs : loc:float -> scale:float -> float
                                                            val cauchy_pdf : float -> loc:float -> scale:float -> float
                                                            val cauchy_logpdf : float -> loc:float -> scale:float -> float
                                                            val cauchy_cdf : float -> loc:float -> scale:float -> float
                                                            val cauchy_logcdf : float -> loc:float -> scale:float -> float
                                                            val cauchy_ppf : float -> loc:float -> scale:float -> float
                                                            val cauchy_sf : float -> loc:float -> scale:float -> float
                                                            val cauchy_logsf : float -> loc:float -> scale:float -> float
                                                            val cauchy_isf : float -> loc:float -> scale:float -> float
                                                            val t_rvs : df:float -> loc:float -> scale:float -> float
                                                            val t_pdf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_logpdf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_cdf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_logcdf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_ppf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_sf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_logsf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_isf : float -> df:float -> loc:float -> scale:float -> float
                                                            val vonmises_rvs : mu:float -> kappa:float -> float
                                                            val vonmises_pdf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_logpdf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_cdf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_logcdf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_sf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_logsf : float -> mu:float -> kappa:float -> float
                                                            val lomax_rvs : shape:float -> scale:float -> float
                                                            val lomax_pdf : float -> shape:float -> scale:float -> float
                                                            val lomax_logpdf : float -> shape:float -> scale:float -> float
                                                            val lomax_cdf : float -> shape:float -> scale:float -> float
                                                            val lomax_logcdf : float -> shape:float -> scale:float -> float
                                                            val lomax_ppf : float -> shape:float -> scale:float -> float
                                                            val lomax_sf : float -> shape:float -> scale:float -> float
                                                            val lomax_logsf : float -> shape:float -> scale:float -> float
                                                            val lomax_isf : float -> shape:float -> scale:float -> float
                                                            val weibull_rvs : shape:float -> scale:float -> float
                                                            val weibull_pdf : float -> shape:float -> scale:float -> float
                                                            val weibull_logpdf : float -> shape:float -> scale:float -> float
                                                            val weibull_cdf : float -> shape:float -> scale:float -> float
                                                            val weibull_logcdf : float -> shape:float -> scale:float -> float
                                                            val weibull_ppf : float -> shape:float -> scale:float -> float
                                                            val weibull_sf : float -> shape:float -> scale:float -> float
                                                            val weibull_logsf : float -> shape:float -> scale:float -> float
                                                            val weibull_isf : float -> shape:float -> scale:float -> float
                                                            val laplace_rvs : loc:float -> scale:float -> float
                                                            val laplace_pdf : float -> loc:float -> scale:float -> float
                                                            val laplace_logpdf : float -> loc:float -> scale:float -> float
                                                            val laplace_cdf : float -> loc:float -> scale:float -> float
                                                            val laplace_logcdf : float -> loc:float -> scale:float -> float
                                                            val laplace_ppf : float -> loc:float -> scale:float -> float
                                                            val laplace_sf : float -> loc:float -> scale:float -> float
                                                            val laplace_logsf : float -> loc:float -> scale:float -> float
                                                            val laplace_isf : float -> loc:float -> scale:float -> float
                                                            val gumbel1_rvs : a:float -> b:float -> float
                                                            val gumbel1_pdf : float -> a:float -> b:float -> float
                                                            val gumbel1_logpdf : float -> a:float -> b:float -> float
                                                            val gumbel1_cdf : float -> a:float -> b:float -> float
                                                            val gumbel1_logcdf : float -> a:float -> b:float -> float
                                                            val gumbel1_ppf : float -> a:float -> b:float -> float
                                                            val gumbel1_sf : float -> a:float -> b:float -> float
                                                            val gumbel1_logsf : float -> a:float -> b:float -> float
                                                            val gumbel1_isf : float -> a:float -> b:float -> float
                                                            val gumbel2_rvs : a:float -> b:float -> float
                                                            val gumbel2_pdf : float -> a:float -> b:float -> float
                                                            val gumbel2_logpdf : float -> a:float -> b:float -> float
                                                            val gumbel2_cdf : float -> a:float -> b:float -> float
                                                            val gumbel2_logcdf : float -> a:float -> b:float -> float
                                                            val gumbel2_ppf : float -> a:float -> b:float -> float
                                                            val gumbel2_sf : float -> a:float -> b:float -> float
                                                            val gumbel2_logsf : float -> a:float -> b:float -> float
                                                            val gumbel2_isf : float -> a:float -> b:float -> float
                                                            val logistic_rvs : loc:float -> scale:float -> float
                                                            val logistic_pdf : float -> loc:float -> scale:float -> float
                                                            val logistic_logpdf : float -> loc:float -> scale:float -> float
                                                            val logistic_cdf : float -> loc:float -> scale:float -> float
                                                            val logistic_logcdf : float -> loc:float -> scale:float -> float
                                                            val logistic_ppf : float -> loc:float -> scale:float -> float
                                                            val logistic_sf : float -> loc:float -> scale:float -> float
                                                            val logistic_logsf : float -> loc:float -> scale:float -> float
                                                            val logistic_isf : float -> loc:float -> scale:float -> float
                                                            val lognormal_rvs : mu:float -> sigma:float -> float
                                                            val lognormal_pdf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_logpdf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_cdf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_logcdf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_ppf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_sf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_logsf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_isf : float -> mu:float -> sigma:float -> float
                                                            val rayleigh_rvs : sigma:float -> float
                                                            val rayleigh_pdf : float -> sigma:float -> float
                                                            val rayleigh_logpdf : float -> sigma:float -> float
                                                            val rayleigh_cdf : float -> sigma:float -> float
                                                            val rayleigh_logcdf : float -> sigma:float -> float
                                                            val rayleigh_ppf : float -> sigma:float -> float
                                                            val rayleigh_sf : float -> sigma:float -> float
                                                            val rayleigh_logsf : float -> sigma:float -> float
                                                            val rayleigh_isf : float -> sigma:float -> float
                                                            val hypergeometric_rvs : good:int -> bad:int -> sample:int -> int
                                                            val hypergeometric_pdf : int -> good:int -> bad:int -> sample:int -> float
                                                            val hypergeometric_logpdf : int -> good:int -> bad:int -> sample:int -> float
                                                            val binomial_rvs : p:float -> n:int -> int
                                                            val binomial_pdf : int -> p:float -> n:int -> float
                                                            val binomial_logpdf : int -> p:float -> n:int -> float
                                                            val binomial_cdf : int -> p:float -> n:int -> float
                                                            val binomial_logcdf : int -> p:float -> n:int -> float
                                                            val binomial_sf : int -> p:float -> n:int -> float
                                                            val binomial_logsf : int -> p:float -> n:int -> float
                                                            val _multinomial_rvs : +Owl_stats_dist (owl.Owl_stats_dist)

                                                            Module Owl_stats_dist

                                                            val std_uniform_rvs : unit -> float
                                                            val uniform_int_rvs : a:int -> b:int -> int
                                                            val uniform_rvs : a:float -> b:float -> float
                                                            val uniform_pdf : float -> a:float -> b:float -> float
                                                            val uniform_logpdf : float -> a:float -> b:float -> float
                                                            val uniform_cdf : float -> a:float -> b:float -> float
                                                            val uniform_logcdf : float -> a:float -> b:float -> float
                                                            val uniform_ppf : float -> a:float -> b:float -> float
                                                            val uniform_sf : float -> a:float -> b:float -> float
                                                            val uniform_logsf : float -> a:float -> b:float -> float
                                                            val uniform_isf : float -> a:float -> b:float -> float
                                                            val exponential_rvs : lambda:float -> float
                                                            val exponential_pdf : float -> lambda:float -> float
                                                            val exponential_logpdf : float -> lambda:float -> float
                                                            val exponential_cdf : float -> lambda:float -> float
                                                            val exponential_logcdf : float -> lambda:float -> float
                                                            val exponential_ppf : float -> lambda:float -> float
                                                            val exponential_sf : float -> lambda:float -> float
                                                            val exponential_logsf : float -> lambda:float -> float
                                                            val exponential_isf : float -> lambda:float -> float
                                                            val exponpow_rvs : a:float -> b:float -> float
                                                            val exponpow_pdf : float -> a:float -> b:float -> float
                                                            val exponpow_logpdf : float -> a:float -> b:float -> float
                                                            val exponpow_cdf : float -> a:float -> b:float -> float
                                                            val exponpow_logcdf : float -> a:float -> b:float -> float
                                                            val exponpow_sf : float -> a:float -> b:float -> float
                                                            val exponpow_logsf : float -> a:float -> b:float -> float
                                                            val gaussian_rvs : mu:float -> sigma:float -> float
                                                            val gaussian_pdf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_logpdf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_cdf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_logcdf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_ppf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_sf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_logsf : float -> mu:float -> sigma:float -> float
                                                            val gaussian_isf : float -> mu:float -> sigma:float -> float
                                                            val gamma_rvs : shape:float -> scale:float -> float
                                                            val gamma_pdf : float -> shape:float -> scale:float -> float
                                                            val gamma_logpdf : float -> shape:float -> scale:float -> float
                                                            val gamma_cdf : float -> shape:float -> scale:float -> float
                                                            val gamma_logcdf : float -> shape:float -> scale:float -> float
                                                            val gamma_ppf : float -> shape:float -> scale:float -> float
                                                            val gamma_sf : float -> shape:float -> scale:float -> float
                                                            val gamma_logsf : float -> shape:float -> scale:float -> float
                                                            val gamma_isf : float -> shape:float -> scale:float -> float
                                                            val beta_rvs : a:float -> b:float -> float
                                                            val beta_pdf : float -> a:float -> b:float -> float
                                                            val beta_logpdf : float -> a:float -> b:float -> float
                                                            val beta_cdf : float -> a:float -> b:float -> float
                                                            val beta_logcdf : float -> a:float -> b:float -> float
                                                            val beta_ppf : float -> a:float -> b:float -> float
                                                            val beta_sf : float -> a:float -> b:float -> float
                                                            val beta_logsf : float -> a:float -> b:float -> float
                                                            val beta_isf : float -> a:float -> b:float -> float
                                                            val chi2_rvs : df:float -> float
                                                            val chi2_pdf : float -> df:float -> float
                                                            val chi2_logpdf : float -> df:float -> float
                                                            val chi2_cdf : float -> df:float -> float
                                                            val chi2_logcdf : float -> df:float -> float
                                                            val chi2_ppf : float -> df:float -> float
                                                            val chi2_sf : float -> df:float -> float
                                                            val chi2_logsf : float -> df:float -> float
                                                            val chi2_isf : float -> df:float -> float
                                                            val f_rvs : dfnum:float -> dfden:float -> float
                                                            val f_pdf : float -> dfnum:float -> dfden:float -> float
                                                            val f_logpdf : float -> dfnum:float -> dfden:float -> float
                                                            val f_cdf : float -> dfnum:float -> dfden:float -> float
                                                            val f_logcdf : float -> dfnum:float -> dfden:float -> float
                                                            val f_ppf : float -> dfnum:float -> dfden:float -> float
                                                            val f_sf : float -> dfnum:float -> dfden:float -> float
                                                            val f_logsf : float -> dfnum:float -> dfden:float -> float
                                                            val f_isf : float -> dfnum:float -> dfden:float -> float
                                                            val cauchy_rvs : loc:float -> scale:float -> float
                                                            val cauchy_pdf : float -> loc:float -> scale:float -> float
                                                            val cauchy_logpdf : float -> loc:float -> scale:float -> float
                                                            val cauchy_cdf : float -> loc:float -> scale:float -> float
                                                            val cauchy_logcdf : float -> loc:float -> scale:float -> float
                                                            val cauchy_ppf : float -> loc:float -> scale:float -> float
                                                            val cauchy_sf : float -> loc:float -> scale:float -> float
                                                            val cauchy_logsf : float -> loc:float -> scale:float -> float
                                                            val cauchy_isf : float -> loc:float -> scale:float -> float
                                                            val t_rvs : df:float -> loc:float -> scale:float -> float
                                                            val t_pdf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_logpdf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_cdf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_logcdf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_ppf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_sf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_logsf : float -> df:float -> loc:float -> scale:float -> float
                                                            val t_isf : float -> df:float -> loc:float -> scale:float -> float
                                                            val vonmises_rvs : mu:float -> kappa:float -> float
                                                            val vonmises_pdf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_logpdf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_cdf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_logcdf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_sf : float -> mu:float -> kappa:float -> float
                                                            val vonmises_logsf : float -> mu:float -> kappa:float -> float
                                                            val lomax_rvs : shape:float -> scale:float -> float
                                                            val lomax_pdf : float -> shape:float -> scale:float -> float
                                                            val lomax_logpdf : float -> shape:float -> scale:float -> float
                                                            val lomax_cdf : float -> shape:float -> scale:float -> float
                                                            val lomax_logcdf : float -> shape:float -> scale:float -> float
                                                            val lomax_ppf : float -> shape:float -> scale:float -> float
                                                            val lomax_sf : float -> shape:float -> scale:float -> float
                                                            val lomax_logsf : float -> shape:float -> scale:float -> float
                                                            val lomax_isf : float -> shape:float -> scale:float -> float
                                                            val weibull_rvs : shape:float -> scale:float -> float
                                                            val weibull_pdf : float -> shape:float -> scale:float -> float
                                                            val weibull_logpdf : float -> shape:float -> scale:float -> float
                                                            val weibull_cdf : float -> shape:float -> scale:float -> float
                                                            val weibull_logcdf : float -> shape:float -> scale:float -> float
                                                            val weibull_ppf : float -> shape:float -> scale:float -> float
                                                            val weibull_sf : float -> shape:float -> scale:float -> float
                                                            val weibull_logsf : float -> shape:float -> scale:float -> float
                                                            val weibull_isf : float -> shape:float -> scale:float -> float
                                                            val laplace_rvs : loc:float -> scale:float -> float
                                                            val laplace_pdf : float -> loc:float -> scale:float -> float
                                                            val laplace_logpdf : float -> loc:float -> scale:float -> float
                                                            val laplace_cdf : float -> loc:float -> scale:float -> float
                                                            val laplace_logcdf : float -> loc:float -> scale:float -> float
                                                            val laplace_ppf : float -> loc:float -> scale:float -> float
                                                            val laplace_sf : float -> loc:float -> scale:float -> float
                                                            val laplace_logsf : float -> loc:float -> scale:float -> float
                                                            val laplace_isf : float -> loc:float -> scale:float -> float
                                                            val gumbel1_rvs : a:float -> b:float -> float
                                                            val gumbel1_pdf : float -> a:float -> b:float -> float
                                                            val gumbel1_logpdf : float -> a:float -> b:float -> float
                                                            val gumbel1_cdf : float -> a:float -> b:float -> float
                                                            val gumbel1_logcdf : float -> a:float -> b:float -> float
                                                            val gumbel1_ppf : float -> a:float -> b:float -> float
                                                            val gumbel1_sf : float -> a:float -> b:float -> float
                                                            val gumbel1_logsf : float -> a:float -> b:float -> float
                                                            val gumbel1_isf : float -> a:float -> b:float -> float
                                                            val gumbel2_rvs : a:float -> b:float -> float
                                                            val gumbel2_pdf : float -> a:float -> b:float -> float
                                                            val gumbel2_logpdf : float -> a:float -> b:float -> float
                                                            val gumbel2_cdf : float -> a:float -> b:float -> float
                                                            val gumbel2_logcdf : float -> a:float -> b:float -> float
                                                            val gumbel2_ppf : float -> a:float -> b:float -> float
                                                            val gumbel2_sf : float -> a:float -> b:float -> float
                                                            val gumbel2_logsf : float -> a:float -> b:float -> float
                                                            val gumbel2_isf : float -> a:float -> b:float -> float
                                                            val logistic_rvs : loc:float -> scale:float -> float
                                                            val logistic_pdf : float -> loc:float -> scale:float -> float
                                                            val logistic_logpdf : float -> loc:float -> scale:float -> float
                                                            val logistic_cdf : float -> loc:float -> scale:float -> float
                                                            val logistic_logcdf : float -> loc:float -> scale:float -> float
                                                            val logistic_ppf : float -> loc:float -> scale:float -> float
                                                            val logistic_sf : float -> loc:float -> scale:float -> float
                                                            val logistic_logsf : float -> loc:float -> scale:float -> float
                                                            val logistic_isf : float -> loc:float -> scale:float -> float
                                                            val lognormal_rvs : mu:float -> sigma:float -> float
                                                            val lognormal_pdf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_logpdf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_cdf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_logcdf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_ppf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_sf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_logsf : float -> mu:float -> sigma:float -> float
                                                            val lognormal_isf : float -> mu:float -> sigma:float -> float
                                                            val rayleigh_rvs : sigma:float -> float
                                                            val rayleigh_pdf : float -> sigma:float -> float
                                                            val rayleigh_logpdf : float -> sigma:float -> float
                                                            val rayleigh_cdf : float -> sigma:float -> float
                                                            val rayleigh_logcdf : float -> sigma:float -> float
                                                            val rayleigh_ppf : float -> sigma:float -> float
                                                            val rayleigh_sf : float -> sigma:float -> float
                                                            val rayleigh_logsf : float -> sigma:float -> float
                                                            val rayleigh_isf : float -> sigma:float -> float
                                                            val hypergeometric_rvs : good:int -> bad:int -> sample:int -> int
                                                            val hypergeometric_pdf : int -> good:int -> bad:int -> sample:int -> float
                                                            val hypergeometric_logpdf : int -> good:int -> bad:int -> sample:int -> float
                                                            val binomial_rvs : p:float -> n:int -> int
                                                            val binomial_pdf : int -> p:float -> n:int -> float
                                                            val binomial_logpdf : int -> p:float -> n:int -> float
                                                            val binomial_cdf : int -> p:float -> n:int -> float
                                                            val binomial_logcdf : int -> p:float -> n:int -> float
                                                            val binomial_sf : int -> p:float -> n:int -> float
                                                            val binomial_logsf : int -> p:float -> n:int -> float
                                                            val _multinomial_rvs : k:int -> n:int -> p:(float, Stdlib.Bigarray.float64_elt) Owl_types.owl_arr -> diff --git a/docs/owl/Owl_stats_extend/index.html b/docs/owl/Owl_stats_extend/index.html index 5138eb128..8c233c686 100644 --- a/docs/owl/Owl_stats_extend/index.html +++ b/docs/owl/Owl_stats_extend/index.html @@ -1,2 +1,2 @@ -Owl_stats_extend (owl.Owl_stats_extend)

                                                            Module Owl_stats_extend

                                                            val shuffle : 'a array -> unit
                                                            val choose : src:'a array -> dst:'a array -> unit
                                                            val sample : src:'a array -> dst:'a array -> unit
                                                            val sum : float array -> float
                                                            val mean : float array -> float
                                                            val var : float array -> float -> float
                                                            val std : float array -> float -> float
                                                            val absdev : float array -> float -> float
                                                            val skew : float array -> float -> float -> float
                                                            val kurtosis : float array -> float -> float -> float
                                                            val cov : float array -> float array -> float -> float -> float
                                                            val corrcoef : float array -> float array -> float
                                                            val quantile : float array -> float -> float
                                                            +Owl_stats_extend (owl.Owl_stats_extend)

                                                            Module Owl_stats_extend

                                                            val shuffle : 'a array -> unit
                                                            val choose : src:'a array -> dst:'a array -> unit
                                                            val sample : src:'a array -> dst:'a array -> unit
                                                            val sum : float array -> float
                                                            val mean : float array -> float
                                                            val var : float array -> float -> float
                                                            val std : float array -> float -> float
                                                            val absdev : float array -> float -> float
                                                            val skew : float array -> float -> float -> float
                                                            val kurtosis : float array -> float -> float -> float
                                                            val cov : float array -> float array -> float -> float -> float
                                                            val corrcoef : float array -> float array -> float
                                                            val quantile : float array -> float -> float
                                                            diff --git a/docs/owl/Owl_stats_prng/index.html b/docs/owl/Owl_stats_prng/index.html index 2de1598f5..e23160c0d 100644 --- a/docs/owl/Owl_stats_prng/index.html +++ b/docs/owl/Owl_stats_prng/index.html @@ -1,2 +1,2 @@ -Owl_stats_prng (owl.Owl_stats_prng)

                                                            Module Owl_stats_prng

                                                            type state
                                                            val sfmt_seed : int -> unit
                                                            val rand_int : unit -> int
                                                            val ziggurat_init : unit -> unit
                                                            val rand_exp : unit -> float
                                                            val rand_gaussian : unit -> float
                                                            val self_init : unit -> unit
                                                            val init : int -> unit
                                                            +Owl_stats_prng (owl.Owl_stats_prng)

                                                            Module Owl_stats_prng

                                                            type state
                                                            val sfmt_seed : int -> unit
                                                            val rand_int : unit -> int
                                                            val ziggurat_init : unit -> unit
                                                            val rand_exp : unit -> float
                                                            val rand_gaussian : unit -> float
                                                            val self_init : unit -> unit
                                                            val init : int -> unit
                                                            diff --git a/docs/owl/Owl_stats_sampler/index.html b/docs/owl/Owl_stats_sampler/index.html index 0a282c52c..2bf3091aa 100644 --- a/docs/owl/Owl_stats_sampler/index.html +++ b/docs/owl/Owl_stats_sampler/index.html @@ -1,5 +1,5 @@ -Owl_stats_sampler (owl.Owl_stats_sampler)

                                                            Module Owl_stats_sampler

                                                            type 'a t = {
                                                            1. samples : 'a array;
                                                            2. accept : float;
                                                            }
                                                            val rejection : +Owl_stats_sampler (owl.Owl_stats_sampler)

                                                            Module Owl_stats_sampler

                                                            type 'a t = {
                                                            1. samples : 'a array;
                                                            2. accept : float;
                                                            }
                                                            val rejection : m:float -> proprvs:(unit -> float) -> proppdf:(float -> float) -> diff --git a/docs/owl/index.html b/docs/owl/index.html index 07a57f12b..7c5913293 100644 --- a/docs/owl/index.html +++ b/docs/owl/index.html @@ -1,2 +1,2 @@ -index (owl.index)

                                                            owl index

                                                            Library owl

                                                            This library exposes the following toplevel modules:

                                                            +index (owl.index)

                                                            owl index

                                                            Library owl

                                                            This library exposes the following toplevel modules:

                                                            diff --git a/src/owl/dense/owl_dense_ndarray_generic.mli b/src/owl/dense/owl_dense_ndarray_generic.mli index 48c2d4ead..17fd8f6c1 100644 --- a/src/owl/dense/owl_dense_ndarray_generic.mli +++ b/src/owl/dense/owl_dense_ndarray_generic.mli @@ -2533,8 +2533,8 @@ val create_ : out:('a, 'b) t -> 'a -> unit val uniform_ : ?a:'a -> ?b:'a -> out:('a, 'b) t -> unit (** - [uniform_ ?a ?b ~out] fills the matrix [out] in-place with random values drawn from a uniform distribution over the interval [a, b). - If [a] and [b] are not provided, the default interval is [0, 1). + [uniform_ ?a ?b ~out] fills the matrix [out] in-place with random values drawn from a uniform distribution over the interval \[a, b\). + If [a] and [b] are not provided, the default interval is \[0, 1\). *) val gaussian_ : ?mu:'a -> ?sigma:'a -> out:('a, 'b) t -> unit diff --git a/src/owl/maths/owl_maths.mli b/src/owl/maths/owl_maths.mli index 929691736..2640ea96c 100644 --- a/src/owl/maths/owl_maths.mli +++ b/src/owl/maths/owl_maths.mli @@ -310,7 +310,7 @@ val gamma : float -> float {math \Gamma(z) = \int_0^\infty x^{z-1} e^{-x} dx = (z - 1)!.} The gamma function is often referred to as the generalized factorial since -{m z\ gamma(z) = \gamma(z+1)} and {m gamma(n+1) = n!} +{m z\gamma(z) = \gamma(z+1)} and {m \gamma(n+1) = n!} for natural number n. *)